cahier0413 - queen's u

Copyright © 2004. HEC Montréal. Tous droits réservés pour tous pays. Toute traduction et toute reproduction sous quelque forme que ce soit est interdite. HEC Montréal, 3000, chemin de la Côte-Sainte-Catherine, Montréal, Québec, H3T 2A7 Canada. Les textes publiés dans la série des Cahiers du GReSI n'engagent que la responsabilité de leurs auteurs.

ISSN 0832-7203

A Transaction Cost Analysis of IT Outsourcing

Par : Benoit Aubert Jean-François Houde Michel Patry Suzanne Rivard

Cahier du GReSI no 04-13 Mai 2004

Copyright © 2004. HEC Montréal 2

A Transaction Cost Analysis of IT Outsourcing

Benoit Aubert HEC Montréal

3000, chemin de la Côte-Ste-Catherine Montréal, Québec, Canada H3T 2A7

[email protected] and Fellow, CIRANO

Jean-François Houde, Ph.D. Student

Queen’s University 99 University Ave.

Kingston, Ontario, Canada K7L 3N6 [email protected]

Michel Patry HEC Montréal



Suzanne Rivard HEC Montréal



Prière de faire parvenir toute correspondance à : [email protected]

A Transaction Cost Analysis of IT Outsourcing Benoit Aubert, Jean-François Houde, Michel Patry, and Suzanne Rivard


Résumé Cette recherche, basée sur la théorie des coûts de transaction, étudie le phénomène de l'impartition des services informatiques. Elle considère, outre la décision d'internalisation ou d'impartition, les modes de gouvernance mixtes. Les résultats suggèrent que le choix du mode d'organisation est soumis à des préoccupations liées à la minimisation des coûts. Les déterminants principaux sont l'importance des habiletés (à la fois techniques et organisationnelles). Ceci appuie les hypothèses formulées par Grossman et Hart (1986). De plus, la mesurabilité de la performance du fournisseur est un autre déterminant de la décision d'impartition. Finalement, les résultats semblent confirmer que la spécificité des actifs associés au capital humain représente un frein à l'impartition. Les autres formes de spécificité ont toutefois l'effet contraire. Abstract This research uses transaction cost theory to investigate IT outsourcing, allowing for intermediate modes of outsourcing. Results suggest that the governance mode chosen by firms to govern their IT activities is guided by cost-minimization considerations. The major transactional determinant of the governance mode choice is the need for technical and/or organization skills. This constitutes a strong endorsement for the Grossman and Hart (1986) proposition. In addition, the measurability of the supplier’s performance is found to be a main driver explaining outsourcing decisions. However, it appears that it is the measurability of supplier performance that is important, rather than the facility to observe and evaluate the process of this work. Finally, results tend to confirm that a high level of asset specificity embedded in human capital will dampen the probability of outsourcing IT activities, while other forms of asset specificity seem to have the opposite effect. Mots-clés Outsourcing, Transaction Cost Theory, Information System Operations



1. INTRODUCTION

The outsourcing of Information Technology (IT) services remains high on the strategic agenda of organizations. In fact, all signs point towards continuous growth of the outsourcing market as organizations are expanding the range of IT services they outsource (Cosgrove, 2002), even when the economy is struggling (Scholl et al., 2002). Outsourcing is a typical example of a make-or-buy decision and can be defined as the “significant contribution by external vendors of the physical and/or human resources associated with the entire or specific components of the IT infrastructure in the user organization.”1 In the past years, several firms have outsourced their Information Systems (IS) services and management concerns regarding the outsourcing of information systems services have become more complex.

Transaction cost theory (TCT) has been widely used to examine the make-or-buy decision: the choice of a particular governance mode to manage a given set of transactions. Outsourcing research, using TCT as a theoretical basis has mainly focused on different subsets of the theory – leading to incomplete explanations. As well, studies that have included asset specificity – a central construct of TCT – have led to conflicting results. This may be due to the use of subsets of the theoretical framework. In this study, we propose a model that encompasses all the main constructs of TCT. We first review the theory and evidence on TCT and IT outsourcing. Then, we develop an empirical framework which encompasses more characteristics than most previous studies. Our framework, which we empirically test, also allows for intermediate modes of outsourcing.

1.1 THE THEORY AND EVIDENCE ON TCT AND IT OUTSOURCING

An important strand of research has examined the make-or-buy decision using TCT. Its foundation was laid by Coase (1937) who positioned the market and the firm as alternative mechanisms that could be chosen to conduct a transaction. TCT has been refined and used extensively in the last twenty years. According to transaction costs theory, the decision to use the market or the firm to regulate a transaction depends primarily on four variables (Milgrom and Roberts, 1992; Williamson, 1985):

(1) The specificity of the assets required to produce the good;

(2) The uncertainty and the measurement problems surrounding the transaction;

(3) The allocation of control and the source of investment; and

(4) The frequency of the transaction.

These considerations constitute deviations from the ideal situation of a perfectly competitive spot market transaction where all goods are available, all information is public knowledge, and all transactions are performed instantly. These anomalies, when too serious, substantially reduce the benefits of using the market and, beyond a certain threshold, lead a party to internalize the transaction (Milgrom and Roberts, 1992; Williamson, 1985, 1989). Another major deviation occurs when measurement problems impede the efficacy of market transactions (Alchian and Demsetz, 1972; Barzel, 1982).

Empirically, the wide majority of the studies using TCT looked at the make or buy decision, whether the transaction was conducted in-house or on a market. Most generally, the dependent variable has been defined as dichotomous, reflecting a yes/no decision toward outsourcing (for example: Monterverdee and Teece, 1982; Pisano, 1990; Masten, 1984; Anderson, 1985; Ang and Slaughter, 1996; Poppo and Zenger, 1998). Sometimes, the portion of the firm’s budget used for internal provision has been used, as in Loh and Venkatraman (1992), and Nam et al. (1996).

1 Loh, L., N. Venkatraman, “Diffusion of Information Technology Outsourcing: Influence Sources and the Kodak Effect,” ISR, 3(4) (December 1992), 334-378; p.336.



Asset Specificity Asset specificity has received a lot of attention from researchers (Williamson, 1985). Whenever the value of an asset is linked to a specific transaction (meaning that its next best use is less valuable than its primary use), a lock-in situation ensues where a party could extract a quasi-rent2 from the other party involved in the transaction. This means that the contracting parties will try to establish contractual provisions to lower the risk associated with specific investments. Developing and enforcing these safeguarding provisions is costly. When the problems associated with lock-in are too severe, the firm will prefer to internalize the transaction in order to avoid these transaction costs. On the other hand, transactions supported by assets with very low level of specificity should be expected to be governed by the market. Finding a suitable partner or changing supplier for such transaction is easy in these circumstances. The role of asset specificity has been supported by studies in various industrial sectors, among which: auto parts (Monteverdee and Teece, 1982; Walker and Weber, 1987), aerospace (Masten, 1984), and aluminum (Hennart, 1988).

