Time: A New Research Lens Author(s): Deborah G. Ancona ... · BARBARA S. LAWRENCE University of California Los Angeles MICHAEL L. TUSHMAN Harvard Business School We tend to use different

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Time: A New Research Lens Author(s): Deborah G. Ancona, Paul S. Goodman, Barbara S. Lawrence and Michael L. Tushman Source: The Academy of Management Review, Vol. 26, No. 4 (Oct., 2001), pp. 645-663Published by: Academy of ManagementStable URL: http://www.jstor.org/stable/3560246Accessed: 11-07-2015 15:56 UTC

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? Academy of Management Review 2001, Vol. 26, No. 4, 645-663.

TIME: A NEW RESEARCH LENS

DEBORAH G. ANCONA Massachusetts Institute of Technology

PAUL S. GOODMAN Carnegie Mellon University

BARBARA S. LAWRENCE University of California Los Angeles

MICHAEL L. TUSHMAN Harvard Business School

We tend to use different lenses-strategic design, political, and cultural-to understand how organizations function, depending on our theoretical orientations (Ancona, Kochan, Scully, Van Maanen, & Westney, 1999). Like different schema, each of these lenses leads us to focus on certain variables and relationships while ig- noring others. Focusing multiple lenses on a given phenomenon highlights different aspects of that phenomenon-much like the story of the blind men and the elephant. Finally, each lens suggests a different set of practices and solutions to managers. The goal of this special issue is to sharpen the temporal lens we use in con- ducting organizational research. In this final article we revisit and delineate this new lens and then identify some new and promising areas of temporal organizational research.

In the organizational behavior literature the strategic design lens focuses on designing strat- egies that "fit" the environment and the structure of the firm and on looking for further con- gruence among organizational components. Managers play the role of organizational archi- tects who design an organization in a way that improves its ability to adapt to its environment. The political lens focuses on power, influence, and conflict. Here, managers need to leverage power and negotiate across multiple interest groups. The cultural lens focuses on norms, meaning, artifacts, and values. Managers become the creators of meaning, using symbols and stories (Ancona et al., 1999).

The papers in this issue and the work that they represent give us a new lens-the temporal lens. Although time plays a role in each of the

other lenses, it is usually peripheral. In contrast, the temporal lens puts time and timing front and center. As such, although we can see the temporal lens as an additional component of the other three lenses- organizations are designed using temporal parameters (Ancona, Okhuysen, & Per- low, 2001), the power and influence styles used to create change depend on how quickly one has to act (Huy, 2001), and cultures differ with re- spect to temporal norms and expectations (Blount & Janicik, 2001)-it is also clear that the temporal lens can stand on its own. This lens offers its own set of variables and relationships, its own view of specific phenomena, and its own set of parameters to guide managerial action.

Using the temporal lens, we begin to think not just about processes and practices but also about how fast they are moving (cf. Eisenhardt, 1989; Huy, 2001), their trajectories over time (cf. Albert, 1995; Lawrence, Winn, & Jennings, 2001), the cycles they align with (cf. Ancona et al., 2001; Gersick, 1994; McGrath & Rotchford, 1983), and the historical positions they take on the contin- uum of time (cf. Blount & Janicik, 2001; Clark, 1985). We think not only about the individual personality but the individual's time urgency and time perspective (Conte, Landy, & Mathieu, 1995; Perlow, 1999; Waller, Conte, Gibson, & Car- penter, 2001). Is a person focused on the past, the present, or the future (Zimbardo & Boyd, 1999)? When these things are taken into account, work is not only designed to fit task interdependen- cies but also to fit group-member temporal orientations and to provide "flow" (Mainemelis, 2001). We seek to understand when an activity starts and stops and how a changing deadline

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646 Academy of Management Review October

impacts behavior (cf. Blount & Janicik, 2001; Lim & Murningham, 1994). Finally, we begin to examine the cultures of time-how monochronic versus polychronic temporal cultures affect the very nature of behavior-and what happens as we move across temporal cultures (Ancona et al., 2001; Bluedorn & Denhardt, 1988; Zerubavel, 1979).

In other words, the variables of interest in this new lens include timing, pace, cycles, rhythms, flow, temporal orientation, and the cultural meanings of time. As time can be allocated across activities, likewise, activities can be scheduled, fit to a deadline, accelerated, or shifted in time. Time can be objectively por- trayed and interpreted based on the measured, linear, forward-moving, and exact clock time. It also can reflect the subjective experience of each individual.

As we sharpen the temporal lens, it permeates our research methods. We begin to think about when and how often to measure key variables and how to measure the "correct" lag across causal variables (Mitchell & James, 2001). Fur- thermore, our data collection and analysis take on new forms. Graphs of activities mapped to time pinpoint the temporal location of phenomena, their pace, cycles, and rhythms as they repeat over time. We draw the interactions across temporal maps and the shape of changes over time (Ancona et al., 2001; Lawrence et al., 2001). Does the change move smoothly and monotonically over time, or is there a spiral that shifts exponentially where small swings grow to major gyrations? We can even draw maps that correspond to different cultural interpretations of time-for example, by juxtaposing a clock- time map, an event-time map, and multiple subjective-time maps.

