التصميم المرن اضفاء الطابع الرسمي
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Transcript of التصميم المرن اضفاء الطابع الرسمي
Electronic copy available at: http://ssrn.com/abstract=2130866
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Contracts as Organization: Designing Flexible Formalization
Anna Grandori
Bocconi University Department of Management and Technology
Milano, Italy
and Marco Furlotti1
Tilburg University Department of Organization and Strategy
Tilburg, the Netherlands
2012-01-25 Abstract
The design of a proper formalization of economic relationships, through contractual and non-contractual documents, and its interaction with informal governance, is a re-emerging issue in organization theory. In fact it is of considerable importance in an era of increasing uncertainty and risk, where protection from hazards and coordination power on one side, but also flexibility on the other, are needed. A common way of framing and solving the problem has been to consider those needs as contrasting requirements affecting the effective degree of formalization. Integrating organization theory with contract theory this study shows that a better solution exists. Uncertainty changes the nature rather than the degree of effective formalization: ‘constitutional’ agreements (including decision, exit and property rights) can and should be more specified, rather than less, as uncertainty and conflict potential grow; while the detail of ‘operational’ agreements is the kind of formalization that is at odd with uncertainty. Survey-data analyses of the formal documents regulating 440 inter-organizational projects in different countries and sectors are presented, corroborating the main hypothesis and suggesting interesting refinements also on the other commonly used predictors of formalization (size/complexity of activities, partner irreplaceability and the longevity of relationships). Key words: contracts; formalization, organizational design; projects, strategic alliances; uncertainty
1 Corresponding author ([email protected])
Electronic copy available at: http://ssrn.com/abstract=2130866
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CONTRACTS AS ORGANIZATION: DESIGNING FLEXIBLE FORMALIZATION
INTRODUCTION
To formalize or not to formalize: that is the question… or not? The design of contracts and
other documents formalizing agreements and rules is typically framed as a dilemma between
the virtues and vices, the benefits and costs, of formalization versus informality. Are detailed
and comprehensive contracts a means for protecting the parties from abuse, or are they a way
of ossifying relations (Orr, 2006)? Are incomplete contracts more flexible and therefore more
advantageous under conditions of uncertainty (Loasby, 1976), or are they the source of all
evils (Hart, 1988)? Why rather simple contracts are suitable to regulate very complex matters
(Al-Najjar, 1995)? These are some of the puzzles of formalization in contract theory, which
are of considerable importance also in the practice of contract design.
Similar puzzles can also be identified in organizational analyses of formalization. Are
detailed formal job descriptions, transparent rules and decision making procedures – both in
internal organization or in interfirm structures (e.g. franchising and sub-contracting) – the
only way of bringing about transparency and professionalism (Weber, 1922), accountability
(Hannan & Freeman, 1984), procedural justice (Greenberg, 1987), and large scale
coordination (Blau & Scott, 1963; Pugh, Hickson & Hinings, 1969)? Or are they the source
of bureaucratic pathologies and the most severe obstacle to flexibility and innovativeness
(Burns & Stalker, 1961; Volberda, 1998)?
The issue behind those puzzles is that formalization involves an ex-ante determination of
future behaviors. By contrast, flexibility is commonly intended as the capability to
reconfigure a system in the face of unforeseeable events (Volberda, 1998). Therefore,
formalization and flexibility are usually seen as contradictory. As a result, scholars both in
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economics and organization theory have often sided with or against the formalization of
uncertain relations, or have proposed selecting an intermediate ‘optimal’ level of
formalization, to ensure a modicum of protection and memory with some but not extreme
losses of flexibility. The core contribution of the present paper in this respect is to show that
in fact it would be possible to have high flexibility and high protection at the same time.
According to an acceptable working definition, widely employed in the study of organized
economic behavior, formalization is the codification and definition of entities, rights and
obligations, rules and actions in written, inter-subjectively verifiable documents, such as
contracts, organization charts, and task descriptions.1 As such, it may include a contractual
component (usually the focus of economic analyses) and a non contractual component
(usually the focus of the analyses of organization theory). In all transaction of some
complexity, in fact, a set of formal rules and agreements, complement and ‘continue’
contracts not only within organizations (Williamson, 1993), but also in inter-organizational
agreements (Vlaar, Van Den Bosch & Volberda, 2007). Therefore, the fact that both
components – contractual and non-contractual – are considered as applied to the governance
of the same set of transactions, is another contribution of the present study.
The paper’s main conclusion is that the way to achieve flexibility and protection
simultaneously is to respond to uncertainty by changing the ’type’ of formalization (to what
extent different types of coordination mechanisms are formalized) rather than the degree of
formalization (how much contracts and other documents are elaborate and detailed).
In principle, the general theoretical argument developed here on the different components of
formalization applies to organization in general, no matter whether internal or interfirm, as it
is the case for the theories it draws on, as contract theory, transaction cost economics and
organizational coordination theory. The empirical study presented here is on the particular
field of inter-firm governance. The analyses are conducted on a data-base of 440 multi-firm
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projects in selected industries and countries. To study the topic of flexible formalization in
that setting has several advantages. First, projects are becoming an increasingly diffused but
yet under-researched organizational form at the interorganizational level (Windeler & Sydow,
2001). Second, temporary organizations present the trade-off between protection and
flexibility in a more clear-cut way, as parties are less helped by the reliance on the continuity
of their relationship, which would extend the ‘self-enforceability range’ (Klein, 1985) of their
agreements. Further research on how the propositions advanced here should be
refined/modified for contracts and formal documents within organized entities (firms, public
agencies etc.), is left as an implication and task for future efforts.
The remainder of the paper is organized as follows: In the next section a review of extant
literature on the properties and design of formalization is offered, to re-establish the meaning
and the relevance of a construct that over the years has progressively slipped into theoretical
obscurity. Then insights from organization theory, economics, and legal scholarship are
brought together to argue that formalization comes in qualitatively different types, and to
achieve theoretical understanding about the content of documents that might be both adaptive
and protective. Hypotheses regarding the main predictors of the use, under conditions of
efficiency, of each type of formalization follow, accompanied by an empirical analysis of
contractual and extra-contractual formal governance on a data base of 440 inter-
organizational projects.
FORMALIZATION IN ORGANIZATION THEORY, LAW AND ECONOMICS
In the beginning… there was bureaucracy (Weber, 1922). It is widely known and reported
that among the features of the bureaucratic organizational ideal-type, formalization occupied
a prominent place. The reasons of the Weberian praise of formalization are less frequently
recalled. Weber’s main concern was to free the then-modern organization of firms and
markets from the pathologies that were common in the pre-capitalistic organization: the
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mingling of private and organizational interests and patrimony; the exercise of influence
through any sort of bargaining power rather than of legitimate authority and rules; the lack of
organizational memory and of clear responsibilities; the non-verifiability of actions; the
unreliability of promises, the difficulty of enforcing agreements with contracting parties, and
the generalized difficulty of predicting economic behaviors. His thesis of the superiority of
formal organization was based on the analysis of those dysfunctions in prior and more
‘informal’ organization. These concerns have recently resurfaced in some contributions in
organizational sociology, underscoring that formalization increases accountability (Hannan &
Freeman, 1984) as well as transparency and procedural justice (Greenberg, 1987).