In the information systems field, the role of asset specificity has often been verified. For instance, using a qualitative analysis, Alpar and Saharia (1995) concluded that some highly specific functions, involving tacit knowledge, were less likely to be outsourced. Using case studies, Aubert et al. (1996) provided support for this hypothesis. These observations were later validated empirically by Wholey and Padman (1998), and by Poppo and Zenger (1998). Yet, Nam et al. (1996) obtained conflicting results. Using three different measures of specificity, they obtained significant results solely for the implicit knowledge associated with the transaction.

Uncertainty and Measurement A basic requirement for a transaction to be conducted on the market is that the parties be able to evaluate the elements exchanged, both in quantity and in quality. The more uncertainty surrounds a transaction, the more difficult it becomes to devise, negotiate, and enforce a commercial contract. The costs of contracting under severe uncertainty can wipe out the benefits associated with the outsourcing contract. Similarly, if the deliverables cannot be defined ex ante, the transaction cannot be completed. Uncertainty will also prevent the establishment of a long-term contract (a traditional remedy for asset specificity). When faced with severe uncertainty, many organizations will prefer to internalize the transaction and to rely on their employees. Employment is a very flexible form of contract which enables the firm to adjust its actions as the future unfolds.

Uncertainty takes many forms. Complexity and measurement problems are two of them. Very complex activities will be difficult to describe adequately in a contract (Williamson, 1985). They will be more difficult to understand for the contracting parties and agreeing upon a description will be arduous. Inversely, standardization limits the effect of complexity and lack of information. Standardization provides detailed guidelines for governing the activities (Van de Ven and Ferry, 1980). Such guidelines facilitate the establishment of an outsourcing contract since the contracting parties can rely on these recognized principles.

Another key element which falls under the general category of uncertainty is the measurement problem. For an activity to be governed by the market (outsourced), parties have to be able to measure the activity. Many activities are inherently difficult to measure. Activities which are the product of joint efforts (team work, collaboration) are especially prone to measurement problems (Alchian and Demsetz, 1972). When the activities or products in the exchange are not measurable, the price is not a sufficient statistic to warrant the exchange. The traditional mechanism used by parties to alleviate this problem is to agree on rules, which is the characteristic of the internal

2 The quasi-rent is the difference between the value of an asset in its best use and the value it takes in its second best use. (Pisano 1990, p. 159).



organization (Barney and Ouchi, 1986). In situations where measurement problems are too strong, the internal organization will be preferred to outsourcing.

In the case of IT outsourcing, Nam et al (1996) measured environmental uncertainty and found that it was negatively associated with outsourcing, confirming the transaction cost hypothesis. Wholey and Padman (1998) showed that there was a significant difference between the outsourcing patterns of software development activities (which are relatively more uncertain) and of IT operational activities (relatively more certain). Poppo and Zenger (1998) measured the volatility of technological innovations and obtained a weak and positive impact of the decision to outsource. This contradicts the transaction hypothesis but supports the traditional idea that risk of obsolescence is better managed by external suppliers.

These authors also evaluated measurement problems and found that activities that were more difficult to measure tended to be managed in-house, which is consistent with the transaction cost hypothesis.

Skills and Investments Another branch of the literature is rooted in Grossman and Hart (1986) who studied the effects on productivity of different allocations of control over decisions. In any contract, control rights have to be allocated. Grossman and Hart (1996) demonstrate that, in a context of incomplete contracting, residual decision rights should be allocated to the party making the most important investment in order to minimize inefficiencies. These investments take various forms. In service industries, they most often take the form of skills and know-how. This rule for the allocation of control reinforces the concentration on core competency and motivates the firms to conduct in-house activities with which they have experience, while outsourcing the ones with which they have little familiarity. Teng at al. (1995), Wholey and Padman (1998), and Slaughter and Ang (1996) all found that companies tended to outsource IT activities that were not within their core competency.

While many articles used the TCT framework, most used it in part, focusing on one or a few aspects at a time. This is true not only in information technology. The majority of the studies published in economics journals concentrate on one aspect of TCT at a time.

2. THEORETICAL FRAMEWORK

From the previous discussion, we conjecture that asset specificity and uncertainty related problems significantly impact on a firm’s decision to outsource its IT activities. This leads us to formulate the following four testable hypotheses:

H1: Asset specificity will be negatively associated with the level of outsourcing. H2: Complexity will be negatively associated with the level of outsourcing. H3: Standardization will be positively associated with the level of outsourcing. H4: Measurement problems will be negatively associated with the level of outsourcing.

Coupling the literature on resource-based strategy and Grossman and Hart’s results (1986) leads to the formulation of two additional hypotheses:

H5: The relative importance of technical skills associated with an activity will be positively associated with the level of outsourcing.

H6: The relative importance of organizational skills associated with an activity will be negatively associated with the level of outsourcing. TCT naturally does not exhaust all rationales for outsourcing. As economies of scale become

more important, smaller firms will have a greater incentive to outsource, all else being equal. Therefore, we must control for firm size. Also, firms will try to avoid over investing in capacity. When demand fluctuates, the organizations might have to make such over investments to meet the peak-demand. Instead of over investing, organizations can rely on outsourcing to provide adjustment.



Kouvelis and Milner (2002) found that demand uncertainty increased the reliance on outsourcing. This leads to the following additional hypothesis:

H7: Demand uncertainty (fluctuation in volume) will be positively associated with the level of outsourcing.

Control variables Finally, we introduce a number of control variables to ensure that the observed effect could not be artifacts of the sample. First, as mentioned, the size of the firm was controlled for. Organization type was also assessed. Private corporations, which are not traded publicly, might have less pressure to be very efficient than publicly traded ones. Government-owned corporations, which may also be less driven by efficiency considerations and more by political considerations, were also controlled for in the analysis. Finally, the knowledge intensity of the industry, the industry itself, and the type of IS activity (management, operation or maintenance) were controlled for.

It is important to note that frequency was not measured formally. There are two reasons for this. First, the IS activities surveyed are IS operations. These activities are performed on a continuous basis. Moreover, according to Williamson (1985), the idea behind frequency is that organizations will try to avoid establishing a governance structure for activities only performed occasionally. This is very analogous to the volume uncertainty hypothesis (H7). Organizations will internalize (and consequently invest in capacity) activities that are recurrent. Testing H7 will therefore provide an indirect evaluation of the frequency hypothesis postulated by transaction cost theory.

