survival of businesses using collaborative relationships to commercialize complex goods

Strategic Management Journal, Vol. 17, 169-1 95 (1 996)

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SURVIVAL OF BUSINESSES USING COLLABORATIVE RELATIONSHIPS TO COMMERCIALIZE COMPLEX GOODS WILL MfTCHELL School of Business Administration, University of Michigan, Ann Arbor, Michigan, U.S.A.

KULWANT SINGH Department of Business Policy, National University of Singapore, Singapore

Authors with many theoretical and managerial perspectives argue that businesses commercializing technologically complex goods benefit when they collaborate closely with other businesses. Collaboration is viewed as a means for businesses to overcome competency limitations and to achieve the close configuration of components required for complex goods. We predict that collaborative relationships ofen assist businesses to produce complex goods, but that the relationships might also cause problems for the collaborating businesses. We find that firms using development-oriented and marketing-oriented collaborative relationships in the hospital sofhvare systems industry are less likely to shut down than businesses that follow independent approaches when the environment changes gradually, but businesses using collaborative relationships are sometimes susceptible to being acquired by other firms. Following a sudden environmental shock, businesses with collaborative relationships for activities central to the shock became more likely to shut down, while businesses with collaborative relationships for activities outside the focus of the shock became more likely to survive. The study critically evaluates and tests the widely stated but little-tested argument that interfirm collaboration is usually beneficial. The results address the issue of whether organizational choices affect comparative business performance.

This paper investigates the survival of businesses that use collaborative relationships with other firms to commercialize complex goods. A growing literature has identified many benefits of interfirm collaboration. Several recent studies argue that businesses that collaborate closely with other organizations in order to develop and market complex goods will be more successful than businesses that operate independently (Jorde and Teece, 1990 Langlois and Robertson, 1992;

Key words: ness survival

interfirm collaboration; complexity; busi-

Teece, 1992). At the same time, several authors have noted that collaborative interorganizational relationships might cause problems for the coop- erating businesses, including lost proprietary information, organizational disruption, and adjustment difficulties (e.g., Weick, 1979; Aldrich and Whetten, 1981; Miner, Amburgey, and Steams, 1990; Williamson, 1991a; Miles and Snow, 1992). A small literature has found only weak empirical relationships between collaborative activity and corporate financial performance (Berg, Duncan, and Friedman, 1982; Balakrishnan and Koza, 1993; Hagedoorn and Schakenraad, 1994). However, corporate-level analyses may

CCC 0143-2095/96/030169-27 0 1996 by John Wiley & Sons, Ltd.

Received I I April I994 Final revision received 15 May 1995

170 W. Mitchell and K. Singh

conceal underlying business unit performance. Moreover, studies of profitability are constrained to studies of surviving firms, while collaborative activity may influence the ability of a business to survive. We are aware of no study comparing the relative impact of the benefits and problems of collaboration on the survival of businesses commercializing complex goods.

This study critically evaluates and tests the argument that interfirm collaboration is usually beneficial. We first identify the relationship between the likelihood that a business exited the industry in any given year and whether the business had formed collaborative relationships with other businesses before that year. We then study the impact of a sudden major environmental change on the survival of businesses using collaborative relationships and independent approaches at the time the change occurred. We argue that firms with collaborative agreements may find it difficult to respond to an environmental shock that directly affects focal collaborative activities, while firms using collaborative relationships for activities outside the focus of the shock are likely to respond effectively. The study also addresses the issue of whether organizationai choices affect comparative business performance. In addition, the study has implications for the management of collaboration strategy. The empirical analysis examines the survival of 973 businesses that commercialized applications software in the U.S. hospital software systems industry, differentiating between development-oriented and marketing-oriented collaborative relationships used in the industry between 1961 and 1991.

For clarity, we will define several terms here. By collaboration, we mean cooperative agreements between legally separable organizations that do not involve establishing separate organizations (Masten, 1988; Williamson, 1991 a). By commercialization, we mean the process of acquiring ideas, augmenting them with complementary knowledge, developing and manufacturing saleable goods, and selling the goods in a market. A business is a commercial unit operating in an industry, while a firm is the legal entity that owns the business. A firm might consist of a single business or of several businesses that operate in several industries. Business survival is equivalent to firm survival for single-business firms, but finns that operate in several industries often discontinue businesses while continuing to operate as corporations.

BACKGROUND

In this section, we first discuss complex goods and describe the forms of organization that businesses might use to commercialize them. We next outline the advantages and disadvantages of independent and collaborative approaches for commercializing complex goods. We then post hypotheses concerning how collaborative relationships will affect business survival in the context of constraints on managerial action.

Independent and collaborative approaches to commercializing complex goods

We define a complex good as an applied system with components that have multiple interactions and constitute a nondecomposable whole (Simon, 1969; Huberman and Hogg, 1986; Singh, 1993).' The systemic characteristic means that a complex good consists of more elemental units, so that overall performance depends on component performance. The characteristic of multiple interactions means that the components require close configuration for reliable performance (Perrow, 1984; Henderson and Clark, 1990; Demchak, 1992). As the number of interactions among the components increases, what Simon ( 1969) calls nonsimple relationships underlying system performance often emerge even when each interaction is itself simple. The network of interactions makes complex goods nondecomposable, such that complex goods cannot be separated into components without degrading capabilities (Scuricini, 1988; Holland and Miller, 1991).

Businesses commercializing complex goods face two significant difficulties-. First, the firms must develop and maintain the ability to produce and coordinate the many dissimilar components that complex technologies typically comprise. Maintaining competencies in a broad range of

' There is no generally accepted definition of technology, which stems from the pervasive role that technology plays throughout social and economic activities and the consequent need for people in many fields and situations to address technological issues. Definitions range from narrow product- oriented specifications to broader views of technology as the processes by which knowledge is used to solve problems (e.g., Bozeman and Link, 1983; Friar and Horwitch, 1985: 144; Dussauge, Hart, and Ramanantsoa, 1992). We take the latter approach, which recognizes the important role that organizations play in creating new goods (Schumpeter, 1942; Nelson and Winter, 1982: 60-65).

Survival of Collaborating Businesses 17 1

technologies is a significant challenge in all but the most static of environments. Second, the firms face high organizational and financial costs from having to develop structures that incorporate as much complexity as the processes they manage (Ashby, 1960). Maintaining a high degree of variety in organizational structures requires the introduction of costly integrative mechanisms (Bums and Stalker, 1961; Lawrence and Lorsch, 1967; Roberts and Gargano, 1990). Although businesses commercializing low-complexity goods also face the challenges of maintaining competencies and efficient organizational structures, the challenges and costs of doing so are particularly high in cases involving complex goods.

Businesses commercializing complex goods can attempt to overcome the difficulties by operating independently or by relying on collaborative relationships with other organizations to cany out business activities. A business with an independent approach carries out some development, production, and marketing functions itself and acquires other components through hands-off relationships with other organizations. In contrast with independent approaches, some businesses collaborate closely with other firms to carry out some commercialization functions, using a vast and dynamic range of collaboration methods (Richardson, 1972; Contractor and Lorange, 1988; Oliver, 1990; Hamel, 1991; Teece, 1992). Inde- pendent and collaborative approaches create several general advantages and problems for the commercialization of complex goods.

Independent approaches allow a business to avoid dependence on other firms. Many of the components required to commercialize a complex good either require transaction-specific investment or become transaction specific over time, because the function of the good is not easily reproducible with other combinations of components. Because the utility and value of the goods depend on the quality and cost of nonsubstitutable components, businesses risk serious quality and cost problems if they rely on other organizations (Williamson, 1975; Montev- erde and Teece, 1982a). Langlois (1988) argues that businesses will often develop and produce specialized components of technologically complex goods internally in order to achieve close configuration and efficient monitoring. Other, more general components, can then be obtained through hands- off relationships with other firms.

Despite the advantages of independence, businesses that take independent approaches to commercializing complex goods risk achieving inferior cost and quality from internal activities and realizing inferior coordination with external suppliers. Complex goods usually draw from many different knowledge bases (Arora and Gam- bardella, 1990; Badaracco, 1991; Rosenberg, 1982; Dosi, 1988: 1126). A business might attempt to carry out all specialized activities internally, but few businesses will be able to obtain leading-edge positions in all relevant areas of knowledge (Penrose, 1959; Abernathy and Clark, 1985; Nelson, 1991). In addition, many components enjoy volume economies that limit the number of businesses that can produce them at reasonable cost. Businesses that carry out all specialized activities internally often will achieve poorer quality or higher cost for some components than if the components were acquired from other firms. If, instead, a business acquires specialized components via hands-off relationships with other firms, the business is likely to face coordination problems stemming from the interdependence between the components and the overall system (Monteverde and Teece, 1982b).

