9
Coordination as Linkage: The Case of Software Development Teams

Sara Kiesler, Douglas Wholey, and Kathleen M. Carley

This chapter examines coordination in software development teams as a practical context for talking about the linkages between individual and group productivity. We do not discuss individual-organizational nor group-organizational linkages, although many of the points we make pertain to those linkages as well.

Software development is a kind of technical work found in many organizations: the technical team project. Certain technical tasks transcend the ongoing functions of departments or the capabilities of individuals, and thus organizations create a project group or team to do the work. A software development project group can have two to several hundred members. Membership is typically diverse; the work may require the participation of programmers, software engineers, applications experts, researchers, requirements analysts, software testers, documentation writers, project managers, customer support personnel, and perhaps others. Project members may be drawn from different locations and different departments and may even work on the project in different places. The projects have predictable stages but also experience unpredictable changes in the organizational and technical environment—changes in personnel, modifications in available software and hardware technology, changing client expectations, and new economic constraints (Brooks, 1987).

In software development, productivity depends on teamwork. Teamwork refers to work done as a team and to the attitudes, skills, and behaviors that subordinate personal prominence to the efficiency of the



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Organizational Linkages: Understanding the Productivity Paradox 9 Coordination as Linkage: The Case of Software Development Teams Sara Kiesler, Douglas Wholey, and Kathleen M. Carley This chapter examines coordination in software development teams as a practical context for talking about the linkages between individual and group productivity. We do not discuss individual-organizational nor group-organizational linkages, although many of the points we make pertain to those linkages as well. Software development is a kind of technical work found in many organizations: the technical team project. Certain technical tasks transcend the ongoing functions of departments or the capabilities of individuals, and thus organizations create a project group or team to do the work. A software development project group can have two to several hundred members. Membership is typically diverse; the work may require the participation of programmers, software engineers, applications experts, researchers, requirements analysts, software testers, documentation writers, project managers, customer support personnel, and perhaps others. Project members may be drawn from different locations and different departments and may even work on the project in different places. The projects have predictable stages but also experience unpredictable changes in the organizational and technical environment—changes in personnel, modifications in available software and hardware technology, changing client expectations, and new economic constraints (Brooks, 1987). In software development, productivity depends on teamwork. Teamwork refers to work done as a team and to the attitudes, skills, and behaviors that subordinate personal prominence to the efficiency of the

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Organizational Linkages: Understanding the Productivity Paradox whole. Teamwork is crucial because every job and every stage is interdependent. High levels of individual productivity do not ensure success. Productivity depends on leveraging competencies through teamwork (Clark et al., 1987). Coordination is the overt, behavioral instantiation (representation) of teamwork. That is, coordination is what people, technology, or organizations actually do to integrate team members and their work to form a group product. Measures of coordination include observations that different people and subunits working on a project agree to a common definition of what they are building, share information, hand off components of the work expeditiously, take responsibility for one another's performance, and mesh their activities. Coordination should be distinguished from exogenous forces—prices, monopoly position of the group, resources made available to the group, management priorities, and so forth—that affect group productivity directly rather than through linkages. THE DOMAIN OF SOFTWARE DEVELOPMENT Software development is a theoretically interesting context for examining linkages and it also has practical importance. The United States has more than 7,000 software firms; many other firms participate in the development of software systems (National Science Board, 1989). Business, education, government, and technical endeavors ranging from automated manufacturing to financial transactions to national defense require complex software systems. Most experts agree that the demand for software outstrips the ability of firms to produce it. Software systems are notoriously difficult to produce. Problems often force delays in the implementation of new applications, compromises in what those applications can do, and uncertainties about their reliability (National Research Council, 1990). Coordination in Software Development Simplified models of the software life cycle break its development into distinct phases. One such breakdown is that suggested by Davis (1987): (1) problem definition, (2) feasibility, (3) analysis, (4) system design, (5) detailed design, and (6) implementation and maintenance. A variety of tasks, each with its requisite skills, must be done during these different phases: analysis, design, coding, documentation, and testing. Analysis involves evaluating and translating organizational or individual needs into system capabilities. Design involves developing a set of distinct logical units, each of which can be developed and