Organization theorists have over time uncovered further functions of formalization,
especially in coordination and decision-making respects. As codes of behaviours, formal
rules, procedures and programs reduce communication costs (Blau & Scott, 1963) and
substitute more expensive mechanisms based on ad hoc decision-making (March & Simon,
1958; Van De Ven, Delbecq & Koenig, 1976). As expectations of behaviours, they reduce
variance in conduct and decision-making costs (Child, 1973). The process that leads to the
drafting of formal documents is also an occasion to focus attention (Delmar & Shane, 2003),
to clarify expectations (Shenkar & Zeira, 1992), and to stretch the mind and anticipate
contingencies and consequences (Kale, Dyer & Singh, 2001). Moreover, formal documents
allow the memorization and retention of information and facilitate the transfer and the
diffusion of knowledge (De Boer, Van Den Bosch & Volberda, 1999). In fact other
organization theory (henceforth, OT) contributions drawing on the Simonian information
processing tradition, suggested that ‘problem size’ – i.e. the ‘number’ of actions and actors to
be taken into account – raises the need for formal decision supports (Galbraith, 1974). This
factor is seen as the main conceptual reason why system size is always found to correlate
positively with formalization, no matter whether the system is a firm (Pugh et al., 1969) or a
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network of firms (Alter & Hage, 1993). More precisely, a relevant point made by OT
research is that interfirm formal agreements, as much as internal ones, are likely to have
coordination functions and not only conflict resolution functions (Grandori, 1997; Gulati,
Lawrence & Puranam, 2005).
The major analyses of the advantages of formalization in terms of conflict resolution have
been produced by organizational economists who see formal governance as set in place first
and foremost by formal contracts – written documents that state the rights and obligations of
transacting or cooperating parties and that are legally enforceable (Williamson, 1975, 1979).
Formal contracts are seen as the main mechanism by which parties can safeguard themselves
against non compliance by others. Thus, they are used in situations where there is an actual or
potential conflict. Conversely, in conflict-free situations, converging interests make
agreements ‘self-enforceable’ so contracts can remain informal (Baker, Gibbons & Murphy,
2002). Hence, the higher the actual or potential conflict between interests the more intensely
and completely economic relationships should be formalized.
Organizational economics also acknowledged the importance of non-contractual
formalization, through rights and obligations stated in private documents that are too specific
and unverifiable to be guaranteed by external courts, but are made enforceable by the
existence of the ‘internal court’ of hierarchy (Williamson, 1993). Although this argument was
formulated with reference to internal organization, it can also be applied to private arbitrators
and non-contractual agreements in inter-organizational relationships. An additional reason for
non-contractual formalization in interfirm relationships highlighted in some law and
economics studies is that the transacting parties themselves may intentionally not want to
include in legal contracts agreements that are too precise because they do not want to be
legally bound to such commitments (Crocker & Masten, 1991). Issues of this kind are
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common in interfirm negotiations and they contribute to the formation of a set of non-
contractual documents that are at times quite thick (Vlaar et al., 2007).
If formalization only had advantages, though, we would live in a completely pre-defined and
formally regulated world. But, formalization does entail costs of analysis, writing and
enforcement (as emphasized especially in economic contributions), and even negative
consequences and outright dysfunctions (as underscored in OT and, as we shall see, in some
contributions in law).
The dysfunctions of bureaucracy in general and of formalization in particular, were
highlighted in early organizational sociology studies. The formalization of rules has been
shown to give rise to bureaucratic red-tape and poor adaptability to changing circumstances;
the formalization of structure to the creation of vested interests and of inter-unit conflict
(Merton, 1949); and both to organizational inertia. Drawing on those insights, OT has
become increasingly critical of formalization, for the ‘rigidity’ it infuses in organizational
systems. It has been argued that under conditions of uncertainty it increases the possibility of
maladaptation, as the detailed knowledge of circumstances that would be needed to select the
appropriate behavior cannot be obtained ex-ante (Burns & Stalker, 1961). Hence it has been
hypothesized, and generally found, that the higher the level of uncertainty, the lower the
effective ‘degree of formalization’ – i.e. the volume of written documents such as contracts,
job descriptions, rules, procedures and programs (Donaldson, 2001; Lawrence & Lorsch,
1967). The ensuing and still prevalent belief is that formalization is at odds with flexibility
and innovativeness; and therefore informal governance is better for achieving them
(Volberda, 1998).
Research on inter-organizational relations has also emphasized the virtues of informal ties
and agreements. Long-lasting relationships and a common culture are typically hypothesized
to bring about trust and self-enforceable informal relational contracting, and are seen as the
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basic remedy for the failures of formal contracts under uncertainty (Reuer & Ariño, 2007;
Ring & Van de Ven, 1992).
A similar conclusion is also common in organizational economics (Al-Najjar, 1995; Miller,
1992; Ouchi, 1980). In fact, organizational economics also see formalization as unfit for
handling uncertainty primarily because of the costs incurred in writing and enforcing
contracts when the utility of parties can be affected in unpredictable ways by a myriad of
contingencies. Hence, as uncertainty increases, contracts are thought to become more and
more incomplete, thereby falling increasingly short of fulfilling their distinctive function of
providing protection against noncompliance (Williamson, 1979).
Acknowledging that formalization has both advantages and disadvantages, recent
contributions in organizational economics have explicitly framed the problem of ‘optimal
formalization’ as a trade-off between the marginal net protection benefits of formalization
and its marginal writing and enforcement costs (Battigalli & Maggi, 2002; Bernheim &
Whinston, 1998). Hence, the optimal degree of formalization becomes a decreasing function
of uncertainty, a conclusion in line with that of OT research. Accordingly, empirical studies
in the economics of contracts have focused on the degree of formalization as the degree of
completeness and of complexity in contracts. Among the findings of that research are that as
technological or market uncertainty increases, contracts contain less precise provisions
(Crocker & Reynolds, 1993) and a smaller number of clauses (Saussier, 2000); and that as
conflicts of interest and the potential for opportunism decrease, the probability of less
detailed contracts increases (Corts & Singh, 2004). This solution to the formalization design
problem has largely influenced empirical research on contracts also in the management field,
in which the ‘degree of complexity’ and ‘degree of completeness’ figures prominently as a
dependent variable.
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A somewhat different view of the nature of contracts is however present in law-and-
economics (Crocker & Masten, 1991; Goldberg, 1976). These contributions have in fact
criticized the view of contracts prevalent in neo-institutional economics for being
mechanistic, and made the important observation that contracts do not necessarily concern
only the terms of exchange, but they often include agreements on the procedures to be
followed for changing those terms and regulating the parties’ relationship: they establish “a
constitution regulating the on-going relationship” (Goldberg, 1976). In this view, ‘relational’
contracting does not mean ‘informal’ contracting, as in the incomplete contract tradition. It
means that the formal contract is used to govern the relation more than the content (the terms
of exchange). This observation highlights that part of contractual provisions have the nature
of ‘procedures’ or ‘rules’, rather than substantive stipulations. In turn, this opens the door to
applying to contracts concepts that have been developed in organization theory, law and the
economics of rules, as to the design of rules and procedures. Those contributions will be used
and developed in the next section, to respond to the core question of whether and how it is
possible to analyze and design formal contracts (as well as non-contractual documents) in a
better way than through a trade-off between protection and flexibility. The core contribution
given is to demonstrate that it is actually possible to get both flexibility and protection from
opportunism, that is, of achieving flexible formalization.
A MODEL OF FLEXIBLE FORMALIZATION
Constitutional and operational agreements and rules
In contrast to marginalist economics, which focuses on differences in quantity or degree,
organizational and institutional analysis is chiefly concerned with qualitative differences, that
is with ‘differences in kind’ (Simon, 1978; Williamson, 2004). Applying this principle to the
issue at hand, let us note that formalization is an attribute that may vary in degree, but that
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can also be applied to aspects of organization that differ in kind. It is possible to formalize
structures or processes, actions to be taken or the procedures for choosing actions, the identity
of players or the relationships between them, etc. Even mechanisms that are usually
considered to be typically formal actually have both formal and informal versions. For
instance, authority can be either legal-rational and formal, or traditional and informal; and
rules can be formal laws or informal social norms. It is even arguable that prices can be both
formal and informal, as they are sometimes publicly and formally posted, while in other cases
they are expressed by informal indicators of value such as in barter ratios, rationing quotas,
time spent in queues, etc. (Barzel, 1989). Contracts themselves, can be formal documents or
informally negotiated agreements, in the latter case still benefiting from some possibility of
legal enforcement (Fried, 1981).