RATIONALE

Why take on such a new lens? To make it worthwhile, the discomfort of learning a new way of thinking must be offset by some key advantage. Perhaps the best way to show this is through an example. Suppose a researcher is interested in understanding why the introduction of a new information technology system worked in one organization but did not work in another. Through a strategic design lens, the researcher comes to understand that the system worked better in an organization that was de-

signed around teams than it did in an individu- ally based organization. Through a political lens, the researcher sees that there was consen- sus for the change in the first company, whereas a high-level manager in the second company resisted the new technology. The cultural lens shows two different cultures-one based on ex- perimentation and risk and the other based on rejection of new ideas and change.

In the two situations each of these lenses missed some key temporal dimensions. In the first organization the new system was introduced just as some other organizational changes were taking place. Organizational members were ready for change and were ex- pecting their work to shift. In the second organization the new system was introduced when members were rushing to finish all of their projects by the end of the quarter. They did not feel as though they had the time to finish their work and learn a new technology at the same time.

Furthermore, members in the first organization had heard from colleagues at competitor firms that this new technology had already swept through the industry and resulted in real competitive advantage. In the second organization the technology was new to the industry and no fevered race to catch up to the competition existed. Finally, the CEO of the first organization had a very long-term planning horizon, and she saw the move to this technology as part of a larger trend to computerize particular processes. The CEO of the second organization, however, had a shorter-term perspective and was more concerned about the temporary disruption of work that the new technology would cause.

This example clearly shows why we should take on a temporal lens: it provides an important framework for explaining and understanding organizational behavior. Another advantage of this new lens is that it focuses our attention on new classes of independent and dependent variables. In this example, and in articles by Huy (2001) and Lawrence et al. (2001), the sequencing, pacing, and duration of change are critical variables that provide new theoretical insights.

Still another reason for selecting the lens is to sharpen our methodological approaches. The duration of X and Y and the temporal relationship between X and Y are fundamental questions, yet in their review of the literature, Mitch-



2001 Ancona, Goodman, Lawrence, and Tushman 647

ell and James (2001) conclude that these questions are addressed in very few empirical studies. It seems that an explicit consideration of these timing issues would improve the quality of empirical research in our field.

OBSTACLES TO THE TEMPORAL LENS

It may be instructive to ponder briefly why time is not embedded in our research. If it is such a pervasive phenomenon, why don't we see a well-articulated temporal view throughout organizational research? Why do researchers re- sist using a temporal lens?

One reason may be that most field studies of organizations are often "studies of convenience or opportunity." It is hard enough to gain organizational access. It is even harder to capture events over time using multiple measures. This not only takes time but additional resources and lots of cooperation. We are accustomed to get- ting in and out of organizations quickly. These additional considerations preclude the use of a temporal lens.

Similarly, much of the experimental work regarding organizational behavior is built around very short-term tasks. There are a lot of reasons for this short-term mentality: existing designs legitimated in the literature, the focus on control, and so on. In any of these cases, the short- term design makes it more difficult to use a temporal lens. It is tricky to include variables of duration, pacing, or sequencing in a fifteen- minute task (although it has been done; cf. Ger- sick, 1988; McGrath & Kelly, 1986; Waller, Giam- batista, & Zellmer-Bruhn, 1999).

Overall, perhaps the biggest impediment to using the temporal lens is that it is simply hard to do. The difficulty stems from several sources. First, we still have little theory about time lags, feedback loops, and durations, making it difficult to know when, for how long, and how often to measure key variables, even when we want to take on a temporal perspective. Second, we do not yet have all of the methodologies needed to measure complex temporal phenomena. Al- though we are skilled at detecting linear patterns and even quadratic forms, we are not yet able to readily detect spirals that increase and decrease over time. Third, we are not experi- enced enough to know how to choose temporal variables. For example, in a study of entrainment in software development teams, we had a

hard time deciding whether we needed to examine days, weeks, months, or years. In looking at entrainment patterns, we did not know which cycles to include (technology, customer, fiscal, and so on). Only through some difficult trial- and-error learning will we improve our use of the temporal lens. Last, some features of temporal research are inherently complex. For example, we have been trying to study teams that respond to attacks on the internet. One obvious problem is that we do not know when attacks (e.g., the spreading of viruses) will occur. Also, the definition of an attack is socially defined. We can identify attacks after they are publicly determined to be so, but we cannot easily determine when an incident is dismissed as not an attack. In addition, the groups that respond to these attacks are distributed, have a life of a few hours or a few days, and change in their composition from one attack to the next. This means we cannot study group behavior over time. These and other factors make using the temporal lens hard to do.

There also are broader, institutional reasons for the lack of focus on time. Doctoral disserta- tions are planned around short rather than longer stays. Tenure clocks focus new professors on shorter-term products. The application of time-based research requires new methodological approaches that may not resonate with the editorial boards of traditional journals. These and other institutional factors focus our attention on the short term, detracting from temporal issues.

The basic thesis of this issue is that the temporal lens brings new functionality to research. At the same time, understanding the reasons why so little temporal research exists may allow us to realistically assess our ability to stimulate this new form of research. This special issue will have little effect unless we (1) rethink how we do our research (e.g., we need to create new "contracts" with firms that will let us explore important temporal issues, to the benefit of both parties), (2) rethink some of our institutional arrangements, such as encouraging more time-based research in theses and our journals, and (3) experiment with new forms of data collection and analysis. If new members of our profession begin using temporal lenses in their research, the body of research will grow quickly.