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Organizational Linkages: Understanding the Productivity Paradox tested separately; choosing software and hardware; structuring a data base so as to minimize redundancy and improve ease of access; and so on. Coding means translating the design specifications into executable instructions that run reliably and efficiently on particular hardware. Documentation involves coordinating and maintaining consistency of the human-computer interface, writing manuals and specifications, and preparing the internal code description, as well as recording the rationale behind design and coding decisions. These tasks are highly interactive in that changes in requirements often require changes in design, code, and documentation. Design decisions often feed back to change or limit the capabilities that the system can offer. Changes in the hardware and software, or changes in a company's financial status, may force the team to return to the design phase. This process is iterative in that software systems must be enhanced and changed as the environments in which they exist change and as people put them to new uses. Achieving a successful software system requires coordination among the various phases and tasks involved in the software development cycle and minimal backtracking. If the software system is small, and members are physically proximate and respect one another, effective coordination can occur because the group can work out problems together and keep all the implementation details in focus. This focus on sharing ideas through direct communication is what traditionally has been meant by teamwork; it is the main emphasis of cooperative team learning in high school and college classrooms (e.g., Bossert, 1988–1989). In many cases of modern technical work, however, this simple model of coordination is impossible. Kraut and Streeter (1990) discuss three reasons why this is so—project complexity, uncertainty, and interdependence. Complexity A fundamental characteristic of many software tasks is that they are too big for any one or two skilled programmers to undertake alone. Moreover, a single complex skill like programming is not the only skill required in the software development process. Software development also requires analysis to determine what the software should do; evaluation of alternative platforms; design to shape the basic structure of the programs and their communication with other programs, data bases, and users; tests to ensure that code meets requirements and that users understand the interface; creation of special tools for implementation; hardware and software maintenance procedures; written documentation; and an administrative infrastructure to set priorities on requests for features and to handle feedback from users.

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Organizational Linkages: Understanding the Productivity Paradox Uncertainty Complexity per se does not invariably lead to difficulties in coordination. As Kraut and Streeter (1990) note, automotive factories, textile mills, and tuna canneries employ hundreds of people to produce their products, yet many run smoothly. Software development is different in that it is more uncertain. Manufacturing involves routines, doing the same thing repeatedly. But the software development process is nonroutine activity, and specifications for it invariably are incomplete. Incompleteness partly results from limited knowledge of the software development domain (Curtis et al., 1988). At many points the information that designers or programmers need to make decisions is not available to them, although others in the project may have the knowledge needed for those decisions. Software development is also uncertain because specification of what a software system should do changes over time (Brooks, 1987; Curtis et al., 1988; Fox, 1982). Competition, regulations, standards, company politics, plans, and financial conditions can lead to changes in specifications. Also, it is often only by using software that purchasers understand its capabilities and limitations. As they use the software, they often demand new capabilities that they were not able to envision at the software's creation. Uncertainty in software development may be reflected in disputes among different groups involved in its development (Curtis et al., 1988; Kraut and Streeter, 1990). People associated with different parts of a project can have different beliefs about what the software should do. For example, analysts translate users' needs into requirements for system capabilities. As a result, they often adopt the point of view of the software's purchasers. On the other hand, designers and programmers may have more of an insider's focus and emphasize ease of development and efficiency of operation. These differences in points of view must be resolved for the team to succeed. Interdependence Complexity and uncertainty in software work would be less of a problem if software did not require integration of its components to such a large extent. Software consists of hundreds or thousands of modules or components that must mesh with each other perfectly for the software system as a whole to operate correctly. One mistake in part of a system can have disastrous, unanticipated consequences (Travis, 1990). This required integration, combined with complexity and uncertainty, requires in turn special coordination techniques that may not be neces-