2.1 Econometric Specification

Following the theoretical framework laid out before, we assume that firms choose the governance mode which maximizes the net benefits of outsourcing (π*). We modeled this net benefit function as a linear relation of transactional characteristics (XTC) and organizational characteristics (XOC):

εδβαπ

π

+++=

=

OCTC

OCTC

XX

XXf

''

),(*

*

(1)

The observed governance mode for each IT activity is therefore the realization of this unobservable maximization process. In this representation, α represents the predisposition of the management towards or against outsourcing (respectively if α<0 or α>0). Following Masten (1984), if the constant is positive, this reflects the “administrative burden” cost of internalization, while a negative constant reflects a “market burden” cost of outsourcing3. In addition, since the governance mode variable is ordered (see methodology section), estimation of Eq.(1) is done using an ordered probit model. This model maximizes the probability that the firm i will choose mode k to organize activity j in the following manner4:

( ) ( )( ) [ ]( ) [ ]( )

( ) ( ) [ ]( )OCTC

OCTCOCTC

OCTC

XXobXXXXob

XXob

''1Pr2ModeProb''''0Pr1)Mode(Prob

''10Pr0)Mode(Prob

*

*

*

δβαµµπδβαδβαµµπ

δβαπ

++−Φ−=>==

++−Φ−++−Φ=≤<==

++Φ−=≤== (2)

In Eq. (1) and (2), µ represents the ordered threshold of the model to be estimated with the vectors β and δ, Φ is the normal distribution function, and ε is the vector of residuals supposed to be

3 This analysis is the reverse of the one done by Masten (1984), since he was comparing the net cost of internalisation, while we compare the net benefit of outsourcing. 4 Hereafter, for the sake of simplicity in the notation, the ij indices will be skipped



independently and identically distributed ( )1,0(~ Nε )5. Eq. (3) defines the log-likelihood function (LLF) which is maximized using the econometric software LIMDEP.

( ){ }

[ ]( ) [ ]( ){ }

[ ]( ){ }

2).or 1, 0, (k chose mode governance theindicatesk ;not if 0 , if 1 where

''1ln

''''ln

''1lnln

196

1

16

12

196

1

16

11

196

1

16

10

===

++−Φ−⋅

+++−Φ−++−Φ⋅

+++Φ−⋅=

∑∑

∑∑

∑∑

= =

= =

= =

kModew

XXw

XXXXw

XXwL

k

i iOCTC

i jOCTCOCTC

i jOCTC

δβαµ

δβαδβαµ

δβα

(3)

However, the final specification is more complex since it adds the stratification of the threshold (µ), and includes a heteroskedastic function in the model.

The stratification of the model is done by estimating a different threshold (µg) for each group of activities (Management activities, operations, and maintenance activities)6. This is necessary, since it is anticipated that there are some group specific effects. For example, maintenance activities are traditionally more outsourced than the two other groups of activities (Aubert et al. forthcoming). Referring to the theoretical framework, for a typical firm the longest experience acquired with the outsourcing of maintenance activities is represented by a systematically highest net benefits of outsourcing relative to the two other groups of activities (µMtnc > µOIS or µMIS).

We also posit that the variance of the residuals is an exponential function of the industrial dummies Ds (Greene, 1997). The alternative form, where the variance is set to 1, was also estimated and a Log-likelihood ratio test was conducted to determine the best specification (Table 3, Ho2): the results support the first specification and only those are reported here. This hypothesis regarding the form of the heteroskedasticity function supports the fact that different industries have different outsourcing practices and different IT needs, which implies that the outsourcing decisions are heterogeneous in regard to the industrial sectors.

With these two modifications, our estimated model becomes:

( )[ ]( )s

OCTCg

gg

OCTCg

DN

XXD

XXDf

'exp,0~ where,

'''

),(16

1

*

,*

γε

εδβαπ

π

+++=

=

∑=

(4)

The log-likelihood function can be written as:

5 The hypothesis of variance equal to 1 can be violated in the case of a diagnostic of heteroskedasticity ( ]²)'[exp(,0(~ zN γε ). This hypothesis will be tested in the next sections. 6 There are three groups of activities: management of IS (MIS), operations (OIS), and maintenance (Mtnc). To stratify the model, the log-likelihood function will be multiplied by three dummies (Dg) representing each activity group, and by adding 16 activity dummies to the equation 1.



2).or 1, 0, (k chosen

mode governance theindicatesk ;not if 0 , if 1 where

'''1ln

'''

'''

ln

'''1lnln

196

1

16

1

3

1

9

2

'16

12

196

1

16

1

3

1 9

2

'16

1

9

2

'16

1

1

196

1

16

1

9

2

'16

10

=

==

++−Φ−⋅⋅

+

++−Φ

−

++−Φ

⋅⋅

+

++Φ−⋅=

∑∑∑ ∑∑

∑∑∑∑∑

∑∑

∑∑ ∑∑

= = = ==

= = =

==

==

= = ==

kModew

eXXDwD

eXXD

eXXD

wD

eXXDwL

ijk

i j g s

DOCTC

jjjgg

i j g

s

DOCTC

jjj

s

DOCTC

jjjg

g

i j s

DOCTC

jjj

s

s

s

s

γ

γ

γ

γ

δβαµ

δβα

δβαµ

δβα

(5)

Finally, in this class of model the marginal effects of the regressors are not equal to the estimated coefficients, but rather to the marginal impact of each variable on the probability of choosing each mode (Greene, 1997). In our case, these marginal effects, evaluated at the sample means, are given by Eq. (6).

ββµφ

ββµφβφ

ββφ

γ

γγ

γ

)'(]0[Pr

)'()'(]1[Pr

']0[Pr

'

''

'

s

ss

s

Dg

DgD

D

ex

xModeob

ex

ex

xModeob

ex

xModeob

−=∂

=∂

−−−=

∂=∂

−=

∂=∂

(6)

However, the marginal effects of the dummy variables (such as corporate status and knowledge-based intensity), are evaluated by comparing the probabilities that result when these variables take values one and zero, all others are evaluated at their means (Greene, 1997).

3. METHODOLOGY

The next subsections present in detail the construction and definition of the dependent and explanatory variables. The data came from a survey conducted among Canadian organizations. The questionnaire was mailed to IT senior persons working in 1496 different companies. No prior contact had been made with the respondents. Of these, 200 returned a completed questionnaire, leading to a response rate of 13.3%. Respondents belonged to a wide variety of industrial sectors, representing the Canadian industrial composition. The most heavily represented sectors were the manufacturing and the financial sectors.

3.1 Variables Definition and Construction

Governance Modes In order to capture the richness of governance modes available to firms, our dependent variable allows for three alternative modes along the continuum between hierarchy and the market. Mode is thus an ordered variable equal to 0 when the firm chooses to keep an activity in-house (property rights allocated to the firm), to 1 if the firm chooses to use a mixed mode of governance (property rights split between the firm and the supplier), and to 2 if the firm chooses to completely outsource an



IT activity (all property rights allocated to the supplier)7. This represents an innovation compared to the usually treated “make-or-buy” conception of the outsourcing decision, which have been the object of many criticisms (Shelanski and Klein, 1995).

Each respondent provided information pertaining to sixteen different IT activities, which represent 3136 observations8. The dependent variable (modeij) thus represents the governance mode chosen by the firm i (i = 1, …, 196) for the IT activity j (j = 1, …, 16). This increases the richness of the statistical analysis by enabling us to exploit the differences between activities and between firms’ practices.