If organizational mechanisms that differ in kind – such as property rights, rules, authority,
negotiations and prices – have different coordination properties, and if all of them can be
formalized into contracts and other formal documents, then the coordination properties of
those documents may also be different, depending on which mechanisms they include. To
analyze contracts and other formal documents through these additional organizational lenses
can shed new light on their nature as well as provide a way to achieve flexible formalization.
This approach requires analyzing and operationalizing the concept of formalization not as a
single variable, but as a set of distinct, albeit interrelated, variables, hence as a
multidimensional construct. In the following we are going to integrate a set of selected
studies on contract design, law design and organization design that converge in signaling that
formal documents include different components and help in defining in general terms which
they are and how they differ in terms of flexibility properties.
In empirical research on contracts, Luo (2002) argues that contractual formalization rigidity
can be avoided if the specified terms of exchange are subject to state-contingent clauses
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and/or to general principles, procedures, and guidelines. His empirical analysis of a set of
international joint-venture contracts shows that scale items measuring the degree of
specification of action obligations and those measuring the degree of specification of
contingent and procedural provisions do load on two different factors. Crocker and Masten
(1991) distinguish the contribution to flexibility provided by contractually stated procedures
for recalculating automatically the terms of exchange as information becomes available
(called ‘redetermination’ procedures), from the flexibility gained by contractually stated
rights and obligations to new ad hoc joint decision making (called ‘renegotiation’
procedures). They also correlate the presence of these procedures with the type of uncertainty
faced. The empirical investigation in that study is on contracts in the natural gas industry, and
the finding is that uncertainty reflected in price volatility does not increase the presence of
renegotiation procedures, as it can be managed by automatic redetermination procedures,
while the broader task uncertainty that stems from long contract durations does. In a detailed
content-analysis of a large sample of biotechnology alliance agreements, Lerner and Merges
(1998) observe that rather than trying to spell out “a myriad of possible world-states, and
dictating outcomes under each of many scenarios” these contracts focus on “discrete aspects
of the fundamental ownership right over the research results.”
From a legal perspective, Smith (2002) also distinguished provisions regulating the
ownership of resources from those regulating their admissible uses. ‘Proprietary’ provisions
are based on exclusion, i.e., they protect a broad range of unspecified uses of a resource from
unspecified third parties. Alternatively, the parties can delineate their rights on resources by
specifying admissible activities and uses of resources by specified actors. Both types of
contractual provisions are enforceable. However, each of them relies on different proxies
(indicators) for identifying right holding and right infringement, and these proxies entail
different measurement and verification costs. Smith observes that the proxies of a property
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right are often easier to monitor than the proxies of specific admissible activities, and
concludes that property rights are contractually more enforceable than action rights, rather
than less (differently from what most property right theorists seem to think).
Legal studies have drawn another pertinent distinction (Fon & Parisi, 2007): the distinction
between ‘general principles’ that should be applied exercising discretionary judgments, such
as ‘reasonable care’, ‘due diligence, ‘fair play’ (called ‘standards’ in this literature) and
statements specifying what actions fall outside the norm (e.g., driving faster than 55 miles per
hour) (called ‘rules’ in these contributions).
It can be noticed that those works recognize that a) there are qualitative differences among
the type of norms that can be formalized, and that b) some are more flexible than others - in
the sense that under uncertain and variable conditions formalization has dysfunctional
consequences in terms of maladaptation, beyond and on top of the costs of writing and
enforcement.
Analogous distinctions have been classic in organization design. In particular, it is well
established that the more stable and known tasks and problems are, the more actions can be
automatically guided by programmed decision making; whereas, as problems become more
uncertain, more discretion and ad hoc decision making are needed (March & Simon, 1958;
Simon, 1969). Hence, uncertainty calls for rules, procedures, responsibilities and task
descriptions that are less detailed and less action–specific (Lawrence & Lorsch, 1967).
In summary, what are the core theoretical distinctions, then, for designing flexible but
protective governance? Bringing all the above insights together it can observed that what can
really hinder and constrain action is not formalization per se, but the degree of detail or
‘action-specificity’ of rules and agreements.2 Then, the content of formal documents that
might be both adaptive and protective can be hypothesized to exhibit the following features:
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- the specification of general, rather than detailed, formal rules;
- the specification of the actors entitled to decide, rather than of the actions to be taken;
- the specification of procedures for ad hoc decision making, rather than the specification
of automatic programs and state-contingent clauses;
- the specification of rights over resources (in rem rights, i.e. rights on things, valid erga
omnes), rather than rights over specific uses and actions (do ut des and facio ut facias
obligations among specific parties).
These types of provisions, share the property of being enforceable, hence protective, while
leaving and actually guaranteeing discretion to the players, thereby providing flexibility. Let
us call the ensemble of these provisions, constitutional formalization (henceforth, CF), as it
plays a role similar to that of a constitution in a law system.
These provisions can be conceptually distinguished from provisions sharing the opposite
feature of determining action, such as descriptions of tasks, terms of exchange and programs
for contingent automatic adjustment. Being an ‘operationalization’ of the principles and
rights stated at the constitutional level, at a more applied and particular level, this component
of formal agreements may be called operational formalization (henceforth, OF). The core
ensuing conjecture is that CF is the flexible component of formal agreements and rules.
The difference can be practically illustrated with a case: the 2004 agreement between Sunesis
Pharmaceutical, Inc. and Biogen Idec for the development and commercialization of
advanced human therapeutics. This agreement can be said to be highly operational, as it
identifies 59 distinct activities, allocates resources among them, and determines their
respective start and end dates through a Gantt chart. Moreover, it provides clear criteria to be
used in the assessment of whether to bring a certain molecule to the next phase of
development or not (e.g. “inhibitory concentration < 50%”). In contrast, a contract in the very
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same field, in which the constitutional component can be said to prevail, is the 2005
agreement between Nuvelo Inc. and the pharmaceutical division of the Kirin Brewery
Company. It deals with similar issues essentially by determining the identity of the party
entitled to make decisions and by specifying the extent of such discretion.
Antecedents
The uncertainty of the activities to be regulated is the major source of the need for flexibility,
hence the main antecedent considered here. Uncertainty is used in many other studies on
formalization and contracts, but here it plays a more central role. In fact, in most other studies
on the issue, transaction specificity, as a source of opportunism potential, is considered the
core predictor of contractual complexity (sometimes moderated by prior relationship
mitigating opportunism). These two factors will be dutifully controlled for in the empirical
investigation, but they will not be the centerpiece of the theoretical explanation offered here,
for the following reasons.
Transaction cost economics is the theoretical source of the hypothesis that asset specificity is
a cause of contractual complexity. However, at a closer scrutiny, transaction cost economics
ascribes the degree of completeness and complexity of contracts more to uncertainty than to
asset specificity per se. In fact, assets can be very specific, but were it not for uncertainty it
would be possible to write complete contracts; whereas with uncertainty contracts become
incomplete, even if assets are not specific (Williamson 1975, 1979). In transaction cost
economics asset specificity is fundamental for the type of remedy to be chosen to manage
uncertainty and the contract incompleteness that comes with it, namely, for the degree of
integration of the transacting parties. As asset specificity increases under uncertainty, it is
posited that the exchange relationship evolves into a dedicated stable association of partners
and resources, and, in the limit, into a firm. Hence, when uncertainty is high, asset specificity
can be expected to possibly reinforce the constitutional, property right sharing component of
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contracts (rather than the number of contract clauses, as the costs of writing and enforcing
them raise with uncertainty). We say ‘possibly’, as, in addition, doubts have been casted on
the conjecture that asset specificity raises contractual hazards: since it generates surplus and
expands the ‘pie’ to be shared, asset specificity may actually favor integrative negotiations
rather than opportunism3. The present study contributes in solving these doubts and
inconsistencies, not only through a restyling of the dependent variable (the kind of
formalization rather than its degree) but also of the independent variables (information
complexity of various kinds as a more important predictor than asset specificity).