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Organizational Linkages: Understanding the Productivity Paradox sary in more standardized manufacturing and in developing projects that are merely complex and uncertain (Ouchi, 1980). Research on Individual-Group Linkages Much of the existing research on software development and other technical teamwork does not deal with linkages. There has been considerable work on individuals' cognitive problems resulting from creating, understanding, and debugging programs, designs, and other aspects of systems, and on the individual-computer interface. (See Curtis, 1985, for a sample of this kind of research.) This approach ignores the linkage issues inherent in software development. Results from studies of individuals' problems in engineering and interaction with computers do not generalize simply to team problems (Scott and Simmons, 1975). Other research does address linkages, but typically indirectly. A great deal of work has gone into software and procedures that should promote coordination, such as code reuse, computer-aided software engineering (CASE) tools, object-oriented languages, and automatic code generation (Chase, 1987; Sims, 1989; Verastegui and Williams, 1988). However, the effects these developments have on linkages are rarely evaluated. Also, there are descriptive studies of labor costs and delays in software development. Generally, these studies use sophisticated simulation or models but not measures of coordination other than costs (Abdel-Hamid and Madnick, 1989; Beatty, 1986). Hence, much of the research on software development does not help one understand individual-group linkages in this domain. Outside the domain of software development, in laboratory studies of group decision making and problem solving, there has been considerable research on individual-group linkages. These studies have long shown that group productivity usually does not equal the sum of individual group members' performance.1 At the least, if individual labor 1   The definition and measurement of performance and productivity at the group or organizational level are not addressed in this chapter. However, to understand the behavioral research on groups and teams, one must know that behavioral scientists primarily use behavioral rather than economic measures of performance. These may be of several kinds: (1) quantity, quality, or cost-effectiveness of per-person output, such as number of problems solved or service calls completed; (2) disruptive behavior, such as absenteeism, accidents, or labor disputes; and (3) attitudes, such as subjective ratings of the quality of work life (Guzzo et al., 1985). Those interested in improving individual productivity have estimated effect sizes for many interventions and the standard

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Organizational Linkages: Understanding the Productivity Paradox is to be combined into a joint product, some resources must be invested in the combination process itself. For instance, planning as a group takes time and effort. Social psychologists who study small groups have called the transaction costs of coordinating work in a team ''process losses" (Steiner, 1972). Three approaches have evolved as ways to reduce process losses and improve coordination: team member selection and training, team design, and team communication. Coordination through Selection and Training In software teams, the top 10 percent of programmers are said to be more than four times as efficient as the bottom 15 percent (Boehm, 1987). These individual differences in ability are relevant to teamwork. If a team is staffed with highly skilled and experienced workers, team tasks such as training, job design, and management are made simpler. More important, because members of a team interact, they influence one another and the team as a whole (McGrath, 1984). Individual competency multiplies through intragroup learning and transfer of skills. Under a competency multiplier process, teams made up of highly competent members outperform other teams even beyond what their individual abilities would predict. The multiplicative effects of individual abilities are particularly important when the team's work is complex, uncertain, and interdependent. Highly able team members can solve nonroutine problems and teach those solutions to one another (Clark and Stephenson, 1989; Hill, 1982; Hinsz, 1990). These members contribute valuable nonoverlapping skills and cancel one another's errors, so team interaction bestows extra benefits on team performance (Porter et al., 1975; Tziner and Eden, 1985). Competency multiplier effects also may be seen over time because competent members become better at what they are already good at     deviation of job performance in dollar terms. These estimates can be used to show the financial benefit of labor savings that would be achieved by introducing an intervention or even what the net dollar value of the intervention would be, taking into account all business costs and benefits (Hunter and Schmidt, 1983). Several difficulties stand in the way of doing this to estimate the effect of a coordination strategy or intervention on team productivity (Boehm, 1987; Jones, 1986). Agreed team goals may exist but have no metric because the work is unique (e.g., building a space platform). Time-based indices are popular: lead time, time to completion of project, and even time to profit (Clark et al., 1987; Abdel-Hamid and Madnick, 1989). These measures do not, of course, address the quality of work.