LOOKING TOWARD FUTURE RESEARCH OPPORTUNITIES

In the remainder of the article, we chart some possible paths for future research. Once we adopt the temporal lens, what new opportunities for research does it reveal? The three paths de- lineated represent the views of the special issue editors. We are writing about what we think is important in exploring the temporal lens. There is no attempt to integrate these perspectives; rather, they represent important opportunities for research. Coupling these perspectives with the concluding sections of other papers in this issue gives the reader a nice set of directions for temporal organizational research.

In the first discussion Barbara S. Lawrence explores the idea of timing norms. In all aspects of organizational life, there are timing norms that govern a wide range of behaviors in organizations. Questions such as what timing norms look like, where they come from, and what their effects are form the basis of the discussion. Law- rence also pays attention to methodological issues in studying timing norms. Readers should be stimulated to think about how timing norms can be integrated into their own research or explored as a research topic in their own right.

The second discussion, by Paul S. Goodman, builds upon the Mitchell and James (2001) paper by examining in more depth their question about understanding lags in organizational research. That is, given X, when will Y occur? The goal is not just to help us be more explicit about when Y will occur but to focus on developing better theoretical tools to help us understand lags in organizational phenomena. Goodman draws examples from the literature on organizational change and organizational errors and pays attention to the timing between X and Y in single and multilevel contexts. In much of our current research, this timing question is not carefully considered. But if we adopt the temporal lens, it forces us to think about the issue.

In the last discussion Deborah G. Ancona and Michael L. Tushman use the lens to reframe how we think about temporal leadership. They examine top management teams as they entrain their organizations to technology cycles, manage across multiple time frames, create temporal architectures, and maintain a vision that provides an anchor for the strategic pacing of the firm.

TIMING NORMS: THE RHYTHM OF INTERACTION

Barbara S. Lawrence

Our temporal lens draws attention to timing norms, which people experience as shared, expected patterns of paced activity. Timing norms govern many activities in organizational life. Schedules for project completion (Ancona & Chong, 1996; Gersick, 1988), patterns of activity in work processes (Pentland, 1999), and career timetables (Lawrence, 1991) enable people to coordinate their behavior with that of others and help them create meaning out of action. Yet much of our literature focuses on personal experiences with time-for example, on an individual's time perspective and time urgency (Waller et al., 2001) or his or her experiences of timelessness (Mainemelis, 2001) and flow (Csik- szentmihalyi, 1990). Although the importance of individuals' encounters with timing norms is ac- knowledged in the literature (Blount & Janicik, 2001; Lawrence, 1988; Zerubavel, 1981), we know little about how people jointly create and experience such norms. This essay examines four important questions: What do organizational timing norms look like? What processes produce these norms and the meanings they acquire? What are the individual and organizational con- sequences of such norms? Finally, what tools can we use to study them?

One of the classic examples of timing norms is Roy's 1959 article, "Banana Time," which describes a small group of machine operators who construct their day around time-based rituals. Hourly, they take a short break named after the activity that occurs during the break: coffee time, peach time, banana time, window time, lunch time, pick-up time, fish time, and Coke time. These coordinated activities represent daily timing norms. When events do not occur at the appointed times, the workers experience disruption.

Several attributes in this example are worth noting. First, there is an event timeline. The patterned events-in this case, break times-occur daily. They do not take place on a weekly or monthly basis; rather, they recur every day. Sec- ond, the event timeline is paced. The events don't just transpire every day; they occur at specific times throughout the day. Third, the event timeline is a norm. We know from Roy's observations of the workers' coordinated behavior




that they have a shared understandings and expectations of these events. Work is disrupted when the prescribed events do not occur at the prescribed times. Finally, the meaning of the workers' timing norms is socially constructed. While events and pacing may be either emergent or scheduled, timing norms are always given meaning through social interaction.

Several theoretical questions about such timing norms need study. The first is What do timing norms look like? Answering this question involves exploring whether there are different types of timing norms, examining what "level of analysis" means when studying these phenomena, and defining what characteristics consti- tute a norm in the first place. One way of cate- gorizing timing norms is to distinguish between those that emerge through social interaction and those that result from formally scheduled events. Although the process of creating socially shared meaning is the same for both, they differ in how events and pacing are established. At one extreme, emergent timing norms such as those in "Banana Time" are purely emic (cf. Headland, 1990). Both the events and their pacing are socially constructed, created, and expe- rienced solely by the workers. At the other extreme, scheduled timing norms, such as those for a routine project's PERT chart, display an etic quality. Both the events and their pacing are established by an external authority. Does this difference alter the creation and experience of meaning around timing norms? Do emergent timing norms exert more control over people's behavior than scheduled timing norms? Emer- gent timing norms may be stronger because they require a level of buy-in that is unneces- sary for scheduled timing norms. Alternately, do scheduled timing norms exert more control over people's behavior than emergent timing norms? This might occur because the visibility of scheduled timing norms increases people's shared agreement about when events occur. The social construction of emergent timing norms may decrease people's shared agreement. When events are not formally scheduled, it is easy to disagree about when they occur.