As to the length and intensity of past relationships, it is usually expected to lower the needs
for contractual protection. However, the reasons for this hypothesis are that past relations
increase partner-specific knowledge, hence the capacity to predict their behavior, as well as
that they raise the expectation of future relations, thereby introducing reputation effects.
Hence, we are back to uncertainty and self-enforceability issues. In addition, a usually
neglected counter-effect of prior relationships is that they should also be expected to raise the
specificity of transactions, whereby their net effect on formalization is ambiguous (if
specificity is considered a source of opportunism).
Finally, the present study restitutes prominence to a variable that used to be considered and
found to be a major predictor of formalization, but almost disappeared in the study of
contracts; namely, ‘size’. ‘Size matters’ for formalization (as highlighted in our OT literature
review) as it increases the number of variables to be taken into account and the number of
values they can assume, as well as the differences and interdependences between relevant
elements to be managed. In addition, higher stakes and hazards go along with larger size, as
size involves more substantial investments. Hence, the size of action should increase the level
of formalization, both for coordination and for protection reasons.
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In summary, if the model of formalization developed here is correct, we should find that the
contracts and the related non-contractual documents effectively formalizing the governance
of economic activities are:
(Hypothesis 1) richer in constitutional provisions, and relatively less operationally
specified, when they regulate more uncertain activities, as in the case of activities
characterized by higher research intensity, creativity and innovativeness.
(Hypothesis 2) more elaborate in both their constitutional and operational components,
when they regulate more complex activities – i.e. systems of larger size that encompass a
higher number and a greater diversity of matters and partners, and employ larger
amounts of resources.
These two conjectures about the patterns of relationships between types of uncertainty and
types of formalization have been developed irrespective of whether formalization is
contractual or non-contractual, and therefore they are expected to be valid in both cases.
Nonetheless, these two vehicles of formalization should differ in other respects. In particular,
contracts should have stronger coordination and protection functions, whereas non-
contractual agreements should have the advantages of being less costly to change and of
exposing less to legal liability. Given these countervailing factors and the dearth of prior
work on this topic, the differences between these two vehicles of formalization vis-à-vis the
aforesaid predictors will be explored empirically in the study presented next in this paper.
AN EMPIRICAL STUDY
The investigation presented here concerns the formalization of inter-organizational projects
taking place in different task environments.4 The research design is a comparative case study,
on the differences in formalization as related to three discrete clusters of industries,
intensively organized through projects: machinery engineering and construction industries
(‘machinery’ here on), creative industries and high technology industries. The data base,
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illustrated in section 4.1, was constituted between 2006 and 2007 through an assisted
questionnaire administered to firms in different locations.5 Each industry was observed in at
least two countries. This design allows controlling for possible societal effects on
formalization, often deemed to be relevant. Section 4.2 illustrates the measurement and
meaning of the independent variables – the uncertainty of industry task environment and the
complexity of the project. The operationalization of the dependent variable – formalization –
is presented in section 4.3. The findings on the existence of a constitutional and operational
component in contracts and extra-contractual documents, and their relations with industry
uncertainty and project complexity are presented in section 4.4.
The data base
The data base was constructed starting from official lists of firms in the target sectors in the
considered countries: the country’s Statistical Business Register within certain NACE codes
(Denmark), or lists of member firms provided by relevant industry associations, such as
Germany’s Verband Deutscher Maschinen und Anlagenbau (German Engineering
Federation). All firms in the lists were contacted; or extractions were made through simple
random sampling when the lists were very large.6 To reduce undercoverage of the underlying
populations the rosters of multiple business associations were used. Appendix A provides an
overview of the sampling framework and of the reassuring results of the assessment of
possible non-respondent biases.
A thick questionnaire was administered, in an assisted way, to different roles in different
industries (e.g., account managers in advertisement, CEO/owner in small high-tech firms,
project managers in machinery firms), as the persons who have the best overview of projects
have different job titles in different industries.7. Interviewees were asked to select a project
with the following features: it had been completed within the last three years, it had produced
a relevant and valuable output and had been economically viable; it had involved different
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legal entities and it had the respondent organization as one of the first three key partners.
‘Key partners’ were defined as organizations taking part in the project that ranked highest in
terms of the amount and importance of the resources they provided, and were identified by
the respondent.8 From the 2404 firms originally contacted, the 440 questionnaires in four
countries (response rate: 18.5%) with valid responses on the questions of interest constitute
the core dataset for this study (see Table 1).
------------------------------
Insert Table 1 about here
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Given the variety of the contexts covered, the research team paid special attention to the
validity of measures and decided to design questionnaire items focused on facts potentially
verifiable from other sources (e.g. budget size) and on indicators expressible in natural
measurement scales (e.g. the number prior ties among the partners) rather than on perceptual
ordinal scales that could generate artifactual covariance among self-reported measures
(Podsakoff & Organ, 1986). In any case, when applying Harman’s one-factor test to assess
the extent of common variance (Harman, 1967) we found no ‘general factor’, so that common
method biases can be excluded.9
The independent variables
That high technology industries are characterized by higher innovation and uncertainty than
the machinery industry hardly needs to be argued.10 The way uncertainty in creative
industries compares to that in machinery requires more detailed discussion. The grouping of a
number of cultural and business activities under the Creative industries banner is common
both in academic research (Jones et al 2004, Caves 2000) and in economic policy practice11.
The sectors of activities typically included under the collective Creative umbrella are
Flexible formalization
19
advertising, architecture, design, film and video, interactive leisure software, music,
television and radio. These industries account for a considerable share of Gross Value Added
in many advanced countries. In core work on the subject, Caves (2000) summarized the
structural properties that distinguish arts and entertainment from other economic sectors. The
first such property is the uncertainty faced by the producer of creative works (will high
quality results necessarily ensue from the use of good quality inputs? according to which
criteria will/can quality be judged? how can value be estimated? how much will customer be
willing to pay? to what extent and with which modifications past experience can be applied to
current projects?). Other authors, found that creative industries are among the most
innovative sectors of the economy (Müller, Rammer & Trüby, 2009). Therefore, it seems
accurate to characterize creative industries as a rather uncertain business environment. This
characterization is substantiated also by other variables in the data base that allow comparing
projects in the three focal industry groupings on a few visible attributes relating to the
uncertainty of their respective task environments. First, projects differ across the three
industries in the innovativeness of the products or services they deliver (Product
innovativeness). Second, they differ in the lack of clarity of the information concerning the
task requirements, as reflected in the percentage of activities that in the course of the project
needed to undergo revisions of sorts (Revised activities). Further, they differ in the extent to
which project activities entail uncertain means-ends relationships, and can be accomplished
only after new knowledge is generated (Innovative activities), and after methods and know-
how are exchanged among the partners (Knowledge exchange). The questionnaire items
through which these attributes of the task environment were measured are reported in
Appendix B. Figure 1 reports the scores of uncertainty items for each industry sector.
------------------------------
Insert Figure 1 about here
Flexible formalization
20
------------------------------
An interesting feature in the scores of these items is the fact that their correlation coefficients
are relatively modest, which points to the multidimensionality of uncertainty.12 The figure
reveals at a glance that the ranking of the industry sectors is consistent across the indicators,
with machinery and construction scoring lowest, and high technology scoring highest.
Almost all between-industry differences are statistically significant. Therefore, industries
themselves offer a better synthetic characterization of tasks environments with varying levels
of uncertainty than summated scales based of direct measures of project activities. As
machinery and construction sectors score lowest on uncertainty, they are used as the base-line
category and analyses are carried out with the dummies Creative and High Tech, checking for
robustness with alternative operationalizions of uncertainty, with the items for which all the
inter-sectoral differences with machinery are clearest (Innovative activities and Knowledge
exchange).
Conceptually, also the life span of the project may increase the uncertainty of activities and
the need for ad hoc adjustment (Crocker & Masten, 1991). However, it may also increase the
number of activities (and indeed project duration is considerably correlated with project size
in the sample used here). Hence, in the model we will include Project duration (logarithm of
the project’s life span in weeks) as a predictor, but we leave to the empirical analyses to
establish the exact nature of this indicator.