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Organizational Linkages: Understanding the Productivity Paradox FIGURE  9-1 Competency multiplier effects. and, together, more uniquely able than other teams (March, 1981). (See Figure 9-1.) Competent teams gain more from technological interventions and tools that increase individual competency and intermember learning, which contributes to an increasing gap between excellent and poor teams. In this manner, selection and training to acquire the most competent team members become a linkage factor, especially over time. A strategy that focuses exclusively on individual selection and training to achieve teamwork is often impractical and has a number of disadvantages. Organizations often are prevented from hiring only the best people. The best people may lead to higher labor costs than are necessary. Moreover, those whose high talents are hidden initially cannot be discovered if the organization tries to hire only those with excellent resumes. Finally, even a group of highly qualified individual workers, placed on a team, may function poorly as a team unless attention is given to their organization as a team. Coordination through Team Design Organization as a team, or team design, refers to the organizational structure and formal procedures that provide "built-in" solutions to coordination. These solutions may include task decomposition, lines of

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Organizational Linkages: Understanding the Productivity Paradox authority, centralization of control, and standard operating procedures, or they may include technologies to standardize or rationalize the work itself.2 Team design through structure and formalization is theoretically an efficient alternative to direct communication when tasks are complex, uncertain, and interdependent (Aldrich, 1979; Cyert and March, 1963; Downs, 1967; March and Simon, 1958; Simon, 1962). For instance, instead of having to talk repeatedly about what each person should do, formal task decomposition allows a group facing a complex task to divide its work into manageable chunks. It should not be surprising, therefore, to find that recent solutions to effecting teamwork in software development and other kinds of technical work have emphasized team design. A major emphasis in team design has been the development of formal procedures governing communication at various stages of the work. For instance, formal meetings may be held at predetermined times in order to consider decisions about changes in the design. Brooks (1987), Curtis et al. (1988), and Fox (1982) noted that problems in accurately and completely communicating stable software requirements to members of a software project are among the most difficult to resolve in software development. Formalization is thought to increase control and regulate information flow. Written specifications or plans, documentation, and formal meetings ensure adherence to the plan and system as they evolve and that all the components fit together. Formalizing project management can also help managers monitor teams' work. Each phase of the work cycle, from planning through operation and maintenance, is supposed to have well-defined products 2   Modern software practices (e.g., logical models, well-defined interfaces, modularity, layered architectures, hierarchical management, object orientation) can be considered team designs because they are meant to regulate the number of connections that people and software components have. Modularity and information hiding, hallmarks of object-oriented design and programming languages, are thought to promote independence in programming, ease adaptation, and minimize backtracking (Dietrich et al., 1989; Parnas, 1972; Rumbaugh, 1991). Object-oriented design and programming also directly incorporate team design principles through inheritance. In software engineering, computer-aided software engineering (CASE) tools have been developed to facilitate the development of logical models, coordinate project design through a shared data dictionary, and automate input/output analysis (Sodhi, 1991; Zarella, 1990). The degree to which current CASE tools actually facilitate team coordination still is under contention (Spurr and Layzell, 1990). In a recent comparison of software maintenance teams that did and did not use CASE tools, groups using CASE tools were less productive (Banker et al., 1991; see also Orlikowski, 1988).