Collaborative approaches offer alternatives to independent commercialization of complex goods. Cooperative relationships may achieve more effective information transfer than transactions that rely on hands-off relationships (Chandler, 1962; Monteverde and Teece, 1982b; Itami, 1987). Jorde and Teece (1990) and Teece (1992) note that collaboration is particularly attractive when key development and marketing know-how is dispersed among different organizations. Col- laborative relationships offer opportunities for autonomous businesses to learn about each other’s capabilities (Ohmae, 1982; Harrigan, 1988; Hari- anto and Pennings, 1990; Fichman and Levinthal, 1991: 453; Heide and Miner, 1992; Hamel, 1991; Balakrishnan and Koza, 1993), exchange technical information (Mitchell and Singh, 1992), share resources (Thompson, 1967), and coordinate the use of each other’s capabilities (Starkweather, 1981; Mariti and Smiley, 1983; Kogut, 1988; Borys and Jemison, 1989; Powell, 1990). Granov- etter (1985) and Williamson (1985: 120-123) argue that collaborative relationships involve greater trust and less opportunism than market relationships. Williamson (1991a) notes that hybrid forms of organization allow managers to enforce more


effective coordination and control than through market relationships, while retaining higher-powered incentives than integrated businesses. Retaining autonomy also provides each business with greater flexibility to adapt to the contingencies of its own environment (Weick, 1982). In general, collaborative relationships help autonomous businesses gain effective access to knowledge held by other organizations and achieve close configuration of the components of complex goods, while also maintaining and improving their skills in their respect- ive specializations.

A competing argument stresses that collaborative relationships sometimes cause serious problems for the collaborating firms, including lost information, organizational disruption, and adaptation difficulties. Several authors have noted that collaborating businesses risk losing critical proprietary information to their partners (e.g., Hamel, Doz, and Prahalad, 1989; Hamel, 1991; Jorde and Teece, 1990). The business press and other empirical descriptions of collaborative relationships also are replete with sto- ries of organizational disruptions that arise as partners attempt to coordinate their activities (e.g., Fri- edland, 1993). Adaptation difficulties may arise from high adjustment costs and the presence of interorganizational routines. Establishing and renewing cooperative agreements is often costly (Coase, 1937, 1960; Akerloff, 1970), while businesses also frequently develop routines that span organizational boundaries as they learn to collaborate (Fombrun, 1988). Interdependence arising from the interorganizational routines often makes it difficult for a single partner to act independently (Simon, 1969; Laumann, Galaskiewicz, and Marsden, 1978: 4 6 1 4 2 ; Weick, 1979: 185-187; Williamson, 1 9 9 1 ~ 291).

Many of the advantages and difficulties of collaboration also apply to low-complexity cases. The issues arise particularly strongly in the commercialization of complex goods, however, owing to the need for businesses to maintain multiple competencies and complex organizational structures. Therefore, our theoretical and empirical focus in this study is directed to the context of complex goods. Future studies could fruitfully extend the analysis to other contexts.

Effects of collaboration

Our first question is whether the problems or the benefits of collaboration for commercializing

complex goods usually dominate when compared to independent approaches. Underlying this question is the issue of whether organizational choices such as the creation of collaborative relationships can affect comparative business performance. In a world of unconstrained choices, asking whether independent and collaborative approaches will influence business success would have little rel- evance. Managers are unlikely to follow inferior commercialization approaches knowingly and, instead, will attempt to select forms of organization that suit at least the conditions that exist when the choices are made (Masten, 1993). If unconstrained organizational choice is the domi- nant trend in business practice, then the use of a collaborative or independent approach to commercializing complex goods will have little or no overall relationship with the success or failure of one business relative to another. Firms that would benefit from collaborating will do so and firms that would not benefit will not collaborate, so that differences in business performance would result from differences in internal firm capabilities and external competitive environments rather than from choices of organizational boundaries.

In practice, though, managers face substantial constraints that affect whether they create collaborative relationships. Businesses that would benefit from collaborative relationships sometimes are not able to find appropriate partners. Organiza- tional practices and policies sometimes keep managers from undertaking actions that would be beneficial in particular business situations (Nelson and Winter, 1982; Hannan and Freeman, 1989). Moreover, organizational decisions often take place in rapidly changing environments and it is sometimes not possible to determine optimum organizational modes in the available time (Williamson, 1991b; Amit and Schoemaker, 1993). If constraints are common, then organizational choices might affect the success of one business relative to its competitors.

Constraints on managerial action are unlikely to lead to a random distribution of inferior choices regarding collaboration. Constraints are more likely to interfere with firms’ abilities to form beneficial collaborative relationships than to cause them to form relationships with obviously inappropriate partners? Simply given the frequency

2Consider a simple environment in which there are three types of firms: (i) obviously appropriate partners, (ii) obviously

Survival of Collaborating Businesses 173

with which businesses enter into collaborative relationships, the benefits of collaboration usually will exceed the problems. If some businesses are constrained from entering into desirable collaborative relationships, then businesses that do form relationships will tend to achieve superior performance. This leads to our first hypothesis.

Hypothesis I : Businesses that have used collaborative relationships to commercialize complex goods will be more likely to survive thun businesses in the same industry that take independent commercialization approaches.

The hypotheses frame business success in terms of the likelihood that a business will survive in any given year. Empirically, we distinguish between firms that exit an industry by shutting down their businesses (business dissolution) and those that exit by selling their businesses to other firms (business divestiture). The predictions apply most directly to business dissolution, which is an unequivocal sign of business failure. Business divestiture is a more ambiguous event. Divestiture sometimes occurs when at least partially successful businesses are purchased by firms that are expanding in an industry, especially by firms that have stronger supporting commercialization assets than those of the target business (Mitchell and Singh, 1993). Founders of new firms sometimes hope to be acquired, while shareholders sometimes welcome business divestiture if the buyer pays a premium. Nonetheless, although some businesses are sold because they are successful, many others are sold because they have performed poorly under the existing ownership. In either case, whether or not a divested business was successful, understanding influences on the

inappropriate partners, and (iii) firms with unknown partnership attributes. Rational managers will form relationships with the obviously appropriate partners and some of the unknown firms, but with none of the obviously inappropriate partners. In almost all cases, the result will be a larger number of relationships formed with appropriate partners than with inappropriate partners. There will be equal or greater chance of choosing an inappropriate partner only if these is extreme ambiguity about partnership attributes, such that the first and second groups of firms are empty sets, or if most firms are inappropriate partners. In practice, managers invariably either have some preexisting knowledge of other firms or are able to carry out enough analysis of potential partners that the extreme ambiguity case is rare or nonexistent, while managers simply will avoid collaboration if almost all potential partners are inappropriate.

likelihood that a business will be sold is a key issue for people associated with the business and is a central aspect of the competitive evolution of an industry (Dertouzous, Lester, and Solow, 1989; Mitchell, 1994). Therefore, both business dissolution and divestiture are important outcomes.

We will also investigate whether the number of collaborative relationships that a business uses influences its chances of survival. Businesses might become more likely to survive as the number of collaborative relationships increases, especially if multiple relationships provide businesses with access to a broader set of skills. Conversely, any problems associated with collaborative relationships might be exacerbated if a business creates relationships with many organizations. Disruptions caused when a business inuo- duces new systems to manage new collaborative agreements are likely to recur with each new partner, while the business will also be vulnerable to losing information to each new partner. The introduction of new systems and routines might also increase organizational complexity, thereby raising costs (Burns and Stalker, 1961; Lawrence and Lorsch, 1967; Perrow, 1984). Increasing the number of relationships will also require that more extensive coordination systems and interorganizational routines be established, creating further constraints on organizational adaptability. Previous studies of organizational mortality have found that dissolution rates sometimes increase with the number of changes made to such organ- ational systems (e.g., Delacroix and Swaminathan, 1991; Miner et al., 1990). Because of the opposing influences we will examine the impact of the number of relationships as a research question.

We will control for business size, breadth of the product line, and prior performance when examining the impact of collaboration. Large businesses and those offering many products are likely to form more collaborative relationships than small businesses with narrow product lines. In addition, any observed associations between collaboration and survival might result from the performance of the business before it formed a collaborative relationship rather than from the collaboration. Weak businesses might be the most likely to form collaborative relationships, in which case any observed association between collaboration and exit would stem from the weakness rather than from the collaboration. Alternatively,


strong businesses might tend to form collaborative relationships because the strong firms are particularly desirable partners, in which case an association between collaboration and survival would stem from the prior strength. We will use the market share of the business relative to the industry average market share at the time the business entered its first collaborative relationship to address the prior performance issue.