The number and size of the activities to be coordinated, and their diversity (both expected to
be antecedents of formalization) are measured respectively by the variables Project size
(logarithm of the total budget of the project in Euros) and Partner diversity (an ordinal
variable describing whether the partners of the project belonged to the same region, to the
same nation or to different countries).
Flexible formalization
21
Results have been controlled for the longevity of relationships and the irreplaceability of the
parties. This study measured one among several correlated aspects of the longevity of the
relation, namely the number of prior collaborations of the respondent firm with the other two
key partners (Shadow of the past). As to the partners’ irreplaceability, we calculated a
composite index based on the number of partners which could have been chosen as an
alternative to each of the key partners for the focal project (Irreplaceability). Finally, the
dummies Denmark, Italy and Silicon Valley, were introduced to control for location context
effects, with Germany providing the base case location.
The dependent variable
As to formalization, the questionnaire included three questions of similar structure, asking
respondents to assess to what extent certain matters were specified respectively into the
contract(s) regulating the project, into the non-contractual written documents employed at the
interorganizational level (exemplified in charts, procedures and job descriptions); or were
regulated through informal agreements and norms. A qualifying feature of this measurement
is that it takes into account the possibility that both formal and informal governance
mechanisms are intensively used (Poppo & Zenger, 2002) by explicitly measuring the
intensity of informal regulation, rather than assuming that an increase in formalization by
definition implies a corresponding decrease in informal regulation.
The relevant matters to which formalization is applied were identified starting from an initial
rich list of possible items produced through direct consultation of legal experts, content
analysis of actual contracts and a review of the empirical literature on contracts (published in
2006 and 2007). Then, different aspects of the same mechanism were grouped into a single
class (for example, ownership of inputs and ownership of outputs were clustered into rights
of ownership). This resulted in the following seven items: rights of ownership; decision and
Flexible formalization
22
control rights; task descriptions; duration; separation procedures; warranties and
indemnities; prices, fees and royalties.
On each matter, and separately for contractual and non-contractual documents, respondents
were asked to select the most appropriate description of the extent to which each was
specified ("Not specified ", " Specified only in terms of general principles", "Extensively but
selectively specified” and "Completely specified"). Appendix B summarizes the
operationalization of the variables.
Results
Table 2 reports descriptive statistics for each of the items measuring contractual and non-
contractual formalization. For contractual formalization the mean of most indicators is
approximated by category 3 ("Extensively but selectively specified”), whereas the means of
non-contractual formalization hover around 2.5. Informal governance has considerably lower
means (between 1.5 and 2), indicating that on average it is not used at all, or that it is used to
specify governance principles only in general terms.
------------------------------
Insert Table 2 about here
------------------------------ The first result, already apparent from these data, is therefore that formal governance is used
more intensively than informal governance and contractual formalization more intensively
than non-contractual formalization.13 Given that projects are generally used to govern
relatively complex and uncertain activities, this pattern interestingly deviates from
conventional propositions about the limits of formalization, while it squares well with the
argument that formalization does not necessarily fail altogether under complexity and
uncertainty.
Flexible formalization
23
Results on the components of formalization. The core conjecture on the twofold
nature of formalization – the existence of the two qualitatively different constitutional and
operational components - was investigated through two exploratory factor analyses (EFAs)
and two confirmatory factor analyses (CFAs) carried out separately on items of contractual
and non-contractual formalization. These analyses indicate that the items exhibit a pattern of
co-variation that is supportive of the distinction between CF and OF.
In fact, in both EFAs, right assigning and procedural items (‘rights of ownership’, ‘decision
and control rights’ and ‘separation procedures’) load on one factor, while more action-
specific ones (‘task descriptions’, ‘duration’ and ‘prices, fees and royalties’) load on a second
one. ‘Warranties and indemnities’ are conceptually more difficult to classify and their nature
remains unclear also after the EFAs, as in both analyses this item loads on both factors almost
to the same extent.14 On account of this ambiguity we drop the item from subsequent
analyses.
------------------------------
Insert Figure 2 about here
------------------------------
CFA allows us testing two measurement models based on the CF and OF distinction.15 The
resulting specification of the model, identical for both contractual and non-contractual
formalization, is represented in Figure 2. In the case of contractual formalization the model
has an acceptable goodness of fit (chi-square (7, N=440) = 28.89 (p < 0.01), SRMR = 0.061
RMSEA = 0.084, CFI = 1.00, NNFI = 1.02). The standardized factor loadings of ownership
rights, decision rights and separation procedure are 0.58, 0.67 and 0.88 respectively, and
those of tasks, duration and prices are 0.76, 0.86 and 0.81. Therefore all the indicators are
related to their factors as expected. The constructs of OF and CF have a composite reliability
Flexible formalization
24
of 0.85 and 0.76 respectively, above the commonly used threshold of 0.70. The correlation
between the two factors is substantial (0.78) but it is below the 0.80-0.85 threshold that is
frequently used as a criterion to define poor discriminant validity.
In the case of non-contractual formalization the model has a similar, or marginally better,
goodness of fit, (chi-square (7, N=440) = 25.48 (p < 0.01), SRMR = 0.062 RMSEA = 0.078,
CFI = 1.00, NNFI = 1.01). The standardized lambda coefficients of ownership rights,
decision rights and separation procedure are respectively 0.78, 0.80 and 0.88 and those of
tasks, duration and prices are 0.84, 0.91 and 0.89. Again, they are related to their factors as
expected. The composite reliability of OF and CF are 0.91 and 0.86. The correlation between
the two factors (0.86) marginally exceeds the conventional threshold. However, the two-
factor model improves the chi-square (∆ df: 1) by 19.27 with respect to a one-factor model,
so that discriminant validity is statistically supported at the 0.0001 level.16
These results justify moving forward and analyzing the antecedents of the two types of
formalization. They also justify using the latent variable scores from the CFA models to
measure CF and OF. The corresponding variables in contracts and non-contractual documents
will be designated as CFcontr, CFnon, OFcontr, and OFnon, respectively.17
Results on the antecedents of constitutional and operational formalization. The
four models that test the factors associated with formalization share the same structure and
are specified as follows:
F = 0 + 1 Creative + 2 High Tech + 3 Project size +
4 Partner diversity + 5 Project duration + Controls,
where F (Formalization) can be CF and OF, in contracts and in non-contractual document,
Controls is the vector of the control variables. As hypothesis 1) requires the comparison of
coefficients across equations, we estimate the models with multivariate multiple regression.
Flexible formalization
25
As it estimates between-equation covariance, multivariate regression makes it possible
imposing and testing constraints across-equations.
------------------------------
Insert Table 3 about here
------------------------------
Table 3 reports the correlations between the variables comprising the multivariate models.
Concerns for multicollinearity can be excluded: a variance inflation factor (VIF) analysis
gives a highest value of 2.26, well below the threshold of 10, which has been suggested in the
literature.18 Table 4 presents the results of the multivariate regressions. Models 1 and 3
incorporate only the control variables and so serve as baseline models. Models 2 and 4 also
include the five variables of interest, Creative, High tech, Project size, Partner diversity, and
Project duration. Chi-square difference tests indicate the joint significance of these covariates
in the equations of Models 2 and 4.
------------------------------
Insert Table 4 about here
------------------------------
Salient results of the analysis are the following:
- Project size increases formalization of all kinds (the coefficient of Project size is
significant at the 0.01 level or better in all models), as predicted.
- Partner diversity has a positive influence on contractual formalization (the coefficient of
Partner diversity is significant respectively at the 0.05 and 0.01 level in the first and in
the second equation of model 2), as expected.
Flexible formalization
26
- The governance of projects in Creative and High tech entails more intensely specified
constitutional contractual provisions (coefficients positive and significant in the CF
equation of Model 2, both with p < 0.05), as predicted. However, somewhat contrary to
expectations, it does not seem to reduce in absolute terms the operational components of
both contracts and non-contractual documents.