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Organizational Linkages: Understanding the Productivity Paradox and milestones. Thus, it is specified in advance what will be delivered at each stage and how the deliverables will be tested or scrutinized to ensure that they do what they are supposed to do. In software development, all official project documents may be under change control. For example, there are usually naming conventions that must be adhered to project wide. Similarly, code cannot be written without design reviews; code cannot be tested before code walk-throughs; changes cannot be made without issuing a modification request; no piece of code goes to system test without an integration test; and so on. Another important element in team design is the authority structure, which can be used to resolve disputes and inconsistencies across units. There is some evidence from an extensive comparison of automotive product development teams that significant variance in the authority structure contributes to the superior performance of Japanese automobile design teams over their American and European counterparts. Japanese team managers had greater authority and independence than American and European managers did (Clark et al., 1987). A concomitant of this idea in software development is that a chief designer or architect is the one person in a complex project who has sufficient knowledge of both the application domain and the possible software architectures to integrate the two. Weinberg (1971) advocated the chief programmer role, in which a senior designer/programmer has control over a software project. Problems arise when the design is distributed in more than one head or, worse (and probably more typically), is not in anybody's head. According to Curtis et al. (1988), skilled designers often assume responsibility for communicating their technical vision to other project members and for coordinating the work of the project. In sum, team design (including group structure, formal procedures, and hierarchy) is advocated in teams to routinize the transfer of information and increase control and reliability. Formality and written documentation also are attempts to reconcile differences of opinion, help people understand their goals and those of others, induce the evaluation of alternatives, and develop agreements that all can accept. The effort expended by a small group writing a formal design document can be more than offset by the communication forgone later when each project member does not need to describe his or her vision separately to the scores of people who need the information. Formal procedures also reduce errors. Thus, for example, in software development one might run automated consistency checks on a formal specification document (cited in National Research Council, 1990) or even use a computer-based system that tracks modification requests to trigger management intervention when a project schedule slips.

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Organizational Linkages: Understanding the Productivity Paradox Benefits obtained from team design do not come without costs, however. Formal structures and procedures can place an extra burden on development costs by increasing the need for a coordination infrastructure: training, increased clerical and management staff, and increased project reports and archives. Fox (1982) estimated that in large software projects, 50 percent of the cost is for planning, checking, scheduling, managing, and controlling. Tools and techniques that formalize communication or management require that time and effort be spent in teaching people to use them and ensuring that they do. Change-control systems are potential time wasters or distractions from work. Management sometimes uses standardization and rationalization of tasks to increase control, which can sap motivation and increase dependency on outside experts. Design also can impede innovation by limiting the options explored by a team. Finally, the "care and feeding" of bureaucracy can become more significant to employees than the ultimate goals they are supposed to accomplish. A particularly serious disadvantage of team design as a coordination strategy is that it can depersonalize interaction. For instance, with task decomposition, team members, or subgroups of the team, have different roles. Team members or subgroups working on their own tasks tend to develop divergent perspectives and habits of work (e.g., Brewer and Kramer, 1985; Tajfel, 1982). They may have little opportunity or eagerness to learn from others on the team, which will impede the exchange of expertise and discovery (Burns and Stalker, 1961; Carley, 1990, 1991, 1992; Faunce, 1958; Festinger et al., 1950; Jablin et al., 1987; Monge and Kirste, 1980; Newcomb, 1961). Task decomposition can also exacerbate demographic or skill differences that existed at the start (Barnlund and Harland, 1963; Dearborn and Simon, 1958; Jablin, 1979; Monge et al., 1985; Sykes et al., 1976). Whether team design through structure, formalization, or technology actually works as well as it is supposed to theoretically, remains debatable. Boehm's (1987) analysis of software productivity indicates that productivity due to changes in team design increased by just 7 percent between 1981 and 1986. Card et al. (1987) reported that software engineering technology improved reliability 30 percent but had no impact on productivity. Chapter 2 reaches the same conclusions as Card et al. did in 1987. Coordination through Team Communication Experience, organizational theory, and behavioral research suggest that team design does not by itself solve all coordination problems in teamwork. No matter how successfully task decomposition, authority