A related issue is what "level of analysis" means in this context. Timing norms do not occur at the individual, group, and organizational levels of analysis, although they might be expe- rienced this way. Rather, they occur at the event timeline level of analysis, where the unit is de-

termined by the duration of the expected events (see Pentland, 1999, for a discussion of this dis- tinction). In "Banana Time" the described activities occur within a workgroup and could be studied as a group-level phenomenon. However, our concern is not so much the group as it is the expected, shared pattern of behaviors-a timing norm for which the level of analysis is one workday. Other examples include the pacing of work during a project, where the unit of analysis is the project, and sequences of jobs both within and outside organizations, where the unit of analysis is the career (Lawrence, 1991; Zerubavel, 1981). Similarly, organizational change processes develop timing norms, where the unit of analysis is the length of the change effort (Huy, 2001) or the time elapsed for new practices to become diffused and legitimated within an organization (Lawrence et al., 2001).

One problem that remains is to define what it means to observe a socially shared, expected pattern of paced activity. In "Banana Time" the answer is reasonably simple, since only four workers are involved during each one-day time period. However, in more complex examples, where the event timeline is longer, more people are involved, and the number of events is larger, it becomes more difficult to define each event. How many people must agree that a timing event is typical before it is considered to be socially shared?

Lawrence (1988) shows that people do not always perceive the timing of typical events in the same way. When managers in a large organization were asked to describe their organization's career timetable, their answers showed strong modal timing patterns: about two-thirds agreed on the ages when each career level was reached. However, this means that one-third dis- agreed. Is two-thirds agreement "enough" to say that the managers' perceptions are socially shared? What criteria should we use to decide when we reach sufficient levels of agreement? Moreover, if more or less agreement exists at different times within the timeline of an event, this suggests that deviation at one point may create more or less difficulty than deviation at another. The correct timing of events just before the curtain falls is more important to the success of a show than their timing during the middle.

The second question to be answered is Where do timing norms come from? In "Banana Time" we can guess that the norms were motivated by




workers' boredom with repetitious work and de- sire for meaningful social connection. The themed breaks emerged during typical eating and snacking times, when workers had time to interact with one another and to coordinate their efforts. Waller et al. (2001) suggest that a team's composition of individuals with differing time perspectives and time urgency affects its overall performance. It seems likely that timing norms-the shared time-related rules for coordi- nation-mediate this relationship. For example, teams composed of individuals with potentially conflicting time-related attributes, such as crammers with their high impatience and irrita- bility and visioners with their risk taking and focus on future goals, may fail because they have difficulty constructing timing norms that coordinate their work. What are the processes by which people negotiate timing norms in situations where individual differences in time urgency and time perspective make this difficult?

Timing norms also emerge from the patterned recurrence of events that become artifacts to which people respond. Institutionalization, learning, adaptive forces, and routines all influence the evolution of timing norms (Blount & Janicik, 2001; Lawrence et al., 2001). However, the processes by which these patterns help to create norms require further explication. It seems likely that there is close interaction between workers' observations of socially significant, re- curring events, such as promotions and project schedules, and their establishment of timing norms. People attempt to make sense of the events they observe and begin to associate each event with certain timing. Lawrence (1991), for example, found that employees develop shared perceptions of a career timetable, which appear to be based on their inferences about the firm's actual age distribution at different career levels. While the data show that the actual and per- ceived timetables are similar, people tend to create larger time differences between levels than appear justified. Thus, although these perceptions are socially shared, they represent an exaggeration of reality.

The third question is What are the effects of timing norms, both positive and negative? There are at least three mechanisms that predict the outcome of timing norms. The first is fit. Are the timing norms appropriate for the task at hand (Ancona & Chong, 1996)? An organization's timing norms for scanning and incorporating envi-

ronmental cues become institutionalized over time. If new technologies increase the speed of change within the environment, these original timing norms may no longer work. The organization's ability to adapt becomes impaired, reducing its chances of survival (Tushman & Romanelli, 1985). Ancona and Tushman discuss this problem in more detail later in this article.

A second mechanism is change. People experience varying degrees of difficulty in respond- ing to changes in timing norms. Blount and Janicik (2001) explore these potential effects through individuals' cognitive responses. They propose that people experience unwanted schedule changes more negatively when they are unexpected, uncertain in length, and have significant associated opportunity costs. In "Banana Time" a misunderstood joke changes the daily schedule. The banter disappears; the workers' participation in timing norms is de- stroyed. Roy describes the effect of this schedule change as "a succession of dismal work- days devoid of times and barren of themes" (1959: 165).

A third mechanism is comparison. People use event timelines to gauge whether they are on schedule and compare their progress to that of others (Abbott, 1990; Neugarten, Moore, & Lowe, 1965). Lawrence (1988), for example, found that people who are seen by others as ahead of schedule in their organizational careers are viewed more positively than those who are seen as on or behind schedule. Managers who are seen as ahead of schedule are more likely to get high performance ratings than would be expected by chance. Lawrence (1984a) discovered that individual perceptions mediate these effects. Managers who perceive themselves to be behind schedule have more negative attitudes toward work than other managers, even when those perceptions are inaccurate.