The sixteen IT activities are classified in three homogeneous groups: Management of IS activities (Mis), IS operations (Ois), and the maintenance of the systems (Mtnc). This categorization was established by Aubert et al. (forthcoming). Table 1 below presents the frequency of the dependent variable (governance mode) for each activity.

Table 1. Frequencies of Governance Mode Choices for IT Activities Studied

Frequencies (%) IT Activities Mode 0

(in-house) Mode 1

(mixed mode) Mode 2

(outsourcing) Management of Information System

Scheduling of operations 86.2 4.1 9.7 Control of operations 86.7 4.1 9.2 Production support services 68.4 14.8 16.8

IS Operations Operation of applications 83.2 5.6 11.2 Operation of operating system 77 7.7 15.3 CPU operation 77 8.2 14.8 Operation of client/server systems 84.2 7.7 8.2 Operation of telecommunication software 65.3 19.4 15.3 Printer operation 84.2 5.1 10.7 Disk space management 77.6 9.7 12.8

Maintenance of Information System Operating system maintenance 57.7 21.4 20.9 Hardware maintenance 23 43.9 33.2 PC maintenance 36.2 36.2 27.6 Network maintenance 52.6 30.1 17.3 Printer maintenance 23 45.4 31.6 Telecommunication lines maintenance 17.9 39.3 42.9

Transactional Characteristics Variables The first set of explanatory variables is composed of 6 variables representing the various transaction cost hypotheses (hypotheses 1 to 6). Using a 7-level Likert scale instrument, we thus obtain measures on technical and organizational skills, measurability of performance, standardization, complexity and asset specificity. Our instrument for volume uncertainty and other control variables are described in the next section.

7 A sample of the questionnaire items is presented in the Appendix. 8 Of the 200 initial questionnaires, four have been dropped because of their high rate of omitted questions (over 30%).



Volume Uncertainty and Control Variables These measures have been constructed using firm-based and industry-based statistics. The data come from various sources, including financial information for public firms and national statistics databases (Statistics Canada and Industry Canada).

Volume uncertainty. Following Levy (1985), this variable was defined as the difficulty to evaluate accurately the volatility of the industry demand for each firm of the sample. To construct the variable, we started with the monthly GDP (on a real basis) of 73 economic sectors, between January 1986 and December 1997. An auto-regressive model with 12 lags and the appropriate deterministic effects (monthly dummies (Di) and a trend) has then been estimated for each of the 73 industries using observed industrial GDP values as dependent variables:

∑∑∑ =+⋅+⋅+⋅===

T

t

tt

it

iit

iiit T

GDPlnLTrendDGDP ln2

212

1

12

1

where; ε

σεφβα (7)

From this model, four variables have been constructed in order to evaluate the ability of firms to anticipate the future volatility of the demand. On the one hand, the first two variables measure the unanticipated volatility of the demand, by the variance of the residuals (σ2) coming from estimating Eq. (7) and by the U-Theil statistic associated with the quality of the demand forecast using the same model9. An increase in both of these variables implies an increase in volume uncertainty, since increases of σ² and U-Theil are respectively associated with a larger variance of unforeseen demand and weaker predictions. The trend coefficient (β in Eq. (7)) represents a linear growth trend. The trend variable and the R² computed from estimating Eq. (1) are used to measure the anticipated volatility of the demand. An increase in both of these variables is associated with a decrease in volume uncertainty (the unexplained variance), since we can more easily anticipate future demand. These four variables are used alternatively in different specifications of the model.

The relative size of the firms included in the final sample is measured by the total sales of each firm, divided by the median of its industry, in order to adequately compare firms from different sectors10. Therefore, a ratio exceeding 1 by ‘x’ indicates that the firm is “x%” larger (in terms of sales) than the median of its sector.

Furthermore, the variable knowledge-based concentration index classifies the Canadian industrial sectors in three broad categories, with respect to the amount of knowledge embedded in firms in these sectors (high KB intensity, medium KB intensity, and low KB intensity). This classification has been constructed by Lee and Has (1995)11. Three dummy variables have thus been added to the model, representing each KB category. 9 The U-Theil statistic is measured using the prediction of the reel monthly GDP for each sectors between 1997 and 2000. The number of forecast periods (M) are therefore equal to 36, and the statistics is calculated using the following formula [White (1997)]:

2/1

2

21

2

2

)(

)ˆ(

00

0

−

−=

∑

∑

=−++

=++

M

ttNtN

M

ttNtN

YY

YYU

o

10 The median statistic has been preferred to the mean because of the small number of firms in some sectors. We used a “9 sectors” industry classification to construct this variable (finance and insurance, manufacturing, transportation, services, retail commerce, wholesale, mining and agricultural, construction, and government). 11 To classify the 55 industrial sectors into three subgroups, the authors used indicators of knowledge intensity, such as the share of R&D in sales and the proportion of highly-skilled workers in the workforce. To be considered a high intensity knowledge-based sector, the indicators for that sector must rank it in the top third of the distribution at least two times out of three. And conversely for low-intensity sectors, which are found at least two times out of three in the bottom third.



Finally, dummy variables have been added to take into account the corporate status of firms, i.e. private corporation, public corporation and crown organization. The industrial sector was also controlled for by adding nine industry dummies (finance and insurance, manufacturing, transportation, services, retail commerce, wholesale, mining and agricultural, construction, and government).

4. EMPIRICAL RESULTS

Table 2 presents descriptive statistics. The hypotheses are also indicated.

Table 2. Descriptive Statistics Variables Hypotheses Mean Std. Dev. Min. Max. Obs. Governance Mode 0,5609 0,7863 0 2 3136

Specificity

7 = highly specific H1: < 0 4,0705 1,7966 1 7 3136

Complexity 7 = highly complex

H2: < 0 3,9104 1,5276 1 7 3136

Standardization 7 = highly standard

H3: <> 0 4,6827 1,5567 1 7 3136

Measurability 7 = easy to measure

H4: > 0 4,7736 1,5690 1 7 3136

Technical skills 7 = highly needed

H5: > 0 5,3131 1,3986 1 7 3136

Organizational skills 7 = highly needed

H6: < 0 3,6955 1,8174 1 7 3136

Volume uncertainty

Sigma2 H7: > 0 0,0018 0,0030 1,4403E-06 0,0186 3136 U-Theil H7: > 0 0,6610 0,3537 0,1280 1,9760 3136 R² H7: < 0 0,9540 0,0476 0,7274 0,9999 3136 Abs(trend) H7: < 0 0,0026 0,0037 4,0099E-05 0,0229 3136

Relative Size (sales) 6,8182 29,9597 2,4600E-04 355,6070 3136 Corporation status dummies:

Private corporation 0,5510 0,4975 0 1 3136 Public corporation (omitted)

0,3827 0,4861 0 1 3136

Crown organization 0,0663 0,2489 0 1 3136 Knowledge Intensity Index:

High 0,2347 0,4239 0 1 3136 Medium 0,6378 0,4807 0 1 3136 Low (omitted) 0,1276 0,3336 0 1 3136

Industry dummies: Building 0,0204 0,1414 0 1 3136 Manufacturing (omitted) 0,3214 0,4671 0 1 3136 Wholesaling 0,1173 0,3219 0 1 3136 Retailing 0,0510 0,2201 0 1 3136 Finance and Insurance 0,2041 0,4031 0 1 3136 Transportation 0,1276 0,3336 0 1 3136 Services 0,0765 0,2659 0 1 3136 Government 0,0153 0,1228 0 1 3136 Mining and Agriculture 0,0663 0,2489 0 1 3136



4.1 SPECIFICATION ANALYSIS

Table 3 below shows the results of four log-likelihood ratio (LLR) tests, which validate our choice of a final specification, which is presented in Table 412. Thus, Table 4 shows the results of the estimation of Eq. (5) by maximum likelihood, while Tables 5a and 5b show the corresponding marginal effects of the main variables. In addition, Table 4 shows some measures of the goodness of fit, which indicate that the model is globally significant and explains a large part of the variance (LLR test of zero slope is highly significant for all specifications). Among these measures, the likelihood ratio index (around 18%) shows a significant increase of the explanation power of the model, in contrast with the more restricted model (zero slopes). The percentage of correct prediction is also quite high (near 70%), suggesting that the model explains very accurately the choice of governance modes for IT activities.

Table 3. Specification tests (Log-Likelihood ratio tests using model 1)

Null hypothesis df Chi-squared Ho 1: Stratification are non-significant 3 85.431 *** Ho 2: Homoskedasticity of the residuals 8 41.413 *** Ho 3: Transactional variables are non-significant 7 20.782 *** Ho 4: Organizational variables are non-significant 17 40.757 *** * significant at 10%. ** significant at 5%. *** significant at 1%

Table 4 also shows four different specifications, which alternately use one of the four demand predictability variables (trend, R², Sigma² and U-Theil). A first look at these results suggests that the variables measuring the facility to anticipate future demand (trend and R²) are more likely to explain the decision to outsource than the two others, which measure the difficulty to anticipate future movements (Sigma² and U-Theil).

12 These LLR tests have been constructed by comparing each specification against the restricted one without stratification and heteroskedasticity function. The results confirm the superiority of the stratified and heteroskedastic specifications (equation 6). The two groups of variables (XTC and XOC) are also globally significant, since we can easily reject the null hypotheses 3 and 4 at a confidence level below 1%.



4.2 TRANSACTIONAL HYPOTHESES

Concerning the validation of the transactional hypotheses (hypotheses 1 to 6), the results tend to confirm most of the theoretical relations. In particular, the two hypotheses concerning the relative importance of technical and organizational skills are strongly supported. When the importance of technical skills increases, the (marginal) probability of outsourcing goes up, while the importance of organizational skills significantly reduces the probability of choosing a market-based mode. The third hypothesis, related to the measurability of the supplier performance is also statistically supported, since the marginal effect of this variable on the probability of choosing the outsourcing mode is positive as expected.

Table 4. Results of the Ordered Probit Estimation Model 1 Model 2 Model 3 Model 4 Coefficients t-stat Coefficients t-stat Coefficients t-stat Coefficients t-stat

Independent variables

Specificity of assets 0.026 * 1.668 0.031 ** 1.995 0.030 * 1.909 0.032 ** 2.033 Complexity -0.020 -1.021 -0.022 -1.084 -0.020 -1.006 -0.022 -1.113 Standardization -0.011 -0.590 -0.013 -0.706 -0.013 -0.741 -0.012 -0.654 Measurability 0.040 ** 2.111 0.037 ** 1.968 0.039 ** 2.049 0.038 ** 2.019 Technical Skills 0.105 *** 5.032 0.106 *** 5.040 0.109 *** 5.151 0.104 *** 4.993 Organizational skills

-0.044 *** -2.880 -0.044 *** -2.845 -0.044 *** -2.862 -0.045 *** -2.889

Demand Uncertainty

Abs(Trend)13 -0.072 *** -3.125 R2 -1.181 ** -2.080 U-Theil 0.164 1.460 Sigma2 4.039 0.510 Control variables Relative size (sales) 1.336E-03 ** 2.077 1.370E-03 ** 2.155 1.190E-03 * 1.857 1.284E-03 ** 2.042 Private Corporation -0.116 ** -1.991 -0.134 ** -2.315 -0.138 ** -2.391 -0.139 ** -2.415 Crown Organization

-0.100 -0.680 -0.098 -0.655 -0.081 -0.542 -0.075 -0.507

High knowledge-based sectors

-0.333 *** -2.656 -0.375 *** -2.987 -0.455 *** -3.566 -0.417 *** -3.328

Medium knowledge-based sectors

-0.056 -0.468 -0.093 -0.782 -0.125 -1.052 -0.110 -0.935

Industry Building -0.115 -0.615 0.064 0.338 0.033 0.174 -0.003 -0.014 Mining and Agriculture

-0.127 -1.276 -0.070 -0.696 -0.137 -1.352 -0.107 -1.082

Wholesaling -0.013 -0.131 0.133 1.257 0.091 0.892 0.058 0.593 Retailing -0.384 ** -2.171 -0.279 -1.522 -0.317 -1.752 -0.361 ** -2.047 Finance and Insurance

0.257 *** 2.845 0.396 *** 4.017 0.271 *** 2.834 0.334 *** 3.661

Transportation and Communications

-0.098 -0.730 -0.015 -0.111 -0.076 -0.556 -0.072 -0.536

Services -0.084 -0.569 0.010 0.061 -0.099 -0.631 -0.045 -0.289 Government -0.463 * -1.871 -0.389 -1.561 -0.401 -1.617 -0.392 -1.587 Management dummies

ACT1 -1.017 *** -4.206 0.144 0.241 -1.135 *** -4.336 -0.986 *** -4.059 ACT2 -1.079 *** -4.450 0.081 0.135 -1.200 *** -4.581 -1.048 *** -4.299 ACT3 -0.483 ** -2.121 0.672 1.141 -0.608 ** -2.427 -0.456 ** -1.994

13 In order to facilitate the interpretation of the coefficient and the marginal effects, the variable abs(trend) has been standardized by dividing each observation by the series’ mean.