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Organizational Linkages: Understanding the Productivity Paradox structures, or standard operating procedures reduce the number of interfaces between team members, different members with different skills and perspectives still must negotiate what is to be built and fit together pieces of the design. Consensus formation, sharing of know-how, and integration of work outputs create communication demands that if not met at one level tend to surface at others. Team design, while necessary for some purposes, is sometimes a misguided attempt to apply structure and formalization when they are not suitable. Formal coordination mechanisms are intended to simplify and disaggregate behavior and therefore increase group resiliency, but they can fail in the face of interdependence under uncertainty, which typifies much software work. Flexibility, texture, richness, expressiveness, and sometimes accuracy—all disappear during the codification of roles, rules, and procedures (Boisot and Child, 1988; Bruner, 1974). Under these circumstances, communication is needed for coordination (Clark et al., 1987; Daft and Lengel, 1984, 1986; Kraut and Streeter, 1990; Stohl and Redding, 1987; Van de Ven et al., 1976). Direct communication is also referred to as coordination by feedback (March and Simon, 1958), mutual adjustment (Thompson, 1967), organismic communication networking (Tushman and Nadler, 1978), clan mechanisms (Ouchi, 1980), and informal communication (Kraut and Streeter, 1990). These terms convey the unique advantages of talking personally with others: spontaneity, interactivity, richness, friendliness. With communication, people develop deeper relationships and more opportunities to observe and learn from one another. Communication improves group commitment, socialization, and sometimes control. It makes possible the acquisition and maintenance of group culture, authority, and norms that people do not talk about overtly (Levitt and March, 1988; Nelson and Winter, 1982). Communication counters some of the costs to relationships of formal approaches to coordination. Research on communication in organizations has shown the heavy use made of communication in research and development teams where work is uncertain (e.g., Adams, 1976; Allen, 1977; Pelz and Andrews, 1966; Tushman, 1977). Despite its advantages, constant communication in the traditional sense of face-to-face or telephone conversation is impractical in many software development teams. The ease of acquiring information is at least as important as the quality of the information in determining the sources that people use (Culnan, 1983; Zipf, 1935). Physical proximity is the major determinant of engineers' work-related information exchange and influence on projects (Allen, 1977). Constant communication may be undesirable as well as impractical—who can be reached conveniently is not necessarily the same as who can contribute high-quality infor-

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Organizational Linkages: Understanding the Productivity Paradox become more similar to one another in their personal attitudes and ideas about teamwork (e.g., Kiesler and Kiesler, 1969). In experimental studies, entrainment is inferred from a group's tendency to use the same work methods even though the task demands change. In research with ongoing groups, entrainment must be inferred by examining the extent to which coordination approaches become more similar and predictable over time. Ironically, as teams become better at what they do and better coordinated, they also can become increasingly rigid in their approach to coordination. If their task assignments change, team members may be unable or unwilling to adjust their coordination strategies to the demands of the new tasks. They may be too internally focused and too comfortable, and their previous successful experience will not have suggested ways in which they should change. Research has only begun on this problem. RESEARCH PROBLEMS AND DIRECTIONS Much of the research discussed above was conducted with small, homogeneous groups working on well-specified collaborative tasks that can be done in one or a few sittings (McGrath, 1984). Except for the work on lateral coordinative mechanisms (which does not examine the role of groups in particular; e.g., Burns, 1989; Galbraith, 1972; Lawrence and Lorsch, 1967; Pfeffer, 1978), there has been relatively little research on coordination of large and ongoing teams within organizations. Also, little is known about technical teams in organizations that use computer-based technology. Such technology permits organizations to form large, dispersed, and diverse teams working on complex, uncertain, interdependent tasks that would not have been possible in the past. These teams have coordination problems that differ from those of traditional small groups and formal departments whose members are physically proximate. Laboratory and field research must employ technological and other resources to study the modern technical team. Certain theoretical problems also must be solved if researchers and practitioners are to understand linkages between individuals and teams. Two of these problems are described in the next two sections. Efficiency versus Social Effects Observations of today's technical projects (e.g., Curtis et al., 1988; Sproull and Kiesler, 1991) suggest that multilevel theories may be required to capture fully how coordination acts as a link between individuals and group productivity. In a two-level framework, for instance,