- Operational non-contractual formalization is reduced, instead, albeit only in its non
contractual component, by the time span of a project (the coefficient of Project duration
is negative and significant at p < 0.1 in model 4).
- In unreported analyses we tested the differences of coefficients across the two equations
of each model. This exercise helped clarifying two points. First, in the case of Creative
and High tech the coefficient differences are significant in both modes of formalization,
thus lending further support to the core hypothesis that uncertainty favors a greater
incidence of CF over OF. Second, the tests confirm that the same holds true of the
coefficients of Project duration, albeit only in the case of non-contractual documents.
Therefore, project duration seems to behave as an indicator and a correlate of uncertainty
rather than of size/complexity (as in the Croker and Masten’s study).
- The effects of the variables of interest hold true on top of and above the influence of
different national or regional settings. Yet, Table 4 shows that country/region dummies
generally have significant effects on formalization (with being in Germany driving
relatively more formalization than being in other contexts).
- As to other controls, we do find a negative effect of relation longevity on formalization,
but, as far as the full models are concerned, this holds true only for contractual
constitutional provisions (for the corresponding coefficient of Shadow of the past, p <
0.1).
Flexible formalization
27
- No effect of the irreplaceability of partners on formalization of any type was found
(Irreplaceability is insignificant in all models).
Table 5 provides the results of some robustness tests for both contractual and non-contractual
formalization. Models 5-8 replace sectors dummies, with two alternative indicators of
uncertainty from the set of items of the questionnaire: Innovative activities and Knowledge
exchange. The effect of these project-level variables on contractual formalization is rather
muted, as was probably to be expected given the sectoral heterogeneity of the sample.19
However, in no model we find statistically significant coefficients for these variables with
sign in the direction opposite to the theoretical claims. Although sectoral uncertainty better
predicts variations in contractual profiles, these project-level measures allow to see a negative
significant impact of task uncertainty (p<0.01), on operational formalization, at least in non-
contractual documents (models 7 and 8), as we had expected but could not confirm in the
models’ main specifications. The results for the other variables are nearly equivalent to those
in Table 4 (models 2 and 4).
------------------------------
Insert Table 5 about here
------------------------------
DISCUSSION AND CONCLUSIONS
The empirical findings on inter-organizational contracts and related documents corroborate
the core assertion that ‘flexible formalization’ is possible and indeed necessary under
uncertainty. Formalization can be flexible due to its higher order constitutional components,
consisting of rights of ownership on inputs and/or outputs, and of decision and control
procedures (including those concerning the exit from the relationship) specified into contracts
and non-contractual documents.
Flexible formalization
28
Two refinements however emerged from the analysis with respect to the hypotheses. First
constitutional governance is infused, in more uncertain sectors, especially into contracts,
rather than into non-contractual documents. This result suggests that the function of providing
both flexibility and protection under uncertainty through more intense specification of
constitutional provisions is better performed by formal contracts, i.e. the legally enforceable
component of formalization. By contrast, it is the degree of specification of non-contractual
documents which decreases under uncertainty, in particular under the type of uncertainty
brought about by the expected duration of the ‘ongoing cooperation’. Hence, the function of
providing flexibility through the reduction of the detail of operational ex-ante coordination
seems to be distinctively performed by non-contractual formalization. Taken together, the
two findings actually supports the core hypothesis on the effects of uncertainty on
formalization in its original strong version – uncertainty not only increases CF but also
decreases OF – but this pattern is visible only if contractual and non-contractual documents
are considered together.
The second hypothesis – that a good part of the observed variations in the degree of
complexity and formalization of contracts and other documents should be attributable to the
size and complexity of activities, rather than to the specificity of resources – received
straightforward support from the analysis. This supports the interpretation of transaction cost
theory discussed in the theory section, rather than the more common interpretation attributing
contractual complexity mostly to transaction specificity and opportunism problems. It can
additionally be observed that many of the studies in which such an association was found,
have actually operationalized specificity as the amount of resources invested in a relationship,
which is arguably more a proxy for size and complexity than for asset specificity. In fact, in
studies where ‘small numbers’ are measured more directly, through an index based on the
number of possible partners, as in Crocker and Masten (1991) and in the data base used in the
Flexible formalization
29
present study, a relationship between small numbers and the degree of specification of
contractual clauses is not found.
As to the other predictor usually employed in empirical research on contracts – prior
relations – it has been found here to have an uneven impact over different kinds of
formalization. This may help clarify why different studies have reached opposite conclusions
on the role of prior ties for contractual articulation. In fact, results suggest that if partners
have previously worked together on other projects, the need for protection under uncertainty
is reduced – whereby contractual CF decreases – but not the need for coordination under
complexity – as in fact the operational component of both contracts and non-contractual
formalization are insensitive to prior relations.
The framework and the results also suggest further research questions. For example, how
does the relative incidence of formal –contractual and non contractual - and informal
governance in their constitutional and operational components, vary across contexts? For
inter-firm projects the recourse to informal agreements and norms turned out to be minor with
respect to formal governance, but results on the incidence of informal governance may be
different for intra-firm governance) or even for different types of inter-firm relations (e.g.
long term buyer-seller relations). Results on location effects invite inquiry on which are the
features of institutional contexts systematically affecting the level and kind of formalization,
and in which direction do they affect it. The framework presented here would allow to go
beyond the mere assertion that there is institutional embeddedness variety, and to formulate
non ad hoc hypotheses on which institutions have which effect on contracts and
formalization. Examples of such hypotheses are that legal and societal requirements for
transparency and accountability may increase contractual formalization, while court
discretion and the constitutional richness of the law system may decrease it as less regulatory
burden would insist on any specific contractual agreement.20
Flexible formalization
30
In conclusion, the framework and the results presented here seem to have significant
conceptual and practical relevance for organizational and contractual design, and for an
appreciation of the rich organizational content of contracts. Not only the theory and results
presented here confirm that the old organizational and legal precept of being as formal as
possible, attempting to cover all conceivable contingencies, and writing clauses for every
possible action, is not a good design principle They also indicate that being informal is
definitely not the whole story and not necessarily a good recipe for managing uncertain
activities. Neither is an intermediate degree of formalization the optimal solution. The answer
to the question of how to design “living agreements for a risky world” (Orr, 2006) – a
question of increasing interest also among legal professionals – is to design contracts, and the
non-contractual documents complementing them, as organizational treatises with a
constitutional and an operational component, which should have different incidence
according to the state of uncertainty and the complexity of activities.