Environmental shocks

Although we predict that collaboration usually will help businesses survive, we expect that the problems of collaboration will overwhelm the benefits in some circumstances. The remaining hypotheses address the implications of environmental shocks, which we define as sudden and substantial changes in technology or market segmentation. When a shock occurs, many firms in the affected industry need to develop new skills that are suited to the new environment. Many firms find it difficult to develop new skills quickly following an environmental change (Cooper and Schendel, 1976; Nelson and Winter, 1982; Tush- man and Anderson, 1986). The commercialization approach that a firm is following when the shock occurs, though, may influence the speed and effectiveness of its response. The effect of a shock on firms with collaborative agreements is likely to depend on how the shock affects the commercialization activities that are involved in the relationships.

Firms with collaborative approaches may find it difficult to respond to an environmental shock that directly affects the activities that are the focus of the collaboration. For instance, firms using development-oriented collaborative relationships may be slow to respond to a shock affecting technical skills, while firms with marketing-oriented relationships may find it difficult to respond to a shock that changes market segmentation.

When firms begin collaborative relationships, they enter into explicit or implicit agreements that attempt to account for the partners’ skills, current technical and market conditions, and expected future conditions (Williamson, 1991a). Over time, the collaborating firms then often develop interorganizational routines that are suited to their skills and the conditions that occur in practice (Aldrich and Whetten, 1981; Levinthal and Fichman, 1988). If conditions change sud-

denly, some collaborating firms will find that their agreements, routines, and partners’ skills continue to be suited to the new environment, either because they correctly anticipated the change or were fortunate. In many cases, though, the agreements, interorganizational routines, and partners’ skills suddenly become obsolete (Miles and Snow, 1992). In Tushman and Anderson’s (1 986) terms, the shock will destroy competencies that were the purpose of some collaborative agreements. Yet collaborating firms may find it difficult to revise agreements, alter interorganizational routines, or find new partners with appropriate skills quickly. David and Bunn (1990) and Langlois and Robertson (1992) argue that firms sometimes avoid switching to new partners with skills that are suited to the new environment owing to the cost of changing interorganizational systems. Williamson (1991a: 292) argues that partners may no longer have common interests following a major change and may defect from the spirit of the agreement. Even if their interests remain aligned in the new environment, the loose ties among collaborating organizations can hinder timely joint response in changing interorganizational routines (Ashby, 1960; Weick, 1982). Once established, a collaborative relationship is thus a sticky asset that commits a firm to a relatively immobile strategy.

The adjustment difficulties suggest that collaborative commercialization of complex goods is strongly influenced by the state of the environment. The very process of designing and maintaining a collaborative relationship to suit a particular context limits the effectiveness of the collaboration in a changed environment. Therefore, collaboration is more likely to assist businesses survive if the environment that fostered the relationship persists than if it changes radically. Because of the adjustment difficulties, businesses with collaborative agreements for activities that are directly affected by an environmental shock are likely to face increased risk of failure following the shock.

Hypothesis 2: Businesses that have used collaborative relationships for activities central to an environmental shock will become less likely to survive afer the shock.

By contrast, firms that are using collaborative relationships for activities outside the focus of the environmental shock are more likely to respond


effectively. Firms with collaborative relationships that are outside the focus of the shock may be able to draw both on their own understanding of the changes and their partners’ evaluation of the changes in order to respond to opportunities created by the shock. For instance, firms with development-oriented relationships may be particularly able to respond to a shock that creates new market segments while leaving technical skills largely unchanged. Similarly, firms with marketing-oriented relationships may respond well to a shock that requires major technical changes but causes little change in market segmentation. Such firms will have wider access than many competitors to information needed to adjust to the shock, while also having independent control of the adjustment process.

Hypothesis 3: Businesses that have used collaborative relationships for activities outside the focus of an environmental shock will become more likely to survive after the shock.

Firms that are following independent commercialization approaches also might gain advantages when an environmental shock occurs. Changing an internal system or switching to a new supplier with a hands-off relationship tends to be quicker than negotiating changes in a collaborative relationship (Williamson, 1991a: 291). We expect independent approaches to be inferior to collaborative approaches prior to an environmental shock, yet independence might allow greater responsiveness following the shock. The improved responsiveness might simply counter any inherent disadvantage of independent approaches or could create superiority.

Hypothesis 4: Businesses using independent approaches to commercialize complex goods when an environmental shock occurs will become more likely to survive afrer the shock.

The preceding predictions suggest that the collaborative commercialization of complex goods is more dependent than independent approaches on the state of the environment. The conflicting influences of environmental shocks require managers of businesses commercializing complex goods to exercise judgment and thereby will cause rational managers- to make different choices concerning collaborative activity. On the one hand,

environments in which collaboration offers the greatest potential benefits are also environments that may well change unpredictably and thus disadvantage some firms with collaborative relationships. In light of this conflict, managers who believe future environmental change to be particularly likely may choose to operate independently even though collaboration would offer immediate advantages. By contrast, businesses with managers who place lower expectation on environmental shocks will be more likely to form collaborative relationships. These firms will tend to realize the immediate benefits of collaboration, while being subject to the disadvantages caused by unexpected environment shocks that affect the focus of the collaboration.

We will treat the impact of a shock as a long- lasting effect on business survival. In practice, the influences eventually will be likely to decline but our empirical study extends for only 9 years after the shock occurred, which is short enough that the impact of the shock would be felt at the end of the study. We considered defining decay functions for the shock in order to investigate the extent to which any positive or negative impact of collaboration during a shock would decline over time, but found that we could not segment our data more finely and still obtain meaningful interpretation. This issue provides an important avenue for future research.

The closest prior empirical analyses of these predictions are found in the organizational ecology literature. Baum and Oliver (1991) found that Toronto child care facilities with institutional linkages to government and community organizations had lower mortality rates. Miner et al. (1990) and Amburgey, Kelly, and Bamett (1993) showed that Finnish newspapers that were affiliated with political parties between 1771 and 1963 had lower dissolution rates than other newspapers. The Finnish results found little difference in the dissolution rates experienced by party-affiliated and independent newspapers following environmental shocks, finding instead an overall decrease in the dissolution rate. In the ecological studies, however, social legitimacy rather than technical and market capabilities provided the principal value of collaboration. The adaptation issues might arise more sharply among commercial collaborations than in cases of social affiliations, especially if the market demands of an industry change more rapidly than the social environment.


TME HOSPITAL SOFTWARE SYSTEMS INDUSTRY

Table 1. Hospital software system product classes (first year sold; number of businesses that offered products by 1991)

The U.S. hospital software systems industry com- prises firms that develop applications software systems designed to be used for administrative and clinical purposes in community hospitals in the U.S.A. Community hospitals include all ‘non- federal short-term general and other special hospitals, excluding hospital units of institutions, whose facilities and services are available to the public’ (American Medical Association, 1991 : xxiii). The industry definition includes firms that design systems specifically for use in community hospitals. The industry definition excludes software businesses that develop general-purpose applications such as word processing and spread- sheet software. The definition also excludes firms that only develop software for nonhospital medical organizations such as group practices, long- term care facilities, and psychiatric hospitals. The challenges that firms face in developing systems for hospitals differ significantly from developing systems for general-purpose use and for nonhospital medical institutions. Hospital software is com- monly treated as a distinct vertical market in business practice. In sensitivity analysis, we included dummy variables denoting whether businesses also sold nonhospital medical software and nonmedical software, finding no material change in the reported results.

Computers used in research programs were first introduced in hospitals in the late 1950s. The first recorded entry of a hospital software system business occurred in 1961, when systems to auto- mate patient management and financial operations became available. Commercial computer-based information systems used for clinically oriented laboratory, radiology, and other applications were introduced in the mid 1960s. Table 1 briefly describes the 13 hospital software systems product classes included in the study, and reports the introduction dates and the number of businesses that have commercialized systems in each class.

The data for this study comprise 973 businesses that commercialized software systems for Amer- ican hospitals from 1961 to 1991, which is close to the population of businesses that offered commercial applications software systems. The firms include computer hardware manufacturers that also developed software systems, and businesses

1.

2.

3.

4.

5 .

6.

7.

8.

9. 10.

11. 12.

13.