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Organizational Linkages: Understanding the Productivity Paradox coordination mechanisms are viewed as having efficiency effects and social effects. Efficiency effects of coordination are the direct, intended benefits of coordination minus its direct costs. These are the benefits and costs discussed above. However, coordination mechanisms can also have systemic, long-term effects on the team, organization, or social system. (For this concept, see Maruyama, 1963; Mason, 1970.) For instance, suppose as a result of using electronic mail to coordinate work, dispersed teams also discover ways to mobilize to influence management policy. Here, communication initially intended simply as an efficiency amplifier for a team also has effects on employee participation and organizational politics. Or, as was observed in one study, management may realize that the communication system can be used to monitor individual team members' performance in ways that used to be too difficult, which changes its authority relationships with the team members (Rule and Brantley, 1990). Social effects can affect linkages and productivity qualitatively and in ways that were entirely unanticipated. For instance, while greater employee participation may have no direct effect on the performance within teams, it can increase interteam learning and exchange of expertise across teams. Linkages and Scaling Up Another theoretical problem in the study of linkages is the incomplete understanding of how to study behavior across individual, group, and organizational levels of analysis. Experimental studies of individual behavior and simple tasks are necessary to test causal hypotheses, but one cannot deduce from experimental findings what will happen with real groups in organizations. Experimental group behavior never replicates exactly that of ongoing groups in organizations. One approach to scaling up from individuals and simple tasks to teams and more complex tasks is to add variables. Amount of discussion (a communication variable) and centralization (a design variable) are variables, for example, that would be appropriate at the group level. A more difficult scaling problem arises, however, when such variables do not scale at the same rate; then multivariate effects change, which causes a phenomenon in the large to look very different from the way it looks in the small. For instance, discussion between two persons working together seems qualitatively different from meetings of 100 or more members of a large team. Ship designers encounter this problem when they try to deduce the behavior of a full-size ship from tests of models. Two important factors in a ship's drag are waves made by the ship's prow and turbulence under the ship. Because wave effects and turbu-

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Organizational Linkages: Understanding the Productivity Paradox lence depend on fine details of the hull shape, designers cannot rely on mathematical calculations alone. Instead, they build scale models and tow them in water, measuring their drag. Although the model gives an estimate of drag, there is no way to measure how much of the model's drag is accounted for by turbulence and how much by making waves. To complicate matters, the two factors do not scale in the same way. The turbulence under the ship depends on the surface area and the speed to the 1.825 power, but the wave drag is a much more complex function of speed and ship size. Since the two effects are confounded in the model's drag and scale differently, scaling up from model tests is very hard. The ship in its full glory may act very differently than the model did, particularly if the model is small relative to the ship. Ship models and towing tanks are surprisingly large for that reason. Based on evidence to date, the scaling problem is probably serious in researching the linkages between individual productivity and the productivity of large, dispersed project groups. For example, in asking how computer technologies and networks affect group coordination and productivity, researchers can test some hypotheses in the laboratory, but in reality, networks often inspire more groups, larger projects, more diverse groups, and more flexible group structures (Sproull and Kiesler, 1991). A social consequence of this is that peripheral employees, such as geographically or organizationally isolated employees, gain new opportunities to initiate and receive communication (Eveland and Bikson, 1988; Fanning and Raphael, 1986; Wasby, 1989). If management policies permit such interactions, peripheral employees can increase their membership in groups and their connections to groups. These interactions can increase information flow between the periphery and the center of the organization and among peripheral workers. In short, while increasing connections through network communication could increase the participation of everyone in principle, peripheral employees are likely to see a relatively greater impact than are central employees (Eveland and Bikson, 1988; Hesse et al., 1990; Huff et al., 1989). This chain of events looks very different from a linear scaling up from individual or even small group behavior in relatively simpler settings. In sum, individual behavior and small group behavior may scale differently to organizational reality. Variables that seem trivial (perhaps because of low variance) in the laboratory may loom much larger in an organization—and vice versa. If so, one may see the same phenomena differently in the two settings, no matter how fine-grained and careful the research is. It is important to do both kinds of research, that is, to study individuals and small groups in the laboratory and in the field and to study large and ongoing groups in organizations. The purpose is not to discover exactly how variables and processes scale at