Flexible formalization
31
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FOOTNOTES
1 Today, electronic data storage complements and partially replaces paper documents as the repository of formalization. However, the change in the medium does not alter the fundamental functions that formalization is expected to perform. 2All this is perfectly in line with a common finding of the studies on organizational cultures: detailed, action-specific routines and precepts, even if informal, can in fact be as rigid as the formal ones and often even more so. 3 The first appearance of this argument seems to be in Croker and Masten Crocker, K. J., & Masten, S. E. 1991. Pretia Ex Machina? Prices and Process in Long-Term Contracts. Journal of Law & Economics, 34: 69-99.. 4 While, as argued, inter-firm projects are interesting to study, they do not belong to any definite ‘population’ of objects; hence, we have opted for a research design providing high internal validity and good causal leverage, rather than pointing at the external validity of results in terms of generalizability of projects features to a population. 5 The data were collected as a part of an international research project on the governance of interfirm projects in several industries and countries, including Denmark, Germany, Italy, California, Canada, France, (see Acknowledgments). Only the questionnaires from the first four locations included complete information on the variables of interest and were used here. 6 A lower bound of 10 employees per firm was applied. 7 It can be acknowledged that a documental analysis of contract themselves could have been better in some respects for the purposes of the present analysis. On the other side, it would have entailed the loss of much complementary evidence and the possibility to match a number of features of the activities with the documents intended to regulate them. 8 The dataset is composed by project collaborations between three or more organizations in 93.5% of cases and by dyadic collaborations in the remaining ones. 9 An unrotated factor analysis of the questionnaire items yielded eight factors with eigenvalue greater than one, the largest of which explained only 19 percent of total variance. 10 For example, in the US in the year 2006 the research and development (R&D) intensity (R&D expenditures as a percentage of value added) in a prototypical high technology industry such as the manufacturing of computing equipment (ISIC C30) was 33.5, versus 8.2 in machinery (ISIC C29) (Source: OECD 2011. STAN: OECD Structural Analysis Statistics (database)). 11 For example, a Creative Industries Task Force has been instituted by initiative of the Department for Culture, Media and Sport of the government of the UK since 1997. 12 Data available from the authors. 13 In paired sample tests of differences of means, every difference between an item of contractual formalization and its correspondent in non-contractual documents is statistically significant, and so are five out of seven differences between non-contractual formalization and informal governance. Data available from the authors. 14 Data available from the authors. 15 As the items are measured on ordinal scales and violate the assumption of multivariate normality required by CFA, the analysis was conducted on matrices of polychoric correlations and of asymptotic
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covariances, using a weighted least squares estimator. Polychoric correlations calculated with Lisrel (v. 8.80) are reported in Table 2. 16 In unreported analyses we tested whether the measurement models are invariant across industry clusters or are just a statistical artifact. Overall, the analyses supported the statement that the structure of both contracts and extra-contractual documents is well described by a two-factor model comprising the facets of CF and OF, for projects in machinery, high-tech and creative industries alike. 17 As a robustness check we reestimated our models of CF and OF using summated scales instead of latent variable scores. Results were substantively the same as those reported below. 18 Breusch-Pagan’s tests and residual plot analyses give mild indications of heteroskedasticity or error term distribution. Therefore we used bootstrap to produce standard errors that are valid under weaker assumptions than homoschedasticity. 19 Results were fundamentally equivalent if we subtracted from Innovative activities and Knowledge exchange their average values calculated over each sector’s subsample. 20 The latter implication for further research in Law & Economics that could be inspired by this paper, has been suggested by Francesco Parisi in a commentary at the ISNIE annual conference 2010.
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Table 1
Sample distribution by industry and country
Country Creative Machinery High tech Total
Germany 61 134 19 214 (28.50) (62.62) (8.88) (100.00) Denmark 38 21 32 91 (41.76) (23.08) (35.16) (100.00) Italy 1 79 5 85 (1.18) (92.94) (5.88) (100.00) Silicon Valley 0 0 50 50 (0.00) (0.00) (100.00) (100.00) Total 100 234 106 440 (22.73) (53.18) (24.09) (100.00)
Notes: row frequencies in parentheses
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Table 2
Indicators of formalization: descriptive statistics and polychoric correlations
Panel 1: Contractual formalization items
Mean Std.Dev. 1 2 3 4 5 6 7
1. DCF prices, fees and royalties 3.25 0.89 1
2. DCF duration 2.99 0.99 0.621 1
3. DCF warranties and indemnities 2.90 1.09 0.603 0.539 1
4. DCF tasks 2.81 0.98 0.482 0.643 0.472 1
5. DCF rights of ownership 2.76 1.17 0.381 0.253 0.447 0.239 1
6. DCF separation procedures 2.55 1.16 0.496 0.522 0.728 0.489 0.508 1
7. DCF decision and control rights 2.45 1.11 0.422 0.328 0.447 0.400 0.541 0.550 1
Panel 2: Non-contractual formalization items
Mean Std.Dev. 1 2 3 4 5 6 7
1. DEF prices, fees and royalties 2.43 1.24 1
2. DEF duration 2.48 1.15 0.757 1
3. DEF warranties and indemnities 2.19 1.21 0.862 0.760 1
4. DEF tasks 2.50 1.06 0.608 0.752 0.625 1
5. DEF rights of ownership 1.89 1.13 0.617 0.533 0.654 0.446 1
6. DEF separation procedures 1.96 1.13 0.687 0.613 0.751 0.536 0.675 1
7. DEF decision and control rights 2.04 1.07 0.525 0.534 0.595 0.582 0.686 0.642 1
Notes: N= 440. All correlations significant at the 0.01 level. “DCF”: Degree of contractual formalization; “DEF”: Degree of non-contractual formalization
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Table 3
Descriptive statistics and correlation matrix
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1. CFcontr 1 2. OFcontr 0.73 1 3. CFnon 0.39 0.29 1 4. OFnon 0.25 0.34 0.80 1 5. Creative -0.04 -0.10 -0.10 -0.11 1 6. High tech 0.02 -0.09 -0.01 -0.12 -0.31 1 7. Project size 0.29 0.28 0.22 0.20 -0.37 -0.17 1 8. Partner diversity 0.17 0.19 0.06 0.11 -0.08 -0.07 0.19 1 9. Project duration 0.14 0.14 0.12 0.07 -0.31 -0.10 0.56 0.16 1
10. Irreplaceability 0.05 0.05 0.08 0.12 -0.11 0.17 0.02 0.15 0.06 1 11. Shadow of the past -0.10 -0.09 -0.08 -0.06 0.17 -0.11 -0.05 -0.02 -0.10 -0.17 1 12. Denmark -0.08 -0.15 -0.15 -0.24 0.23 0.13 -0.12 -0.08 -0.06 -0.28 0.20 1 13. Italy -0.09 -0.02 -0.03 -0.02 -0.25 -0.21 0.13 0.09 0.11 0.00 -0.04 -0.25 114. Silicon Valley -0.03 -0.08 0.01 -0.07 -0.19 0.64 -0.24 -0.11 -0.24 0.18 -0.23 -0.18 -0.18 115. Innovative activities -0.01 -0.03 -0.03 -0.13 0.05 0.32 -0.10 0.04 0.05 0.18 -0.08 0.00 -0.07 0.22 1 16. Knowledge exchange -0.12 -0.13 -0.11 -0.23 0.16 0.21 -0.13 -0.10 0.01 0.00 0.05 0.31 -0.12 0.18 0.33 1 Mean 3.18 3.60 2.41 2.74 0.23 0.24 6.03 2.07 1.67 3.29 0.58 0.21 0.19 0.11 25.81 44.29 S.D. 1.09 0.93 1.06 1.10 0.42 0.43 1.27 0.82 0.51 0.88 1.17 0.41 0.40 0.32 23.35 31.10
Notes: N= 440
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Table 4 – Determinants of constitutional and operational formalization in contracts and non-contractual documents
Contracts Non-contractual documents (1) (2) (3) (4)
CFcontr OFcontr CFcontr OFcontr CFnon OFnon CFnon OFnon