Table 4. Results of the Ordered Probit Estimation, cont’d Model 1 Model 2 Model 3 Model 4 Coefficients t-stat Coefficients t-stat Coefficients t-stat Coefficients t-stat

Operations dummies

ACT4 -0.987 *** -4.132 0.167 0.282 -1.112 *** -4.273 -0.961 *** -4.008 ACT5 -0.904 *** -3.782 0.253 0.425 -1.030 *** -3.938 -0.876 *** -3.65 ACT6 -0.923 *** -3.853 0.235 0.394 -1.048 *** -4.012 -0.895 *** -3.72 ACT7 -1.095 *** -4.518 0.061 0.103 -1.224 *** -4.606 -1.064 *** -4.369 ACT8 -0.525 ** -2.378 0.627 1.073 -0.652 *** -2.676 -0.499 ** -2.248 ACT9 -1.040 *** -4.570 0.124 0.209 -1.162 *** -4.611 -1.006 *** -4.39 ACT10 -0.855 *** -3.662 0.309 0.519 -0.978 *** -3.823 -0.821 *** -3.499 Maintenance dummies

ACT11 -0.161 -0.718 0.994 * 1.701 -0.290 -1.176 -0.136 -0.601 ACT12 0.543 ** 2.429 1.701 *** 2.913 0.418 * 1.701 0.568 ** 2.543 ACT13 0.295 1.358 1.453 ** 2.489 0.169 0.704 0.321 1.476 ACT14 -0.097 -0.431 1.057 * 1.806 -0.227 -0.912 -0.071 -0.313 ACT15 0.574 *** 2.654 1.734 *** 2.983 0.452 * 1.900 0.6 *** 2.781 ACT16 0.834 *** 3.815 1.995 *** 3.419 0.712 *** 2.944 0.862 *** 3.941 Heteroskedastic function

Mining and Agriculture

-0.431 *** -3.739 -0.442 *** -3.784 -0.434 *** -3.730 -0.453 *** -3.821

Building -0.063 -0.379 -0.067 -0.402 -0.058 -0.351 -0.072 -0.431 Wholesaling -0.070 -0.766 -0.067 -0.726 -0.065 -0.704 -0.069 -0.749 Retailing -0.075 -0.533 -0.082 -0.579 -0.080 -0.564 -0.083 -0.589 Transportation and Communications

0.242 ** 2.218 0.240 *** 2.219 0.259 *** 2.362 0.248 ** 2.272

Finance and Insurance

0.264 *** 2.920 0.290 *** 3.192 0.279 *** 3.054 0.280 *** 3.072

Insurance Services 0.253 * 1.916 0.298 *** 2.260 0.291 *** 2.211 0.298 ** 2.250 Government -0.933 *** -2.998 -0.936 *** -2.988 -0.944 *** -2.986 -0.938 *** -2.995 Thresholds Mu – MIS 0.385 *** 6.461 0.388 *** 6.451 0.388 *** 6.452 0.387 *** 6.453 Mu – OIS 0.441 *** 9.926 0.444 *** 9.898 0.443 *** 9.893 0.442 *** 9.868 Mu – Mtnc 1.085 *** 17.101 1.090 *** 17.103 1.090 *** 17.041 1.087 *** 16.923 Nb. of observations 3136 3136 3136 3136 Iterations completed

58 56 56 57

Log likelihood function

-2372.36 -2377.3 -2376.8 -2378.54

Restricted log likelihood

-2889.76 -2889.7 -2889.76 -2889.76

Likelihood ratio index

0.179 0.177 0.178 0.177

Right fitted ratio 0.677 0.677 0.679 0.679 Chi-squared 1034.792 *** 1024.85 *** 1025.885 *** 1022.438 *** Degrees of freedom 45 45 45 45 * significant at 10%. ** significant at 5%. *** significant at 1%.



Table 5 Marginal Effects of Continuous Variables on the

Probability of Outsourcing

TABLE 5a. Marginal effects of continuous variables (from model 1) Management of

Information System Exploitation of Information

System Maintenance of

Information System P(Mode=

0) P(Mode=

1) P(Mode=

2) P(Mode=

0) P(Mode=

1) P(Mode=

2) P(Mode=

0) P(Mode=

1) P(Mode=

2) -f(B'X)B [f(-B'X)

- f(mu-B'X)]B

f(mu-B'X)

-f(B'X)B [f(-B'X) -f(mu-

B'X)]B

f(mu-B'X)B

-f(B'X)B F(mu-B'X) -

F(-B'X)

f(mu-B'X)B

Technical skills

-0.0301 0.0096 0.0205 -0.0314 0.0096 0.0218 -0.0371 -0.0044 0.0416

Organizational skills

0.0127 -0.0041 -0.0087 0.0133 -0.0041 -0.0092 0.0157 0.0019 -0.0175

Complexity 0.0059 -0.0019 -0.0040 0.0061 -0.0019 -0.0042 0.0072 0.0009 -0.0081Standardization

0.0031 -0.0010 -0.0021 0.0032 -0.0010 -0.0022 0.0038 0.0004 -0.0042

Measurability -0.0114 0.0037 0.0078 -0.0119 0.0036 0.0083 -0.0141 -0.0017 0.0158 Specificity of assets

-0.0075 0.0024 0.0051 -0.0078 0.0024 0.0054 -0.0092 -0.0011 0.0103

Abs(Trend) 0.0209 -0.0067 -0.0142 0.0217 -0.0066 -0.0151 0.0257 0.0031 -0.0288Relative Size -3.85E-

04 1.23E-04 2.626E-

04 -4.01E-

04 1.23E-04 2.78E-04 -4.74E-

04 -5.64E-

05 5.30E-04

TABLE 5b. Marginal effects of dummy variables (from model 1) Management of

Information System Exploitation of Information

System Maintenance of

Information System Pr(Mode

= 0) Pr(Mode

= 1) Pr(Mode

= 2) Pr(Mode

= 0) Pr(Mode

= 1) Pr(Mode

= 2) Pr(Mode

= 0) Pr(Mode

= 1) Pr(Mode

= 2) 1-F(B'X) F(mu-

B'X) - F(-B'X)

1-F(mu-B'X)

1-F(B'X) F(mu-B'X) -

F(-B'X)

1-F(mu-B'X)

1-F(B'X) F(mu-B'X) -

F(-B'X)

1-F(mu-B'X)

Private = 1 0.8043 0.0887 0.1070 0.7894 0.0935 0.1171 0.3318 0.1484 0.5198 Private = 0 0.7724 0.0988 0.1288 0.7561 0.1036 0.1402 0.2928 0.1437 0.5635

Change 0.0319 -0.0101 -0.0217 0.0332 -0.0101 -0.0231 0.0390 0.0047 -0.0437High KB = 1 0.8533 0.0712 0.0755 0.8408 0.0759 0.0833 0.4047 0.1526 0.4427 High KB = 0 0.7682 0.1001 0.1317 0.7518 0.1049 0.1433 0.2881 0.1430 0.5689

Change 0.0851 -0.0289 -0.0562 0.0890 -0.0290 -0.0600 0.1166 0.0096 -0.1262Note: f(B’X) is the density function of the normal distribution, while F(B’X) is the cumulative distribution function of the normal.



However, the hypotheses related to the complexity and to the standardization of the transactions are not supported by the results. The marginal effects of these variables on the choice of governance mode are not statistically different from zero. In addition, the specificity of assets has a positive and significant impact on the probability of outsourcing. This result contradicts the TCT hypothesis, but seems recurrent in the recent literature [Murray and Kotabe (1999), Nam et al. (1996)].