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Organizational Linkages: Understanding the Productivity Paradox each level, but to ensure that researchers are always attuned to scaling problems. Studying Groups in Organizations Understanding of linkages in group productivity might be more effectively advanced if the tests of models in this domain were more ambitious scientifically. For example, an Israeli study involved a true experiment in the field on the effects of selection on tank crew performance (Tziner and Eden, 1985). The study involved the assignment of 672 soldiers to 224 crews, using a complex Latin square factorial design to control for differential performance ratings by the 28 unit commanders. Assignment on the basis of ability was varied experimentally. No other interventions were made in the natural military environment, but considerable control was exerted on data collection to increase its reliability and validity. There were four waves of measurement using previously validated instruments. The study showed that ''spreading the talent around" is an inefficient way to distribute staff for interdependent groups, and the researchers were able to provide empirically supported advice counter to prevailing practices. A kind of sociological/microeconomic study needed in the domain of software productivity is exemplified by a comparative study of product development teams in the automotive industry (Clark et al., 1987). The unit of analysis in this study was a major car development project; three U.S., eight Japanese, and nine European auto companies participated in the research. The researchers collected data from the companies on 29 projects (6 in the United States, 12 in Japan, and 11 in Europe) involving the development of new sedans, micro-mini cars, and small vans introduced from 1980 to 1987. The researchers used questionnaires and interviews with project managers, heads of R&D groups, engineering administration staff, and engineers, as well as archival data on lead time, engineering hours, technology, subcontracting, and outcomes such as model prices. This study confirmed that Japanese projects were completed in two-thirds the time and one-third the engineering hours of the non-Japanese projects, and it reconfirmed that if schedules are kept under control, cost overruns also tend to be restrained. These results do not refute a time-cost trade-off. Rather, the study points to the potential importance of particular project strategies, kinds of project organization, leadership, and staffing.

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Organizational Linkages: Understanding the Productivity Paradox CONCLUSION A changing but mostly large proportion of the variance in the productivity of software development and other technical teams derives from how such teams coordinate their work. Without coordination, individual work cannot be integrated and turned into a group product. Technical teams use team design and communication to coordinate their work, each of which can be considered a linkage process. Research has contributed much to the understanding of the additive and interactive effects of team design and communication on coordination. They are, in part, substitutes for one another. Too much of either one, or of both, creates costs that outweigh the benefits of coordination. There are many unknowns in this domain, however, especially when one tries to predict the side effects and outcomes of linkages over time. The very meaning of productivity in software development has changed as approaches to coordination have changed. Improvements in IT and formal methodologies used for coordination have increased the scope of software engineering projects. In 1963 the Mercury space project required 1.5 million object instructions, whereas a space station of the 1990s requires at least 80 million. Today, software development teams are generally much larger, more diverse, better trained, and more dispersed than they used to be. Moreover, their tasks are more complex, more uncertain, and more fluid than they were in the past—all this despite improvements in hardware and software that have made individual work and coordination less onerous. Hence, as new technological and nontechnological approaches to linkages are developed, there are new efficiency and social consequences, including changes in one's expectations of team productivity. REFERENCES Abdel-Hamid, T.K., and S.E. Madnick. 1989. Lessons learned from modeling the dynamics of software development. Communications of the ACM 32:1426–1438. Adams, J.S. 1976. The structure and dynamics of behavior in organizational boundary roles. Pp. 1175–1199 in M.D. Dunnette, ed., handbook of Industrial and Organizational Psychology. Chicago: Rand-McNally. Aldrich, H. 1979. Organizations and Environments. Englewood Cliffs, N.J.: Prentice-Hall. Allen, T.J. 1977. Managing the Flow of Technology. Cambridge, Mass.: MIT Press. Banker, R.D., S.M. Datar, and C.F. Kemerer. 1991. A model to evaluate variables impacting the productivity of software maintenance projects. Management Science 17:1–18.

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