The final question is What tools might we use to explore questions about timing norms? All of the standard research restrictions apply to this question, and the answer requires both quantitative and qualitative methods. However, in addition to standard approaches, two additional tools should be considered-one conceptual and one empirical. The conceptual tool is historical perspective (Lawrence, 1984b). Based on an idea from Hume (in Mannheim, 1952; 277), plac- ing events in unlikely settings can generate unexpected insights, and the contrasts inherent in




historical comparison may be useful for time research. For example, in studying the human life span, scholars have identified consistent, age-specific developmental stages in adult- hood, punctuated in the middle by a midlife crisis (e.g., Jaques, 1965; Levinson, 1978, 1996). If we place this result in historical perspective, we see that some of the events typically associated with this punctuation are historically dependent (Lawrence, 1984b). Two hundred years ago, and without birth control, men in their middle years still had young children running around the house: thus, there was no empty nest. People did not expect to change careers and did not experience as many job opportunities as do people today. Moreover, the human life span has lengthened, changing the very definition of "midlife." Considering this timing norm in a different historical context generates questions about its theoretical boundaries and interactions.

An additional empirical tool with which we might explore timing norms is sequence analysis (Abbott, 1990). This analysis is particularly suited for timing norms, because it allows us to use event timelines as the unit of analysis. By using optimal matching, we can test hypotheses about the similarity of events and pacing during a specified duration. For example, Abbott (1990) studied the careers of 595 German musicians in the eighteenth century. In this study the events are the different positions a musician holds, the pacing is the time between positions, and the unit of analysis is the career. Abbott found dis- tinctive job sequences for organist, church, and court careers but not for town or opera careers. In the organizational literature Pentland (1999) discusses event sequences as stories, where the unit of analysis is the story and where the event timeline and the narrative it acquires provide the basis for much organizational reality. This methodology allows us to focus more broadly on timing norms and to study not only whether people perceive each event or the pacing between events but whether they see the entire event timeline (the "story") and understand its meaning to organizational life.

In summary, timing norms provide a temporal lens that requires us to think of all behavior, interaction, activity, and events as embedded within a paced, temporal context. For example, if we just compare the experiences of minority and white executives after they have been pro-

moted, we may miss the fact that the former get to the top by pathways that are paced quite differently than those of their white counterparts (Bailyn, 1980; Thomas & Gabarro, 1999). Individ- uals are not just promoted or socialized to new jobs; they are promoted or socialized within a timetable spanning their careers (or even their lives). If we examine only the success of venture capital funding, we may miss the fact that funding decisions depend on timing norms. In Sili- con Valley, venture capitalists expect an entre- preneur's timetable to be fast, furious, and young, whereas in Great Britain, investors expect a more moderate timetable (Cowe, 1998; Moody, 1998).

To use this temporal lens, we must first understand timing norms as a significant phenomenon in their own right. Consistently paced processes, whether they involve work or social activity, pervade organizational life. From the short event timeline of a single customer service call to the long event timeline of an industry's value chain (Slywotzky, 1996), timing norms describe the rhythm of interaction. They require us to view individual, group, and organizational interaction as paced and meaningful sequences of events, rather than single, isolated occur- rences.

UNDERSTANDING TIME LAGS Paul S. Goodman

An important contribution of the temporal lens and, more specifically, the Mitchell and James article (2001) is to refocus our attention on critical timing issues in organizational research. That is, if we are interested in the relationship between X and Y, we need to determine when Y will occur, given X. If a supervisor changes a reward system, when will an individual performance change follow? If a group has more authority and responsibility for decision making, when will its performance change? If a large organizational change is instituted, when will organizational benefits, such as increased productivity and customer satisfaction, be realized?

In this section this question of when is explored. Although Mitchell and James (2001) identify this key question, they are more interested in a host of methodological time issues, such as the duration of X and Y. They also provide the reader with a set of X and Y configurations,




including reciprocal causation between X and Y or changes in X and Y over time, and so on. They do not go into depth about when Y might occur and why it might occur. These are two fundamental questions at any level of organization research and are important issues for people who are interested in multilevel phenomena.

In this discussion some conceptual tools for understanding the lag between X and Y are ex- amined, beginning in a single-level setting and then moving to a multilevel context. The latter case is interesting because the question of when changes in a group's performance will affect organizational-level performance, if at all, is ex- amined. It has been relatively clear over the past decade that we need to look at organizational issues in a multilevel context (Chan, 1998). Time lags in a single or multilevel context have not been explored in any of the articles in this issue.

I begin this exploration by focusing on planned organizational change. When an organizational intervention is introduced, one question concerns when we might expect changes in outcomes such as productivity, quality satisfaction, and so on. Following is an example of this interesting theoretical and practical problem. I had the opportunity to conduct a large-scale multidisciplinary assessment of an organizational change over multiple years (Goodman, 1979). A very simple question was when to do the assessment. Looking at the organization over a three-year period, I saw no initial improvements, but noticeable improvements were visible fourteen months into the change. These positive outcomes were directly tied to the change effort. Between years two and three, however, there was a gradual decline of the improvements to the original baseline. The problem is that the time periods you select to measure Y will determine what you will learn. In the example above, if I had done solely annual measure- ments in this study, I would have observed no change.