Accounting, business, and finance ( 196 1 ; 49 1): Financial and business office operations Patient management (1961; 258): Patient admissions, discharge, transfer, and scheduling Materials management (1963; 170): Inventory and purchasing management Clinical laboratory (1 964; 294): Laboratory department management and test result reporting Pharmacy ( 1965; 2 12): Inpatient and outpatient pharmacy management Radiology (1965; 148): Radiology department management; Picture archiving and communications systems Nursing (1965; 125): Nursing department management Other administrative (1 966; 175): Miscellaneous administration Blood bank (1967; 34): Blood bank management Patient care (1968; 218): Medical records management Bedside (1969; 37): Point-of-care management Operating room (1969; 82): Operating room management Dietary (1971; 57): Dietary management and kitchen operations

that developed only software. Almost all businesses in the industry were based in the U.S.A. The data were collected through an extensive search of the business press, corporate reports, government publications, and other public sources (a list of sources is available on request). In most cases we were able to confirm from corporate reports, business histories, and discussions with people familiar with the industry that the first year of recorded participation was actually the first active year in the industry. In the remaining cases (about 14%) we estimated the year of entry into the industry based on the first year the business appeared in an industry report, which is the usual procedure in studies of organizational survival. We aggregated the data at the national level. Many of the firms in the sample have sold systems throughout the U.S.A. Others have offered systems only regionally but are affected by national changes in technology and health care payment policies. Unquestionably, variation in state-based regulation, local competition, and local embeddedness will affect the context in which the firms operate and will have additional regional impact (e.g., Kogut, Shan, and Walker,


1992), but all firms in this industry will be affected by the change in national health policy.

Figure 1 depicts annual sales ($ million deflated by the 1982 Producer Price Index) and number of participants in the industry. After a period of slow diffusion during the 1960s, hospital software systems sales rose rapidly following the creation of the Medicare health insurance program in 1966. The program indirectly under- wrote many hospital capital expenditures and gave hospital administrators incentives to record costs so that the hospital could obtain reimbursement from Medicare and other third-party payers. Administrative software systems began to be employed widely during the 1970s and almost all hospitals employed administrative systems by 1979. Administrative systems became broader reaching and more sophisticated during the 1980s. Software systems used in clinical departments of hospitals began to receive widespread attention during the late 1970s. Most hospitals now employ computer-based information systems in major clinical departments, although only about half of all community hospitals had adopted comprehen- sive clinically oriented software systems by 1991 (Collen, 1991).

Figure 1 shows that the number of businesses in the industry closely tracks sales trends. The number of participants tended to increase shortly

before high sales growth took place, perhaps as businesses anticipated that hospitals were about to increase their software system purchases. The number of businesses tended to decrease during periods of flat or declining sales. The close relationship between participation and market size trends over the 31-year period suggests that volume economies have not been a major factor in the industry, which allows us to focus on the association between forms of organization and business survival.

The hospital software systems industry is suited to the study because the systems are complex, an environmental shock occurred during the study period, and businesses have used both independent and collaborative forms of organization to commercialize the systems. Hospital software systems are prime examples of complex goods because they are systemic, consist of multiple modules that interact in multiple nonsimple ways, and typically are organized hierarchically in many subsystems (J. L. Johnson Associates, 1975; U.S. Congress Office of Technology Assessment, 1977; Malvey, 1981; Predicasts, 1982; Frost and Sullivan, 1982). The need to integrate data com- prehensively in real time from multiple modules within and across departments make hospital software systems highly nondecomposable (Minard, 199 1; Aller, 1992). Although some

60 65 70 75 80 85 90 95 Year

- A - Collaborative businesses - All businesses

Figure 1.

- +- Industry sales

Hospital software systems businesses and sales, 1961-91


forms of hospital software systems are more com- plicated than others, all systems entail a substantial degree of complexity owing to the complexity of the hospital environment, the high human costs of system failure, frequent change in medical and computer technologies, and extensive external regulation and monitoring.

Clinical laboratory information systems provide an example of the complexity of hospital software systems. A laboratory information system encompasses interfaces among systems used by physicians and nurses, laboratory personnel, and hospital administrators. A physician delivering care in a patient care unit places an order for a laboratory test to laboratory personnel, who enter the order into the clinical laboratory information system. Technologists working in the individual clinical laboratories then perform the ordered test. The test result is subsequently entered into the laboratory information system and made access- ible to the test-ordering physician, either through the medium or a hard copy report or via the electronic test archive. In addition, infomation concerning the purpose and cost of the test must be transferred to patient management and accounting information systems (Friedman and Mitchell, 199 1).

A sudden major environmental change that affected the hospital software systems industry occurred in 1983, with the introduction of the Prospective Payment reimbursement system and the concurrent increase in the use of selective contracting by third-party payers (Zajac and Shor- tell, 1989). Under the prospective payment system, hospitals are reimbursed for medical procedures performed according to a predefined diagnosis-related group schedule rather than on a fee-for-service basis, which had been the primary practice followed by public payment agencies since the Medicare insurance system was established in 1966 (Imberly et al., 1989). During the same period, many healthcare insurers began to issue fixed-rate contracts for hospital services rather than paying on a fee-for-service basis. The inception of prospective payment and growth of selective contracting represented a major operating change and forced hospitals to give greater attention to operations efficiency and cost containment issues (Fennell and Alexander, 1993). Hospitals required new information systems to make management decisions about resource utilization, reimbursement maximization,

marketing strategies, competitive analysis, and ownership changes (Hospitals, 1984; Collen, 1991).3

The nature of the environmental shock that occurred in 1983 is suited to the hypotheses that we raise in this study because the shock had greater impact on technical skills than on market segmentation. The environmental changes led to substantial changes in hospital behavior, including shifts in the use of treatment technology, diversi- fication into outpatient services, and increased interaction with new insurance programs (Fennell and Alexander, 1993). In turn, the changes in behavior created demands for new technical capabilities in hospital software systems (Jackson and Jensen, 1984; Palley, 1991) and required vendors to undertake major software systems modification and new development (Bozeman, 1988; Dorenfest and Associates, 1988; Collen, 1992). By contrast with technical requirements, marketing relationships remained largely unchanged for several years after the shock. Although the structure of the hospital market has changed since the early 1980s, the changes have occurred more gradually than the technical changes. Even in the mid- 1990s, well after the end of the study period, multihospital systems, which represent the largest change in market segmentation, account for a minority of available beds. Therefore, we expect hospital software systems businesses with development-oriented collaborative relationships in 1982 to be most likely to suffer following the shock, while firms with marketing-oriented relationships are more likely to have adapted effectively.

Firms operating in the industry have taken both independent and collaborative commercialization approaches. At one extreme of independence, some software businesses have relied extensively on short-term market relationships with hardware manufacturers, other software firms, and distributors to commercialize their systems. In practice, these businesses have not attempted to integrate their systems with those of other businesses, leaving this considerable task for businesses that act as systems integrators or for hospitals themselves

'The introduction of the Medicare and Medicaid systems in the late 1960s also created major changes in the industry. There were few firms and few cases of collaboration at the time, however, so that we cannot examine the impact of collaborative relationships during the period.


to undertake. At the other extreme of independence, some businesses have carried out the key activities required to commercialize hospital software systems themselves. Collaborative relationships also have been common in the industry. Many software development businesses work closely with one or more hardware manufacturers to develop and fine-tune applications software. Two or more software businesses sometimes also collaborate to ensure the compatibility of their systems or to develop complementary modules for broad-based systems spanning several administrative and clinical departments within a hospital. Therefore, the industry provides a fruitful source of information concerning the relative success of businesses that have engaged in collaborative relationships to commercialize complex goods and businesses that have operated independently.

Our approach to the challenge of gathering empirical information about collaborative relationships during the 31-year period of the study required the assumption that most important explicit and implicit interfirm relationships will be publicly reported. This assumption is credible in a study of an American industry, owing to the extensive reporting of commercial information by multiple business, industry, and government sources. The hospital software systems industry is particularly suited to this approach because it lies at the intersection of two well-reported industrial sectors: the computer and medical sectors. The characteristics and limitations of the methods we used to collect data on collaborative relationships are common to studies that have relied retrospectively on published sources for information, such as the CAT1 databank maintained by Hagedoorn and Schakenraad (1990).

We identified collaborative relationships through an extensive review of published government, business, and industry sources, augmented with interviews with participants in the industry. Our criterion for recognizing the existence of a collaborative relationship was the formal announcement of an agreement in a published medium. This announcement also served as the means for classifying agreements, because the nature or intent of cooperation was described in almost all cases. In total, we identified 693 cases in which businesses operating in the hospital software systems industry formed collaborative relationships, with such agreements being created

by 242 of the 973 businesses in the sample. Almost all agreements were among businesses operating in the hospital software systems industry. Adding a dummy variable denoting that a partner was outside the industry or unknown did not materially change the results of the statistical estimates that we report in the paper.

We believe that our search identified most publicly reported interfirm agreements and that we have assigned accurate creation dates to the agreements. We classed businesses for which there was no public record of collaborative relationships as taking independent commercialization approaches. Owing to data limitations, we did not attempt to distinguish between independent approaches in which businesses engaged primarily in hands-off market relationships and those where businesses carried out most activities internally.