Finally, with the exception of model 2, the activity dummies are globally highly significant, while the group-specific thresholds are significantly different from a restricted one. The signs of these coefficients also confirm that there are group specific effects for IT activities. In fact, the coefficients associated with the intercepts and the thresholds clearly confirm that the maintenance activities are generally more outsourced than the two other groups. This suggests that firms see a net market-burden cost (or significant risk) of outsourcing management and operation activities, while there is a net management-burden cost of keeping maintenance activities internal.

4.3 VOLUME UNCERTAINTY

The results from Table 4 tend to confirm the traditional hypothesis related to demand uncertainty. They confirm the idea that a more predictable economic environment (measured by the presence of a trend in the industry demand and the coefficient R² of the forecasting model) significantly reduces the incentive of firms to outsource globally or in part their IT activities. Table 5a shows that an increase of 1% of the level of the trend variable (compared to the series’ mean) reduces the probability of choosing the first mode by around 2%.

4.4 CONTROL VARIABLES

The results also show that many of the control variables had significant effects. First, the relative size of the firms has a positive impact on the outsourcing decision. Also, private corporations statistically outsource less than their public counterparts, which confirms the idea that publicly traded corporations might be subject to more pressure to remain efficient. In addition, the marginal effect of the knowledge-based (KB) intensity variable is very important and statistically significant. In fact, our results show that, on average, firms in the highest KB sectors outsource around 10% less than the ones from the lowest KB category.

5. DISCUSSION

Given the preceding results, we can conclude that the mode chosen by firms to govern their IT activities is guided by cost-minimization considerations, as suggested by the economic theory of the firm. In fact, the major transactional determinant of the governance mode choice is the need for technical and/or organization skills. This constitutes a strong endorsement for the Grossman and Hart (1986) proposition, that the allocation of the property rights over an asset is determined by the relative importance of the ex ante investments in human capital made by the buyer and the supplier. In the case of the IT assets, these investments take the form of an increase in the technical skills (supplier investment) and in the organizational skills of the employees (firm investment). All else being equal, an increase in the importance of one of these two types of skills strongly expands the probability that the party who has to make the investment will seek to retain control over the transaction. From a property rights point of view, and given an incomplete contracting situation, this result leads to an efficient allocation of property rights, since it provides the necessary incentives to each party to commit investments to the transaction.

In addition, the measurability of the supplier’s performance is found to be a main driver explaining outsourcing decisions, confirming the postulated hypothesis. When firms cannot clearly verify the quantity and the quality of the suppliers work, they prefer to rely on authority and an employment contract. However, it appears that it is the measurability of supplier performance that is important, rather than the facility to observe and evaluate the process of this work. In fact, the ability to



evaluate the process (measured by the standardization of an activity) is not a significant variable explaining the outsourcing decision. This represents an important insight for the measurement arm of TCT, since it suggests that no matter how we observe the work of an agent, the key factor is the quality of the measures that support the evaluation of the agent’s performance. This is a critical determinant of the effectiveness of market institutions. Low measurability of the supplier’s performance clearly increases sharply the cost of outsourcing IT activities, thus inducing firms to internalize these. Future research in computer science and information system management should thus focus on the development of effective tools to facilitate the monitoring of supplier output.

On the other hand, we found little evidence that the complexity of the transaction reduces the likelihood of outsourcing. Demand and volume uncertainty appears to be driving outsourcing decisions much more than the contractual difficulties that come with increased complexity. Combining these observations with the salience of measurement problems suggests that, all else being equal, there are some contractual tools that can be cost-effectively used to deal with the complexity of transactions, while handling performance ambiguity and measurement problems poses a greater challenge for IT outsourcing.

With respect to asset specificity, our results seem to contradict the TCT prediction, since they suggest that the level of asset specificity increases the probability of outsourcing. One should remember, however, that we dealt with human capital asset specificity through our technical and organizational skills constructs. Overall, our results tend to confirm that a high level of asset specificity embedded in human capital will dampen the probability of outsourcing IT activities, while other forms of asset specificity seem to have the opposite effect. Outsourcing is seen as a strategy to acquire important technical skills, while the decision to internalize the transaction responds to a greater need for organization skills in the execution of IT activities.

Our results concerning the Knowledge-Based intensity of firms also reflect the importance of human capital aspects in the outsourcing decision. On one hand, they suggest that firms that possess highly qualified human resources have fewer incentives to outsource their IT services. On the other hand, this result can also be interpreted in terms of the added value of IT activities. As measured by Lee and Has (1995), it is in fact reasonable to suppose that highly intensive KB sectors make a more valuable utilization of their information system. Putting this in a resource-based framework, we can thus conclude that firms in the highly intensive KB sectors keep the operation of their IT internal because they are part of their core knowledge. For these organizations, in contrast with the ones from the lowest category, the outsourcing option represents a higher risk of losing this competitive advantage.

As for firm size, and contrary to the scale economies hypothesis, our results show that larger firms are inclined to outsource their IT activities more. This result, which has been confirmed by many others (Poppo and Zenger, 1998), tends to confirm the idea that increasing management costs, linked to wider and more complex organizational structure, are major determinants of the outsourcing of non-core activities. This makes some sense in the context of IT, since information system services are generally viewed as non-core activities, and difficult to manage internally (Lacity and Hirscheim, 1993).

Furthermore, our results bring interesting new insights concerning the role played by demand volatility in the modeling of organizational structure. Defining volume uncertainty as the ability to anticipate future demand movements, we found that firms tend to outsource their IT services more when the level of industry-wide economic activity is highly volatile and difficult to anticipate. As Boyer and Moreaux (1999) found, this suggests that outsourcing is viewed as a strategic option to increase the flexibility of firms in the context of a highly unstable economic environment. In fact, when future demand is difficult to anticipate, outsourcing is a valuable option which reduces the need to



invest in costly and rigid IT infrastructure that would not be used full-time. This is coherent with the explanation of frequency provided by Williamson (1985). Suppliers are thus able to manage this uncertainty more efficiently, for instance by having many users of the same infrastructure.

Finally, the positive impact on the probability to outsource of the dummy for publicly-traded corporations may explain the well-known “bandwagon effect” observed in the IT sector (Lacity and Hirscheim, 1993). This result suggests that publicly-traded corporations received indirect financial incentives to outsource their IT, which is not the case of privately held ones. As noted by Loh and Venkatramen (1992), these specific incentives may come from the positive publicity surrounding many outsourcing “success stories” during the 90’s – the so-called “Kodak effect.”