The basic question, ex ante, is can we better understand when Y might occur? Our assump- tions are

* we can develop theories about time lags; * we cannot do point estimates-that is, pre-

dict exactly when changes in outcomes will occur (e.g., in four days or four months); and

* we can develop tools that will permit some dynamic forecasting and understanding of the lags.

Organizational Change-Single Level

Let's assume a multiple-system intervention is introduced at the departmental level of an organization. The intervention includes changes in rewards, such as pay for knowledge and profit sharing, recognition, communication, and decision-making systems. The intent is to increase productivity, quality, and employee satisfaction. How can we begin to understand the when question?

First, the nature of existing work characteristics can be an important tool in predicting the results of an intervention. A number of studies (e.g., Sitkin, Sutcliffe, & Schroeder, 1994; Ster- man, 1994) have shown that the impact of changes will be seen more slowly in certain work settings. For example, Sterman, Repen- ning, and Kofman (1997) indicate that change was much slower in a product development group than in a manufacturing group. In the latter unit tasks were more concrete and less abstract, the problems to be solved were often more visible, and outcomes such as increased productivity were easier to measure as com- pared to the product development group.

While the nature of work does not predict exactly when the outcomes are likely to appear, it does indicate that there will be differences across workgroups and gives us some idea of why these differences may occur. The idea is to think about the features of work in various settings and the interaction with specific organizational change interventions. In the above example the intervention was designed to improve team problem-solving skills, ultimately lower- ing costs and improving quality. In the manufacturing setting the visibility of problems, ease of measuring outcomes, and fast feedback cycles complemented the intervention. In a product design group or, similarly, in a research lab, this form of intervention will not be as good a fit. As work becomes more abstract, outcomes harder to measure, and the feedback cycles longer, the lag between changes and results is likely to be both longer and more difficult to predict. I introduce other work features in subsequent analyses to sharpen the role of such features in predicting when Y will occur.




A second tool that might help in our analysis of when is positive feedback cycles. In these cycles, changes in one variable lead to changes in a second variable, which lead to changes in the first variable. These cycles tend to accelerate in one direction, rather than to seek equilibrium. An important issue is the rate and direction of acceleration. Positive feedback cycles are going to help us predict lags and understand them in the following way: when the feedback cycle starts, the acceleration rates are slow; however, if we can track the cycle as it begins to accelerate faster, we will be in a better position to predict when Y will occur.

Consider the following examples. A multiple intervention is introduced in a department. Ini- tially, there are only a few participants who work on high-yield and immediate-results projects. Their initial success is broadcast to others, which motivates other employees to both participate in the change and produce results. The positive feedback cycle is underway. As the percentage of participants in the change increases from 0 to 10 percent to 30 to 40 percent, the force attracting more people to become involved in the change accelerates. Also, the actual impact on the outcomes will be greater. Change effects are not linear. The effects of moving from 30 to 40 percent participation are different, and possibly much larger, than the move from 0 to 10 percent. There is an accelerating feedback among participation, motivation, and results. Better results lead to greater participation and motivation, which, in turn, lead to even greater results. As these relationships, which are all measurable, begin to accelerate, the probability of a noticeable change in outcomes at the department level will be greater.

A second example of using positive feedback cycles to understand temporal lags concerns planned change involving problem-solving and continuous improvement teams. The role of problem-solving teams is to react quickly and effectively in solving problems. Continuous improvement teams focus on improving the system. Initial improvements by the continuous improvement teams should reduce the number of problems facing the problem-solving teams, which should permit faster response time and better-quality solutions to the problems these teams do observe. That is, the success of the continuous improvement teams improves both cost effectiveness and quality and, at the same

time, changes the ability of the problem-solving teams to improve cost effectiveness and quality. This is the beginning of a positive feedback cycle. Eventually, as the frequency of problems decreases, members of the reactive problem- solving teams can switch to performing continuous improvements, which, in turn, should further improve the organization's performance indicators. This is another example of a positive feedback cycle. The positive level of interaction between these two problem-solving processes should improve organizational outcomes, such as costs, quality, capacity, and utilization, which, in turn, should motivate the teams to operate at higher levels, which, in turn, should lead to organizational improvements.

In the above examples we cannot give point estimates about when the changes will occur, and we cannot make strong predictions before the changes actually begin. But understanding when a positive feedback cycle starts or fails to start, and the rate at which it accelerates, provides important information about when department-level outcomes will change. Another interesting research challenge is understanding when these cycles begin to decelerate.

By continuing the discussion of the nature of work variables and the positive feedback cycle, we can make the following observations:

* Positive feedback cycles will start earlier and accelerate faster in manufacturing units versus product development units. Features of the work (e.g., degree of structure, abstract versus concrete tasks, visibility of problems, and visibility of results) affect this timing.

* Delays in achieving significant benefits or results in product development units (com- pared to manufacturing units) may discour- age these workers from actively participat- ing in the change.

* "Quicker" results in a manufacturing unit may lead the organization to move more resources for change into that unit, which may further accelerate the change and pull resources away from product development units, which may need these resources more. This, in turn, will increase the lag in the product development unit (Sterman et al., 1997).

What has been accomplished with this analysis? First, we can now say more than just "make time lags explicit." Second, two concepts were introduced that will help us think about lags in a change context. These tools do not tell




us exactly when Y will change, but they provide some guidance on determining when a change in outcomes is likely to occur across different contexts.