Although there undoubtedly have been private agreements between individuals within businesses and perhaps between the businesses themselves, three reasons lead us to believe that most such private agreements have limited significance compared to the public agreements that we identified. First, the many publications specializing on this industry provide detailed coverage of most activities within the industry, including substantial amounts of information that the businesses have not deliberately released. Second, most significant agreements are formal and legally binding, and as such usually are public knowledge. Third, businesses benefit from widespread knowledge of most cooperative development and marketing agreements in the industry and often publicize them actively.

We omitted two types of interfirm agreements from the collaboration classifications. We defined cases in which one business purchased another business as acquisition rather than collaboration. In addition, 10 cases in which businesses created free-standing joint ventures were defined as new businesses rather than as collaborations between autonomous organizations. The characteristics of free-standing joint ventures differ significantly from those of collaborative relationships and suf- ficiently approximate the characteristics of businesses that operate independently to justify this treatment (Williamson, 1991a: 293; Kay, 1992).

Figure 1 reports the time trend in the use of collaborative relationships. Only in the 1990s does the number of collaborations begin to


approach the number of businesses taking independent approaches. The scarcity of collaborative relationships early in the study period might occur for at least two reasons. First, collaborative relationships might not have been especially necessary in the industry until the late 1980s. Even the earliest hospital software systems were generally recognized to be complex (Collen, 1991), however, so that incentives to form collaborative relationships have existed since the industry emerged. Second, many businesses might have been unable to find acceptable partners until the industry matured, which helps explain why businesses might operate independently even though independent approaches might be inferior forms of organization.

The collaboration data have two limitations. First, we found that agreement termination was much less likely to be reported than agreement creation. By necessity, therefore, our records report the cumulative number of interfirm agreements that each business had created by the end of each year of the study, rather than the number of active agreements in each year. As a result, businesses categorized as employing collaborative relationships in a particular record year can be viewed strictly as having established collaborative relationships in the past. We believe that it is appropriate to estimate the association between the use of collaborative relationships and business survival in a particular record year, because the benefits and costs of collaborative relationships are likely to extend well beyond the point at which they are formed. Second, it would also be desirable to control for the quality of collaboration, which will directly impact the risk of exit, but such information was not available. A well- executed collaborative relationship will have a more favorable impact on business survival than an ineffective collaboration. Adverse conse- quences are limited by the large number of collaborations, however, which helps ensure that the entire range of collaboration quality is represented in the data.

Table 2 describes and provides examples of the types of relationships we identified, which we group into three categories of activities. We used the following process to categorize agreements. We used published descriptions of the relationships to identify seven collaborative purposes, including (a) agreements in which information or products were licensed from another firm,

(b) technology agreements, (c) marketing agreements, (d) distribution agreements, (e) value- added relationships, (f) agreements in which information or products were licensed to other firms, and (g) other agreements. We then col- lapsed the data into the three categories: 135 cases of development-oriented relationships (licensing from other firms and technology agreements), 509 cases of marketing-oriented relationships (marketing, distribution, and value- added relationships), and 49 cases of other relationships (licensing to other firms and other agreements). We distinguished between development-oriented and marketing-oriented relationships because the two categories of activities are often carried out separately and to explore the potential differential impact of the environmental shock. The distinction between development and marketing-oriented classes of agreements is consistent with Hagedoom (1993), who found the two classes to be the most common collaborative links employed by businesses. The other relationship category includes 27 cases in which firms licensed technology to other businesses, which we separate because such licensing represents sale of know-how rather than acquisition of commercialization technology, and 22 agreements for which we could not identify a primary purpose. The three-part classification provides a basic typology that we can apply over the study period.

We test for empirical differences among the association between business survival and the different types of relationships. Marketing-oriented relationships might produce weaker influences on business survival than development- oriented relationships if marketing relationships involve looser ties between businesses. However, the complexity of hospital software systems means that marketing and distribution agreements are often substantial and demanding. For example, businesses serving as distributors typically develop skills in their partner’s software so that they can demonstrate the operation of the system, modify the system to some degree to meet clients’ needs, install the system on appropriate hardware, and undertake some maintenance of the system after installation. Similarly, marketing agreements often entail the development of significant understanding of highly complex software systems. Much of the knowledge gained and skills developed are specific to the particular systems distributed or marketed. Within the ‘other


Table 2. Types of collaborative relationships (693 cases)

Development-oriented relationships (1 35 cases): In-licensing of products or components from other business; technology agreements (e.g., joint R&D; joint development of product interfaces or product compatibility)

Example: In 1993, HBO & Co. and Clinicom announced an alliance to develop a patient management system that would document treatment costs, measure outcomes, and document adherence to treatment standards. The businesses planned to develop a new interface to allow CliniCom’s wireless bedside system to access HBO’s nursing applications (Healthcare Informatics, May 1993: 24)

Marketing-oriented relationships (509 cases): Marketing or distribution agreements (e.g., marketing or distribution by one business of partners’ products): value-added relationships (e.g., software business resells systems for hardware business).

Example: In 1993, WMG Peat Marwick, which provides consulting services and develops administrative software systems for the health care industry, announced an alliance with Biven Software, Inc., a vendor of a knowledge-based administrative system. The two businesses planned to market systems that would ‘jointly provide IS solutions for hospitals, practices and provider-based managed care organizations’ (Healthcare Informatics, April 1993: 24)

Other interjirm relationships (49 cases): Out-licensing relationships (27 cases) and agreements that could not be classified among above categories (22 cases)

interfirm relationships’ category, licensing raises conflicting issues concerning business survival, although there are too few cases for us to focus on them. Licensing technology to other businesses might be a sign of commercialization weakness and, therefore, be associated with lower likelihood of survival. An alternate argument is that licensing arrangements might aid survival by providing businesses with network externality benefits (Farrell and Saloner, 1985), which might be important in the study because there are as yet no widely accepted standards in the hospital software systems industry.

METHODS

The sample for the study was defined at the parent-firm level of analysis. A businsss that was sold by its parent to another firm was treated as having exited the industry. A business entry was recorded in such cases if the acquiring firm did not already operate a hospital software system business, which occurred in 93 instances that are identified by a dummy variable. We tracked each business from its entry into the industry until it exited the industry or until the end of the study period in 1991. The 973 businesses in the study accounted for a total of 6703 business years of participation, which we refer to as record years.

To create dependent variables for the study, we first recorded whether a business exited the industry in a given record year. The variable took a value of 0 if the business continued to participate at the end of a year and 1 if it exited during the year, which occurred in 549 cases. This procedure is often referred to as spell-splitting in the event history literature. We then created two dependent variables, distinguishing between business dissolution (316 cases) and business divestiture (233 cases). We treat each type of exit as a nonexit (a right-censored case) in the analysis of the other type of exit.

Collaborative relationship variables

Table 3 reports variable definitions for the independent variables, while Table 4 reports summary statistics. To test the first hypothesis, we defined three nonexclusive 0-1 dummy variables that recorded whether the business had entered into development-oriented (ColDev), marketing-oriented (ColMkt), and other (ColOther) collaborative relationships before the end of each record year. We also recorded the cumulative number of agreements that a business had formed by the beginning of each record year (NumCol).

To test the environmental shock hypotheses, we identified businesses that were operating in the industry during 1982, the year before the


Table 3. Definitions of independent variables (F = fixed value, TV = time varying)

Collaborative relationships ColDev: Business has established at least one development-oriented relationship by the end of the current

ColMkt: Business has established at least one marketing-oriented relationship by the end of the current record

ColOther: Business has established at least one other interfirm relationship by the end of the current record

NumCol: Number of collaborative relationships that the business has established by the beginning of the

ColDevShock: Business operating in 1982 had established at least one development-oriented relationship by

ColMktShock: Business operating in 1982 had established at least one marketing-oriented relationship by that

IndepShock: Business operating in 1982 had established no interfirm relationships by that year, recorded for

BusSalesColShock Business sales during 1982 of businesses that used collaborative relationships during that

record year (TV)

Year (Tv)

Year (TV)

current record year (TV)

that year, recorded for each year that the business operated between 1983 and 1991 (F)

year, recorded for each year that the business operated between 1983 and 1991 (F)

each year that the business operated between 1983 and 1991 (F)

year, recorded in each year that the business operated between 1983 and 1991 ($ million deflated by 1982 PPI, F)

BusSalesIndShock: Business sales during 1982 of business that did not use collaborative relationships during that year, recorded in each year that the business operated between 1983 and 1991 ($ million deflated by 1982 PPI, F)

LowShareCollab: The market share of a business that formed a collaborative relationship was below industry average during the year before the relationship was formed, recorded for each year that the firm had a colIaborative relationship (F)

HighShareCollab: The market share of a business that formed a collaborative relationship was above industry average during the year before the relationship was formed, recorded for each year that the firm had a collaborative relationship (F)