6. LIMITATIONS AND CONCLUSION

We have developed and estimated a model to explain the decision to outsource IT activities based on the TCT framework. The originality of our modeling comes from our taking into consideration many dimensions that have not in the past been dealt with simultaneously. We have also allowed for three governing modes for IT activities, instead of the traditional “make-or-buy” approach. Our estimation strategy rests on a very large and diversified data set, which eliminates a number of biases that were present in previous research. Our results were obtained using an ordered probit approach and proved very robust. Overall, the predictions of TCT regarding the outsourcing of IT activities are validated, in particular regarding the importance of measurement problems, of uncertainty, and of human capital asset specificity. Our results also point out to significant activity- and sector-specific effects.

One limit of the study is the focus on IS operations. While variance was observed between activities and groups of activities (operations, maintenance, and management), it is plausible to suppose that contrast would have been more important if software development had been included in the portfolio of activities. This is especially true for the construct of asset specificity. There might not be enough asset specificity in IS operations to obtain significant results.

Results suggest several avenues for future research. For instance, the contract parameters would deserve attention from research. While the TCT variables predict the decision to outsource or to internalize an activity, they will also influence the type of contract signed when activities are outsourced. A formal test could include the ability of TCT to predict the portfolios of control mechanism. Choudhrury and Sabherwal (2003) showed that several different portfolios of control could be used in outsourced software projects. It is reasonable to assume that it is also the case for IS operations. Identifying the predictors of portfolio choice would be an interesting avenue for research. The results also bring forward the Grossman and Hart (1986) argument, and the notion of control of critical assets in a transaction. This was the component of TCT that had been less frequently included in past empirical efforts. The results suggest that it is an important part of the outsourcing decision.



REFERENCES

Alchian, A. A., H. Demsetz. 1972. Production, information costs, and economic organization. American Economic Review. 62(4) 777-795.

Alpar, P., A. N. Saharia. 1995. Outsourcing information system functions: an organization economics perspective. Journal of Organizational Computing. 5(3) 197-217.

Anderson, E. 1985. The salesperson as outside agent or employee: A transaction cost analysis. Marketing Science. 4(3) 234-254.

Aubert, B. A., M. Patry, S. Rivard. forthcoming 2004. A transaction cost model of it outsourcing. Information and Management.

Aubert, B., S. Rivard, M. Patry. 1996. A transaction cost approach to outsourcing behavior: Some empirical evidence. Information and Management. 30(2) 51-64.

Barney, J., K. Ouchi. 1986. Transaction-cost economics: governing economic exchanges, J. Barney, K. Ouchi, eds., Organizational Economics, 1st Edition, Jossey-Bass, San Francisco, CA.

Barzel, Y. 1982. Measurement cost and the organization of markets, Journal of Law and Economics, 25(April), 27-48.

Boyer, M., M. Moreaux. 1999. Impartition stratégique et flexibilité. M. Poitevin, ed. Impartition : fondements et analyses. Presses de l’Université Laval, Quebec, Canada.

Coase, R. H. 1937. The nature of the firm. Economica. 4(13-16) 386-405. Choudhury, V., R. Sabherwal. 2003. Portfolios of control in outsourced software development projects.

Information Systems Research 14(3) 291-314. Cosgrove Ware, L. 2001. Upward and onward with outsourcing. CIO Magazine. August 1. Greene, W. H. 1997. Econometric Analysis, 3rd Edition. Prentice Hall, Upper Saddle River, NJ. Grossman, S., O. Hart. 1986. The costs and benefits of ownership: a theory of vertical and lateral

integration. Journal of Political Economy. 94(4) 691-719. Hennart, J. F. 1988. Upstream vertical integration in the aluminum and tin industries, a comparative

study of the choice between market and intrafirm coordination. Journal of Economic Behaviour and Organization. 9(3) 281-299.

Kouvelis, P., J. M. Milner. 2002. Supply chain capacity and outsourcing decisions: The dynamic interplay of demand and supply uncertainty. IIE Transactions, 34(8) 717-729.

Lacity, M. C., R. Hirschheim. 1993. Information Systems Outsourcing. John Wiley & Sons, New York, NY.

Lee, F.C., H. Has. 1996. Évaluation quantitative des industries à forte concentration de savoir par rapport aux industries à faible concentration de savoir. P. Howitt, ed. La croissance fondée sur le savoir et son incidence sur les politiques micoréconomiques. Industry Canada, University of Calgary Press, Calgary, Canada.

Levy, D. T. 1985. The transactions cost approach to vertical integration: An empirical examination. Review of Economics and Statistics. 67(3) 438-445.

Loh, L., N. Venkatraman. 1992. Diffusion of information technology outsourcing: Influence sources and the Kodak effect. Information Systems Research. 3(4) 334-358.

Masten, S. E. 1984. The Organization of production: evidence from the aerospace industry. Journal of Law and Economics. 27(2), 403-418.

Milgrom, P., J. Roberts. 1992. Economics, Organization and Management. Prentice Hall, Englewood Cliffs, NJ.

Monteverde, K., D. J. Teece. 1982. Supplier switching costs and vertical integration in the automobile industry. The Bell Journal of Economics. 13(1) 206-213.

Murray, J. Y., Kotabe, M. 1999. Sourcing strategies of U.S. service companies: A modified transaction-cost analysis. Strategic Management Journal. 20(9) 791-809.

Nam, K., S. Rajagopalan, H. R. Rao, A. Chaudhury. 1996. A two-level investigation of information systems outsourcing. Communications of the ACM. 39(7), 37-44.



Pisano, G. 1990. The R&D boundaries of the firm: an empirical analysis. ASQ. 35(1) 153-176. Poppo, L., T. Zenger. 1998. Testing alternative theories of the firm: Transaction cost, knowledge-

based, and measurement explanations for make-or-buy decisions in information services. Strategic Management Journal. 19(9) 853-877.

Shelanski, H. A., P. G. Klein. 1995. Empirical research in transaction Cost Economic: A review assessment. The journal of Law, Economics, & Organization. 11(2) 335-361.

Scholl, R., B. Caldwell, I. Marriott, B. Pring, G. Tramacere, A. Young, A. Couture. Outsourcing providing growth amidst the gloom, dataquest perspective, Note Number: ITSV-WW-DP-0421, Gartner Group, 18 November 2002.

Slaughter, S., S. Ang. 1996. Employment outsourcing in information systems. Communication of the ACM. 39(7) 47-54.

Teng, J. T. C., M. J. Cheon, V. Grover. 1995. Decisions to outsource information systems functions: Testing a strategy-theoretic discrepancy model. Decision Sciences. 26(1) 75-104.

Van de Ven, A., D.L. Ferry. 1980. Measuring and Assessing Organizations, Wiley, New York, NY. Walker, G., D. Weber. 1984. A transaction cost approach to make-or-buy decisions. ASQ. 29(3) 373-

391. Wholey, D. R., R. Padman. 1998. Risk, uncertainty, and Transaction organization: determinants of

information technology outsourcing among health maintenance organizations, InterStudy Publications, July.

Williamson, O. 1985. The Economic Institutions of Capitalism. Free Press, New York, NY.



Appendix: Example of Questionnaire Pages

cahier0413 - queen's u

Documents