Organizational Change-Multilevel

A different question is, given change outcomes at one level (e.g., group or department), when will change occur at the organizational level? This is an important theoretical and practical problem. In the literature on organizational change, there is some evidence that positive changes at one level do not necessarily have positive benefits at other levels (Goodman, 2000). Other studies (MacDuffie, 1995) have shown positive organizational benefits from change but give little, if any, documentation of how individual- and group-level performance contributed to these changes.

Let's go back to one of the earlier examples and assert that through technological and organizational interventions, there were productivity improvements in the firm's product development group. Today, the group can design more products in the same time. The question is when will the positive changes in this unit impact organizational-level outcomes, such as sales and profitability?

As with the single-level analysis, the nature of work is a good place to begin. First, the presence of constraints elsewhere in the value chain (e.g., in production or sales) will help us to understand whether changes in the number of new products a team can design will lead to changes in overall organizational sales levels. If there are constraints in manufacturing or sales capacity to absorb the new products, there will be no change in organizational-level indicators. How- ever, if there are no constraints, then the inherent time embedded in the manufacturing or sales cycles will identify at least the minimum time frame for organizational-level outcomes to occur. For example, if the manufacturing/sales cycle were three to four weeks, we would have to wait at least that long to see whether changes in product development led to changes in organizational-level outcomes.

Another nature-of-work variable takes the form of interdependence. In this example the workflow is sequential (Thompson, 1967), and intermediary activities take place between changes in product development and changes

in organizational effectiveness indicators. Any increases in the number of constraints or intermediary activities suggest that no effects or very delayed effects will occur. Compare this picture to one in which the form of organizing is addi- tive (e.g., units in a department store), where typically there are no constraints or intermediary activities between the unit and the larger organizational entity. Here, the time between changes in one unit's outcomes and the organization's outcomes may be limited only by the accounting control system (see Goodman, 2000: Chapters 4-6, for more detail).

Although the nature of work should provide some insights regarding the when question, some other concepts discussed earlier may also be helpful. For example, in the scenario of product development improvements in the presence of constraints and a long manufacturing/sales cycle time, it was indicated that changes in Y might not occur at the organizational level. How- ever, there are some possible compensatory processes. Perhaps this firm has strong reactive problem-solving and continuous improvement teams. If a positive feedback exists between these two types of teams, the aforementioned constraints might be removed, or both teams could focus on reducing cycle times. If constraints are removed and problems with cycle time are reduced, we may be better able to link the changes in the product development unit with the timing of organizational changes in sales and profitability. That is, although certain work features lead to long delays in Y, compensatory processes may speed up changes in the organizational outcome variables.

Some Observations

In this section only one content area-that of organizational change-has been explored. How generalizable is this approach to other areas? In a very different kind of literature-that of organizational errors-we could use a similar approach (Goodman, 2000). "Organizational errors" refers to deviations from standard practices that result in negative organizational con- sequences, such as the Three Mile Island crisis, the Barings Bank scandal, and so forth.

If we briefly look at some snapshots of the bankruptcy of Barings Bank, we see a trader operating well outside the explicit trading limi- tations-a clear deviation from standard prac-




tices. In addition, we see a bank providing funds to cover the positions of its trader, motivating the trader to further exceed the limits, that at the end amounted to more than 28,000 contracts worth $29 billion. In this case the positive feedback cycle discussed earlier accelerated the potential vulnerability of the bank by encouraging the trader to deviate further from accepted practice.

No one could have predicted the earthquake in Japan that led to a major decline of the Nikkei Index and the subsequent demise of Barings. However, the accelerating feedback cycle that put Barings at high risk was observable and avoidable. It was possible to predict that Bar- ings was at risk of experiencing a financial di- saster. That is, although we could not have predicted exactly when Barings would announce bankruptcy, and we could not have predicted anything when the trader began working for the bank, when the trader began to exceed the limits and the bank reinforced that behavior by providing funds to cover his positions, the like- lihood of bankruptcy increased. As the positive feedback cycle accelerated, so did the probability of Barings' demise.

Another potential limitation of this analysis is its focus on only a few concepts-the nature of work and positive feedback cycles. While there are, of course, other relevant concepts, the goal of this discussion is to make you think about how to apply this lag question in your own work. I do not believe there is a generic theory of time lags; each research problem and each context will evoke a different cluster of factors to help understand when Y will occur. However, the nature of the work and the positive feedback cycle can help us to understand time lag issues across research areas.

Conclusion

The purpose of this discussion has been to stimulate the reader to think more explicitly about the time lags between X and Y. "When will Y occur?" is a fundamental question at any level of organizational analysis. It also is a fundamental question across different levels of analysis. As we conduct more research in a multilevel context, the mapping of changes in Y at one level to changes in Y at a different level presents an exciting area, and the temporal lens becomes an even more important tool.

To advance our understanding of when, we must face at least two important challenges. First, we need to build "minitheories" about lags in our specific research. In this discussion I pre- sented the nature-of-work variables (e.g., visibility of results, constraints, form of interdependence) and positive feedback cycles as examples of tools that can help explain lags in the organizational change literature and organizational errors literature. While we need to create new theory, rich bodies of conceptual tools already exist in this literature for us to draw upon.