Business-level characteristics BusExp and BusExpSquared: Number of years that the business has operated in the industry, and squared

Bussales: Annual hospital software systems sales by the business ($ million deflated by 1982 PPI, one-year

NumProducts: Number of product classes listed in Table 1 that the business offers (TV) AcqEntry: Denotes businesses that operated in the hospital software systems industry under other ownership

PriorAgeFm: Corporate age (years) of the parent company when the business entered the hospital software

DivEntryHardware: Firm had experience as manufacturer of computer hardware industry prior to its entry to

DivEntryNoHardware: Firm had experience in industrial sectors other than the computer hardware industry

Startup: Business was new venture when it entered hospital software systems industry (F) Private: Privately held company (F)

term (TV)

lag, TV)

before current entry (F)

systems industry under current ownership (F)

the hospital software systems industry (F)

prior to its entry to the hospital software systems industry (F)

Industry-level characteristics MktSize: Hospital software system sales, minus business sales ($ million deflated by 1982 PPI, TV) MktGrowth: 1-year growth rate of market size (MktGrowth, = [MktSize, - MktSize, - ,]IMktSize, - ,,

MktGrowth,,,, = average of 1962-63 growth; TV)

Tabl

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(N

= 6

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Prospective Payment System was implemented. For businesses that had formed development-on- ented collaborative relationships by 1982, we set a dummy variable equal to 1 for each record year from 1983 to 1991 that the businesses continued to participate in the industry (ColDevShock). We created an equivalent variable for businesses that had formed marketing- oriented collaborative relationships by 1982 (ColMktShock). In 1982, 3 1 businesses had entered marketing-oriented relationships, 20 businesses had formed development-oriented relationships, and 302 businesses had no collaborative relationships. The coefficients for the ColDev- Shock and ColMktShock variables will be negative if businesses that have used collaborative relationships find it difficult to adapt to a sudden major environmental change. So that we could compare how the survival chances of collaborative and independent businesses changed when an environmental shock occurred, we created an equivalent variable for industry incumbents in 1982 that had not entered collaborative relationships by that year (IndepShock). The base com- parison case for the IndepShock, ColMktShock, and ColDevShock variables includes all pre- 1983 record years of all participants, all record years of businesses that entered after 1982 and the post- 1982 record years of four businesses that had other interfirm relationships in 1982, but no development-oriented or marketing-oriented agreements in that year. We also identified the business sales of businesses operating in 1982 and recorded the value for each record year from 1983 to 1991, creating variables for the size of businesses with collaborative relationships in 1982 (BusSalesColShock) and without collaborative relationships in 1982 (BusSalesIndShock). Larger businesses might have more resources to draw from in order to adapt to a sudden change or might experience greater inertia than smaller businesses.

We defined two variables to address the possibility that the market strength of a business deter- mines whether it uses collaborative relationships, in which case observed associations between collaboration and survival would stem from the market strength rather than from the collaboration. We used market share as the measure of business strength, following March and Simon’s (1958: 153) argument that market share is a relevant performance measure that might trigger mana-

gerial action. Starting with the year in which a business formed its first collaborative relationship and continuing until the business left the industry or the study period ended, we set a dummy variable equal to 1 for cases in which the market share of the business was below industry average during the year before the business formed its first relationship (LowShareCollab) and another variable equal to 1 when the prior share of the business was above industry average (HighShareCollab). The variables had value of 0 in record years before a business formed its first collaborative relationship and all record years of businesses that never formed collaborative relationships. If collaborative relationships tend to be formed either by businesses with relatively low or high market shares, then the influences of the Low ShareCollab or HighShareCollab variables will replace any overall associations between survival and collaboration.

Other independent variables

Several other business-level and industry-level variables addressed factors that might influence business survival, and help control the possibility that the choice of commercialization approach and the likelihood of survival both stem from underlying business and market characteristics. The variables are defined in Table 3. Businesses become less likely to shut down as they age (Hannan and Freeman, 1989), sometimes after an initial honeymoon period (BusExp, BusExpSquared). Dissolution rates decline with the size (Bussales) of a business (Aldrich and Auster, 1986) and might also decline with business scope (NumProducts). A dummy variable identified businesses that had operated in the hospital software systems industry under different ownership before the current entry (AcqEntry), to test for any impact of prior business experience and to control for the tendency for acquired businesses to be resold (Ravenscraft and Scherer, 1987). Businesses with prior corporate experience before entering the industry might be more likely to survive (PriorAgeFirm). Two dummy variables identified privately held (Private) and startup (Startup) firms, in order to determine whether financial structure or newness affected business survival. Among entrants that were not startup ventures, we distinguished between those with experience manufacturing computer hardware


prior to their entry into the hospital software systems industry and those without such experience (DivEntryHardware, DivEntryNoHardware), in order to determine whether possessing the resources required to manufacture computer hardware would affect the success of a computer software business.

At the industry level, greater market size and growth might be associated with greater likelihood of survival (MktSize, MktGrowth). With market segmentation of the industry into 13 types of departmental and clinical information systems, using sales of all systems as a measure of market size risks overaggregation. Obtaining accurate year-by-year sales figures by product line for either the individual businesses or the market often is not possible but our use of total market size is appropriate because our focus is on the effects of collaboration rather than the impact of market sales levels.

Statistical method

We calculated binomial logistic regression estimates of the influence of the independent variables on the likelihood that a business would exit the industry in each record year. The models took the form In PJ(1 - Pi) = pXi . In this equation, Pi is the probability that business i will exit during an observation year. The log odds of the probability is held to be linearly affected by a vector of covariates Xi with coefficient vector p (including an intercept). The effect of a one-unit change of covariate j on the probability that a business will exit in an observation year is pjPi(l - Pi). The maximum likelihood estimates were obtained using the logistic regression procedure of the SAS statistical package.

Calculating logistic regression estimates of influences on the likelihood that a business will exit in each record year is an appropriate method for this analysis because it allows us to update the annual value of time-varying covariates. Logistic regression assumes that each case is independent, which is not true in our analysis because most businesses survived longer than 1 year, but Hosmer and Lemeshow (1989) show that logistic regression produces robust estimates under these conditions as long as the conditional probabilities of the event occurring in each period are small. With dissolution exits occurring in 4.7 percent and divestiture exits in 3.5 percent of our record

years, this provision is met. Peterson (1986) and Teachman, Tedrow, and Hill (1993), meanwhile, show that the discrete time logistic regression estimator leads to no downward bias in standard errors when compared to continuous time models and that spell-splitting does not lead to unob- served correlation across multiple records. As sensitivity analysis, we used the TDA statistical package (Rohwer, 1993) to estimate the effects of the collaborative relationship variables on the exit rate, finding that logistic regression and exponential baseline rate estimates with equivalent sets of variables produced materially equivalent results. We also calculated separate logistic regression analyses for the businesses with collaborative and independent approaches (following Miner et al., 1990), which allows the variance of the exit likelihood for the two types of cases to vary, and found equivalent results to those reported here.

RESULTS

Table 5 reports a cross-tabulation of survival, dissolution, and divestiture incidence and whether a business entered a colIaborative relationship by the last year of the study period. The table shows that businesses using collaborative relationships were more likely to survive than businesses that remained independent (57% vs. 39%) and less likely to be dissolved (14% vs. 39%). Businesses using collaborative relationships were slightly more likely to be acquired (29% vs. 22%). This exploratory analysis suggests that collaboration might provide dissolution advantages but does not reduce the chance that a business will be divested.

We calculated two sets of multivariate logistic regression estimates. Table 6 reports the estimated influences on business dissolution, while Table 7 reports influences on business divestiture. Nega- tive coefficients indicate lower likelihood of exit in a given record year. All the models reported in Tables 6 and 7 are significant, which is indi- cated by the significant likelihood ratio test at the foot of each column in the tables. The likelihood ratio test statistic is defined as -2 times the difference between the loglikelihood of the estimated model and the loglikelihood of a model containing only the intercept. The statistic is distributed as a x2, with degrees of freedom equal to the number of independent variables specified

'


Table 5. Survival incidence compared to use of collaborative relationships

Business entered a Business remained

throughout study collaborative relationship independent

by last year of study

Survived to end of study 153 (57%) Dissolved 36 (14%) Divested 76 (29%) Total 265 (100%)

273 (39%) 280 (39%) 155 (22%)

708 (100%)

in the model. The increase in the likelihood ratio test statistic is the difference between the likelihood ratio test of two nested models, with degrees of freedom equal to the number of additional variables, and tests whether there is a significant difference of statistical fit between incrementally nested models. We will discuss the dissolution results first and then turn to the divestiture analysis.