Irreplaceability 0.030 0.012 -0.019 -0.010 0.042 0.086 0.033 0.088 (0.061) (0.052) (0.058) (0.053) (0.059) (0.061) (0.058) (0.062) Shadow of the past -0.097* -0.077* -0.085+ -0.063 -0.049 -0.034 -0.032 -0.025 (0.048) (0.037) (0.048) (0.039) (0.046) (0.041) (0.046) (0.040) Denmark -0.296* -0.399** -0.324* -0.297* -0.414** -0.737*** -0.353* -0.603*** (0.136) (0.125) (0.137) (0.133) (0.143) (0.146) (0.146) (0.155) Italy -0.383** -0.216+ -0.348* -0.271** -0.205 -0.334* -0.226+ -0.422** (0.146) (0.117) (0.140) (0.105) (0.127) (0.133) (0.135) (0.132) Silicon Valley -0.355* -0.435** -0.308 -0.198 -0.161 -0.560*** 0.015 -0.360+ (0.153) (0.142) (0.226) (0.212) (0.144) (0.147) (0.207) (0.218) Creative 0.314* -0.048 0.029 -0.231 (0.160) (0.139) (0.151) (0.158) HighTech 0.414* -0.040 0.044 -0.191 (0.162) (0.143) (0.172) (0.157) Project size 0.294*** 0.187*** 0.183*** 0.143** (0.051) (0.048) (0.048) (0.052) Partner diversity 0.164* 0.154** 0.019 0.071 (0.064) (0.057) (0.057) (0.059) Project duration -0.097 -0.102 0.005 -0.200+ (0.124) (0.128) (0.107) (0.115) Constant 3.316*** 3.781*** 1.341*** 2.548*** 2.445*** 2.761*** 1.273*** 2.140*** (0.230) (0.198) (0.407) (0.368) (0.232) (0.228) (0.377) (0.402) R-squared 0.037 0.048 0.145 0.127 0.032 0.089 0.076 0.124chi2 16.13** 27.88*** 80.96*** 68.72*** 13.67** 43.36*** 33.01*** 63.79*** ∆ chi2 54.47*** 39.00*** 21.15*** 16.62*
Notes:+ p<0.10; * p<0.05; ** p<0.01; *** p<0.001. Bootstrap standard errors in parentheses. Model specification: multivariate regression. N=440
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Table 5
Robustness controls
Contracts Non-contractual documents (5) (6) (7) (8) CFcontr OFcontr CFcontr OFcontr CFnon OFnon CFnon OFnon Irreplaceability 0.003 -0.012 0.009 -0.009 0.041 0.099 0.040 0.089 (0.058) (0.053) (0.059) (0.053) (0.061) (0.064) (0.060) (0.062) Shadow of the past -0.078 -0.063+ -0.075 -0.061 -0.032 -0.030 -0.028 -0.021 (0.050) (0.038) (0.050) (0.039) (0.046) (0.041) (0.046) (0.041) Denmark -0.183 -0.311* -0.114 -0.272* -0.331* -0.650*** -0.275+ -0.539*** (0.135) (0.127) (0.149) (0.133) (0.142) (0.147) (0.148) (0.153) Italy -0.436*** -0.258* -0.432*** -0.256* -0.235+ -0.364** -0.230+ -0.351** (0.132) (0.102) (0.131) (0.102) (0.128) (0.127) (0.127) (0.128) Silicon Valley -0.072 -0.212 -0.003 -0.178 0.068 -0.346* 0.101 -0.311+ (0.176) (0.169) (0.178) (0.170) (0.152) (0.161) (0.156) (0.165) Innovative activities 0.000 -0.000 -0.002 -0.006** (0.002) (0.002) (0.002) (0.002) Knowledge exchange -0.002 -0.001 -0.002 -0.005** (0.002) (0.002) (0.002) (0.002) Project size 0.261*** 0.192*** 0.253*** 0.189*** 0.175*** 0.152** 0.174*** 0.154** (0.049) (0.045) (0.049) (0.044) (0.044) (0.049) (0.045) (0.049) Partner diversity 0.162* 0.154** 0.158* 0.151** 0.021 0.080 0.014 0.062 (0.063) (0.056) (0.064) (0.056) (0.056) (0.058) (0.056) (0.058) Project duration -0.119 -0.096 -0.091 -0.083 0.016 -0.133 0.026 -0.129 (0.128) (0.131) (0.125) (0.127) (0.104) (0.114) (0.105) (0.115) Constant 1.628*** 2.500*** 1.706*** 2.541*** 1.317*** 1.965*** 1.366*** 2.048*** (0.369) (0.335) (0.378) (0.346) (0.342) (0.376) (0.348) (0.384) R-squared 0.130 0.126 0.134 0.128 0.077 0.131 0.079 0.133 chi2 62.81*** 69.56*** 80.20*** 75.25*** 33.27*** 72.21*** 34.28*** 67.20*** Notes:+ p<0.10; * p<0.05; ** p<0.01; *** p<0.001. Bootstrap standard errors in parentheses. Model specification: multivariate regression. In every model N=440.
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Figure 1
Measures of uncertainty: Means by industrial sector
0 20 40 60Mean
Knowledge exchange
Innovative activities
Revised activities
Product innovativeness
MC
HT
MC
HT
MC
HT
MC
HT
Notes: HT: 'High tech'; C: 'Creative'; M: 'Machinery'
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Figure 2
Measurement model of formalization
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Appendix A
Industry groupings and sampling frame
Industry groupings Sampling frame Creative Events (fairs, exhibitions, congresses; operation of arts facilities) Motion pictures Business-to-business design Games Advertising Publishing Recorded music
Denmark: registered enterprises within NACE/DB03 industry codes 221110, 365000, 744010, 744090, 921100, 923110, 923200 Germany: list obtained from various industry associations (Federal Association of German Galleries, the Association of Concert Agencies, etc.), industry rankings (e.g.: Werben & Verkaufen ranking of major advertising companies) and prior academic research (e.g. DFG-Project “Production in Projects” – Bonn U.)
Machinery Machinery and equipment Engineering activities Construction
Germany: list of member firms of the German Engineering Federation; Italy: list of member firms of the following industry associations: ANIMP, OICE, UCIMU, APRI, AIAD, UCIMA, UCOMESA
High technology Semiconductors Computer/Communication hardware Innovation services Biomedical Electronic components Software consultancy and supply
Denmark: registered enterprises within NACE/DB03 industry codes 722100 and 722200 Germany: list from prior academic research project (DFG-Project “Production in Projects” - Bonn University) California: list of firms from the Silicon Valley Venture Capitalists Association
To assess potential non-response bias, tests for differences between early and late respondents were run, on the assumption that the latter are more similar than the former to non respondents (Armstrong & Overton, 1977). The tests were implemented using “time to respond” as a regression variable. In particular, this variable was used as a predictor of project characteristics such as budget size, the number of partners or the number of employees, and firm characteristics such as turnover. In each of these analyses the coefficient of time to response was not significant.
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Appendix B
Operationalization of variables and indicators
Variable Operationalization
Degree of contractual formalization (DCF)
DCF rights of ownership Q.: How were the following matters regulated in written, legal, enforceable contracts between the three key partners? 1 = Not specified; 2= General principles specified; 3 = Extensive specification of rights and obligations under conceivable specified conditions; 4 = Complete specification of rights and obligations so that they hold under any condition
DCF decision and control rights DCF tasks DCF duration DCF separation procedures DCF warranties and indemnities DCF prices, fees and royalties
Degree of non-contractual formalization (DEC)
DEC rights of ownership Q.: To what extent were the following matters regulated by written internal charts, procedures and job descriptions (e.g. of the type used in internal organization) in the relation between the three key partners? 1 = Not at all; 2= General principles 3 = Extensively; 4 = Completely
DEC decision and control rights DEC tasks DEC duration DEC separation procedures DEC warranties and indemnities DEC prices, fees and royalties
Explanatory variables
Project size Log10 of project budget in Euros Project duration Log10 of project duration in weeks Partner diversity Whether parties were from same region (1), same country (2) or
different countries (3) Irreplaceability Max (I1, I2, I3), where Ii = number of organizations (on 4 point
scale, in decreasing order of numerosity) which could have been chosen for the focal project in alternative to key partner i.
Shadow of the past Log10(N * (C2 + C3)), where C2 and C3 = number of prior collaborations of respondent firm respectively with key partner 2 and 3; N = 2 if number of key partners = 2; N = 1, otherwise
Product innovativeness *
The product or service developed in the project was:
a variation of an existing product or service (1) a new generation of an existing product or service line (2) a new product or service line for the partners (3) a new-to-the-industry product or service (4) a new-to-the-world product or service (5)
Revised activities Please indicate the percentage of activities of the whole project for which it was necessary to revise when, how or which activities should be carried out: ____%
Innovative activities Please indicate the percentage of activities of the whole project for which it was necessary to generate new knowledge (e.g., new approaches, new analytic schemes): ____%
Knowledge exchange Please indicate the percentage of activities of the whole project for which it was necessary to exchange knowledge (e.g. methods, know-how) among partners: ____%
* Treated as a [1-5] scale and rescaled on [0,100] range for comparability with other items