Column 1 of Table 6 reports a baseline analysis of the dissolution effects of the control variables. Greater business experience had a nonmonotonic relationship with the likelihood that a business would shut down, first rising and then falling (BusExp and BusExpSquared). Businesses with greater revenues were less likely to shut down (BusSales). The greater the age of a firm when it entered the industry, the less likely it was to shut down its business (PriorAgeFirm). At the same time, though, firms with prior experience outside the hospital software systems industry often shut down their hospital software businesses whether or not the firms had experience manufacturing computer hardware (DivEntryHardware, DivEntryNoHardware), which suggests that the hospital software industry places different technical and market demands on its participants. Busi- nesses were less likely to shut down when total market sales were high (MktSize) and growing (MktGrowth).

Column 2 of Table 6 adds the collaboration variables to the baseline model to test hypothesis 1 for dissolution exits. The development and marketing collaboration results support the hypothesis, which predicted that businesses that have used collaborative relationships to commercialize complex goods will be more likely to survive than businesses that take independent commercialization approaches. The collaboration variables improve the model, as shown by the

significant increase in the likelihood ratio test statistic at the foot of the column. The results show that businesses using either development- oriented or marketing-oriented collaborative relationships (ColDev, ColMkt) were less likely to shut down in any given year than were businesses that did not use collaborative relationships. The results suggest that many collaborative advantages provide benefits to businesses that commercialize complex goods. By contrast, businesses that licensed technology to others or had unknown types of relationships (ColOther) often shut down, which suggests that selling technology to other businesses is frequently a sign of business weakness.

Column 3 of Table 6 adds the number of collaborative relationships. Adding the variable increases the significance of the analysis, based on the increase in the likelihood ratio test statistic. The NumCol coefficient is positive and significant, although with a magnitude substantially less than the collaboration variables (ColDev, ColMkt). The result suggests that the benefit of the agreements declines as their number increases and might eventually become negative. The result must be interpreted cautiously because intrinsi- cally weak businesses might seek many partners in attempts to shore up their weakness, but the BusSales variable, which is one measure of business strength, helps control the issue of reverse causality. Part of the reason for the result is likely to stem from problems created when a business forms many collaborative relationships.

Column 4 of Table 6 adds the sudden change variables, producing results that are consistent with Hypotheses 2 to 4. As predicted by Hypo- theses 2 and 3, the sudden change results in column 4 display two opposing tendencies among businesses that had formed collaborative relationships prior to the shock. On the one hand, sup-

Tab

le 6

. 31

6 ex

its)

Log

isti

c re

gres

sion

es

timat

es o

f in

flue

nces

on

the

lik

elih

ood

of

diss

olut

ion

(neg

ativ

e si

gn i

ndic

ates

low

er l

ikel

ihoo

d of

ex

it;

6703

rec

ord

year

s,

Var

iabl

e 1

2 3

4 5

Coe

ff.

S.E.

C

oeff

. S.

E.

Coe

ff.

S.E.

C

oeff

. S.

E.

Coe

ff.

S.E.

Col

labo

ratio

n Co

lDev

C

olM

kt

Cof

Oth

er

Num

ber

of r

elat

ions

hips

N

umCo

l Si

idde

n ch

ange

C

olD

evSh

ock

Col

Mkt

Shoc

k In

depS

hock

B

usSa

lesC

olSh

ock

BusS

ales

IndS

hock

Pr

ior

perf

orm

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Lo

wSh

areC

olfa

b H

ighS

hare

Cof

lab

Base

line

vari

able

s Bu

sExp

Bu

sExp

Squa

red

Bus

Sale

s N

umPr

oduc

ts

Acq

Entry

Pr

iorA

geFi

rm

Div

Enlry

Har

dwar

e"

Div

Entry

NoH

ardw

are"

Pr

ivat

e M

ktSi

ze (

X

IOOO

) M

ktG

row

th

Inte

rcep

t M

odel

log

likel

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d Li

kelih

ood

ratio

tes

t (d

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Incr

ease

in l

ikel

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d ra

tio (

d.f.)

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1 -0

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59

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28

1.37

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514

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86

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40

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188.

8 (l

l)**

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666"

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297"

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52

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27

1.46

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530

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porting Hypothesis 2, businesses with development-oriented collaborative relationships before the market environment changed in 1982 become more likely to shut down after the shock (ColDevShock). The result is consistent with the argument that some firms had become trapped by outmoded technology and could not adapt quickly to an environmental shock that required new technology. Therefore, businesses using collaborative relationships for activities central to an environmental shock faced increased risk of dissolution following the shock. By contrast, firms with marketing-oriented collaborative relationships became even less likely to shut down after the shock (ColMktShock), supporting Hypothesis 3. The result suggests that having collaborative relationships for activities outside the focus of an environmental shock appeared to have helped many businesses adjust. Presumably, the marketing partners provided information about the new needs of users that helped their allies gain a better understanding of the nature of the sudden environmental change and how to respond to it.

Consistent with Hypothesis 4, column 4 shows that businesses using independent commercialization approaches became less likely to shut down after they faced a sudden shock (IndepShcck). The coefficient had magnitude similar to the main marketing-oriented collaborative relationship effect ( C o w ) . Thus, it appears that independent approaches often lose at least part of their inferiority relative to collaborative approaches when a sudden change occurs, presumably because the ability of the firms to take independent action becomes more valuable than when change occurs gradually.

Column 4 also suggests that larger businesses lose part of their survival advantage after a sudden shock, whether they were taking collaborative or independent approaches. The BusSalesColSh- ock and BusSalesIndShock variables both take positive coefficients with about half the magnitude of the reduced likelihood of dissolution associated with the main effect of business size (Bussales). The reduction in the advantage con- ferred by greater size following a sudden major shock might stem from bureaucratic inertia. In addition, businesses with larger bases of existing customers might find it difficult to introduce radically different products that are not compatible with their existing systems. Greater business size appears to be beneficial, but loses part of its value when a need to change occurs suddenly.

Column 5 of Table 6 adds the prior performance variables in order to investigate whether the relative market position of the business deter- mines both its use of collaborative relationships and its ultimate survival (LowShareCollab, HighShareCollab). Neither variable is statistically significant, which suggests that a business’s market share relative to the industry average before it forms a collaborative relationship has little impact on the likelihood that the business will shut down after forming the collaborative relationship. Although the influence of marketing- oriented collaboration (ColMkt) loses statistical significance in column 5, the overall significance of the analysis does not increase from that of column 4, based on the increase in the likelihood ratio test statistic shown at the foot of column 5. Because the significance of model 5 does not increase, the simpler model in column 4 is prefer- able. In a sensitivity analysis, removing the collaboration variables (ColDev, ColMkt, ColOther) from column 5 significantly reduced the x2 statistic, while displaying no significant effect of LowShareCollab or HighShareCollab. This result also suggests that the main effects of collaboration overwhelm the influences of prior market share.

Table 7 reports the divestiture analyses. Col- umn 1 reports the control variables. Columns 2 and 3 add the collaboration measures, which show that Hypothesis 1 does not hold for divestiture. We find that businesses that have used development-oriented collaborative relationships are often divested (ColDev), where they were less likely to be dissolved (columns 2 and 3 of Table 6) . Businesses with marketing-oriented relationships also become more likely to be sold (column 2), but the result is not statistically significant. The difference in the magnitude and significance of the development and marketing relationship influences suggests that businesses find it more desirable to acquire firms for their R&D capabilities than for marketing skills, possibly because development activities more often result in transaction-specific investment. The number of relationships (NumCol in column 3) also is positive and moderately significant.

In light of the positive relationship between collaboration and divestiture, we expected that many of the purchasing firms would be partners of the divesting firms. Recent arguments in the strategy literature suggest that collaborative agree-

Tabl

e 7.

23

3 ex

its) Lo

gist

ic r

egre

ssio

n es

timat

es o

f in

fluen

ces

on t

he l

ikel

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d of

div

estit

ure

(neg

ativ

e si

gn i

ndic

ates

low

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ikel

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d of

exi

t; 67

03 r

ecor

d ye

ars,

Var

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e I

2 3

4 5

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ff.

S.E

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oeff.

S.

E.

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ff.

S.E

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oeff

. S.

E.

Coe

ff.

S.E

.

Col

labo

rorio

ti C

olD

ev

0.51

4 0.

217"

0.

456

0.22

0"

0.51

8 0.

235'

0.

580

0.27

2"

Col

Mkt

0:

020

0.19

0 .

-0.0

64

0.19

9 -0

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0.

2 14

0.01

0 0.

308

Col

Oth

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0.25

4 0.

303

0.24

5 0.

303

0.27

5 0.

305'

0.

340

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0 N

iiiiib

er o

f re

la~i

oirs

hips

N

uinC

ol

0.04

2 0.

024'

0.

03 I

0.02

7 0.

029

0.02

7 Si

idde

ti ch

ange

C

olD

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-0.2

16

0.45

7 -0

.163

0.

465

Col

Mkt

Shoc

k 0.

161

0.39

6 0.

083

0.40

9 Iii

depS

hock

0.

153

0.18

6 0.

147

0.18

6 Bu

sSal

esC

olSh

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0.01

3 0.0

1 I

0.01

3 0.

01 I

BusS

ales

lndS

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0.

01 I

0.01

3 0.

012

0.01

3 Pr

ior

perf

orm

ance

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wSh

areC

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b -0

.159

0.

331

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line

vari

able

s Bu

sExp

0.

253

0.04

4"'

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044"

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262

0.04

6"'

0.25

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049"

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261

0.04

9"'

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xpSq

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d -0

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0.

002"

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.008

0.002"'

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09

0.002"'

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09

0.00

2"'

-0.0

09

BusS

alcs

-0

.003

0.

004

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05

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4 -0

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0.

004

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10

0.00

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007'

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urnP

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4 0.

035

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Prio

rAge

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-0

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0.

008

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05

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008

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009

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dwar

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48

0.42

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-0.8

20

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155"

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.395

0.

156"

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0.

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-0

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0.

158"

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0.

161"

M kt

Gro

w t h

0.

077

0.26

3 0.

074

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2 0.

039

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0.

280"

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282"

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283"

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0.

298"

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0.

299"

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Mod

el l

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-965

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-961

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st (d

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2-

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Size

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ments sometimes act as learning devices that allow prospective buyers to evaluate the worth of another business (see Balakrishnan and Koza, 1993). Contrary to this argument, however, of 196 businesses for which we could determine whether the acquiring firm was a partner (out of 233 divestitures), only two involved partners. This result is consistent with that reported by Mitchell and Singh (1992), who found that firms that used alliances before undertaking independent entry to new technical subfields of the medical diagnostic imaging industry rarely acquired their partners. It appears, therefore, that collaboration is sometimes a substitute for acquisition rather than a precursor to it, complementing Balakrishnan and Koza’s (1993) argument that joint ventures offer alternatives to acquisition.

Columns 4 and 5 of Table 7 add the environmental shock and prior performance variables to the divestiture analysis. None of the additional variables is statistically significant. It appears that shocks and performance prior to forming collaborative relationships have little impact on business divestiture. Instead, only the use of development- oriented collaboration has a substantial influence on divestiture.

We carried out several sensitivity analyses of the results. We calculated by-year estimates of the relationship between the likelihood of dissolution and a dummy variable that denoted firms with either development-oriented or marketing- oriented agreements. Consistent with the pooled results, the by-year estimates were negative in all but two years, neither of which was statistically significant. To explore differences among the types of relationships, we defined three mutually exclusive variables for ‘development-only,’ ‘marketing-only,’ and ‘both types’ cases (the ‘both types’ category applied to 67 businesses in a total of 335 record years). The results obtained for the ‘development-only’ and ‘marketing-only’ variables were similar to those reported above, while the ‘both types’ estimate was nonsignificantly negative for dissolution and significantly positive for divestiture. We also investigated the influence of several other independent variables. At the business level we identified product class dumm- ies, hospital size for which a business’s products were suited, and the number of years of business experience under other ownership before the current entry. At the industry level, we identified the calendar year of entry, the number of business

failures in the U.S.A., expenditure by U S . community hospitals, the number of community hospitals in the U.S.A., the number of admissions to U.S. community hospitals, the number of beds in U.S. community hospitals, the number of businesses in industry when a business entered, the density in each record year, and a square of the current density term. Adding these variables did not materially change the reported results.

DISCUSSION AND CONCUSION

Several results of this study are notable. The analyses show that businesses using collaborative relationships to commercialize complex goods are less likely to shut down as long as the environment changes gradually but may be more likely to be sold to another firm. Following an environmental shock, firms with collaborative agreements for activities outside the focus of the shock become even less likely to shut down. By contrast, firms with collaborative agreements for activities that are at the focus of the shock face a reversed impact of collaboration, losing their earlier dissolution advantages. In a similar rever- sal, businesses taking independent commercialization approaches lose at least part of their inferiority when a shock occurs. The results suggest that collaboration is usually beneficial but can sometimes turn on the user. Development-oriented and marketing-oriented collaborative relationships appear to help businesses acquire needed commercialization capabilities, but firms risk becom- ing locked into obsolete capabilities following a shock if they become dependent on a partner.

This study is one of the few empirical analyses that contrasts the benefits and disadvantages created by collaborative forms of organization. The study brings together arguments that collaborative will create coordination advantages for businesses commercializing complex goods (e.g., Powell, 1990; Langlois and Robertson, 1992; Teece, 1992) with assertions that close collaboration might also create adaptation problems for the partners (e.g., Weick, 1979; Aldrich and Whetten, 1981; Williamson, 1991a; Miles and Snow, 1992). From the point of view of managerial strategy, our study suggests that there is a need to evaluate the benefits of interfim! collaboration more critically. The large strategy literature concerning this subject sometimes offers interfirm


collaboration as a panacea by which businesses can address and overcome a wide range of limitations. While clearly pointing to the value of cooperative relationships, our study is among the first to provide empirical evidence of the limits of such links.

Despite the limits, collaboration usually appears to be superior to independent approaches in help- ing a business avoid the clear-cut failure of dissolution when commercializing complex goods. Why then, do not most or all businesses form collaborative relationships? The results of the study suggest two reasons. First, some firms might be unable to find suitable partners that are willing to form collaborative relationships, especially given the apparent impact of the number of partners on business exit during periods of gradual change. Firms that otherwise would be desirable partners but have already formed several collaborative relationships with other businesses might refuse to create additional agreements. Second, using collaborative forms of organization raises a substantial tension for business managers. Although the relationships often help a business avoid dissolution, businesses with collaborative relationships become somewhat more likely to be sold. While selling a business often will be profitable for shareholders, although by no means always, business divestiture frequently is disruptive for the employees, suppliers, and customers of the divested business. The disruption concern is heightened by our finding that many divested businesses are later resold, which is shown by the positive AcqEntry coefficient in Table 7. Thus, managers will often prefer to avoid collaborative relationships if the approach appears likely to lead to divestiture. The prefer- ence for maintaining business independence is particularly likely if managers believe that they are likely to encounter sudden environmental shocks, in which case collaboration at the focus of a shock may create problems while independent approaches lose their relative inferiority.

More generally, the study provides empirical evidence for the argument that organizational choices significantly affect organizational survival. The results are consistent with the premise that managers often face substantial constraints when making choices that would affect the way their business is organized. As a result, businesses with fewer constraints or with managers that are able to overcome the constraints will sometimes bene-

fit relative to their competitors as a result of their choices of organizational relationships.

The study points to the need to develop managerial skills for both collaborative and independent sourcing of key components. Businesses that are able to work closely with current partners while at the same time identifying possible new partners or identifying capabilities that need to be integrated are likely to be the most successful in an industry marked by ongoing technological change. Such a dual orientation has limits. One restraint is the risk of offending an important current partner that could be put out of business by a business that is being courted for a future relationship. In addition, it might be difficult to establish two sets of commercialization routines within a single business, with one set of routines being oriented to close collaboration with partners and the other to regular reevaluation of independent approaches. Nonetheless, empirical observation of businesses that consistently perform well in their industries while both maintaining independent strength and forming many interfirm relationships suggests that businesses that are able to create effective dual sourcing orientations will benefit. The ability of many Japanese businesses to manage interfirm relationships while also developing independent internal capabilities appears to contribute to their success in competition with many North American and European firms (e.g., Fruin, 1992). In the U.S.A., meanwhile, firms such as Coming Glass and Chaparral Steel use interfirm agreements as central parts of their ongoing business strategies, while also maintaining strong independent research and marketing capabilities. This issue provides an important topic for further research.

Several other issues merit attention. First, addressing differences in the types, strength, and motivations of collaborative relationships would yield important insights. Second, one partner’s performance is likely to affect the other partner’s survival. Third, it would be valuable to examine the relationship between collaboration and survival of networked groups of organizations. Fourth, it would be useful to examine the impact of using collaborative relationships for noncom- plex technology. Fifth, it would be useful to examine the evolution of collaboration as the components of a system evolve. This study helps provide a base to analyze these questions.

One must use care in generalizing the results


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ACKNOWLEDGEMENTS

We appreciate comments received from Bruce A. Friedman, M.D., Ken Kusunoki, Anand Swamina- than, Michael Tushman, and participants of the Conference on Science and Technology Policy into the Next Century, January 7-9, 1993 (hosted by the MIT-Japan Science and Technology Program), at which an earlier version of this paper was presented. We also are grateful for comments received from several reviewers.

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