The second challenge is to design our research in a way that permits an examination of the when question. If we do quick, cross- sectional studies or ten-minute experiments, it is unlikely we can address the question. How- ever, this is not simply a call to do only longitu- dinal studies, although many others have made that request. Rather, this is an assertion that when Y will occur is a theoretical, interesting question, and whether we prefer to do field studies, experiments, or observational work in a quantitative or qualitative mode, we should build theories and design research to address that question.

TIME, TECHNOLOGY, AND DYNAMIC CAPABILITIES: TOWARD TEMPORAL

LEADERSHIP Deborah G. Ancona and Michael L. Tushman

We now focus the temporal lens on leadership and the temporal tasks of the senior team. The temporal lens centers our attention on cycles, time perspectives, temporal structures, and timeless visions. This view complements that of the three lenses mentioned at the start of this article and the top management team literature by bringing time into the foreground.

The three lenses suggest the role of architect for the firm (strategic design lens), power broker and coalition builder (political lens), and meaning creator (cultural lens) for the senior team (Ancona et al., 1999). The top management team literature focuses us on top team demography (cf. Bantel & Jackson, 1989; Williams & O'Reilly, 1998), team affect (cf. Staw & Barsade, 1993), team cognition (cf. Isenberg, 1988), team processes (cf. Eisenhardt & Zabaracki, 1992; Peter- son, Owens, Tetlock, Fan, & Martorana, 1998),




and strategy-making capabilities (cf. Burgelman & Doz, 2001; Hambrick, 1997). Yet, in these perspectives time has been relegated to the background (see Eisenhardt, 1989, for an exception).

Here we bring time into the foreground by examining the temporal leadership challenges senior teams face as they help their organizations adapt to changing environments. Teams enact temporal leadership as they entrain their organizations to technology and competitive cycles, manage across multiple time frames, and create temporal architectures for their organizations. In the context of these multiple and contrasting time frames, the senior team must maintain a timeless organizational vision that provides an anchor for the strategic pacing of the organization.

To anchor our discussion of time, cycles, dynamic capabilities, and temporal leadership, consider three brief examples. The senior team at Firestone Tire and Rubber Company had sub- stantial knowledge of and capabilities in radial technology and tires in the early 1970s. These competencies were rooted in their considerable experience with the European tire business. Fur- ther, this senior team had considerable knowledge of the changing demands of the American auto producers (Sull, 1999). Although radial technology was fundamentally different from the then-standard bias-ply technology, and radial tires had a significantly longer life span than bias-ply tires, the business and organizational models Firestone's senior team employed were the same as those employed in their bias-ply tire business. As a result, Firestone missed the radial tire revolution, underperformed the rest of the tire industry throughout the 1970s, and finally was forced to undergo radical change initiated by an external CEO in 1980.

While Firestone used its bias-ply organization to get into the radial business, Polaroid separated its digital camera business from its historically dominant film business; staffed this distinct unit with a new, digitally competent senior team; and had its senior leader report directly to the CEO (Tripsas & Gavetti, 2000). Yet, Polaroid's senior team, when given the opportunity and competencies to operate simultaneously in multiple time frames, retreated to its historically anchored film-based business model. Through 2001, Polaroid's digital business has floundered, even as the technology it pioneered has pros- pered in other firms.

Finally, USA Today built its newspaper fran- chise over a twenty-year period, based on re- porting, producing, and delivering a daily na- tional paper. By 2000, USA Today was the most widely circulated newspaper in the United States. It was, however, challenged by news de- livered instantaneously by its major competitors' internet-based organizations. Tom Curley, USA Today's CEO and the architect of its newspaper business, had to grapple with the question of "when is news news": is it once a day or is it second by second? Where other competitors floundered, Curley drove streams of innovation inside his organization and built capabilities such that both his paper and its dot.com busi- nesses flourished. By 2001, Curley and his senior team simultaneously managed both a mature 20-year-old newspaper organization and a young, entrepreneurial web-based news deliv- ery organization. In executing this proactive revolution, Curley changed five of the seven members of his senior team.

These examples illustrate the role of senior teams in building multiple time frames into their organizations and in shaping the rate of both technology cycles and organizational change. These examples also illustrate the con- sequences for organizations that do not build the capacity to operate simultaneously in multiple time frames. In this essay we further discuss these challenges and indicate the possibilities for future research.

Technology, Competitive Cycles, and Entrainment

Top management teams face temporal leadership decisions, including how fast to act and with which external cycles to coordinate. These decisions are linked to the concept of entrainment (McGrath & Rotchford, 1983). Entrainment entails adjusting the pace or cycle of one activity to synchronize with that of another (Ancona & Chong, 1996). In the case of top teams, the leadership challenge is to match the pace and cycle of organizational change to the competitive and technological cycles that are of strategic importance to the unit. For example, USA Today's Curley was able to build a senior team and organizational architecture that helped them entrain to multiple technology cycles, whereas the teams at Firestone and Polaroid remained




entrained to their historically dominant bias-ply and film-based cycles.

The literature on technology cycles is anchored in unfolding processes of variation, selection, and retention (McGrath, 2001; Tushman & Murmann, 1998). A technology cycle begins with a technological discontinuity (e.g., bias-ply tires, analog camera technology) that triggers periods of technological ferment. These fermen- tation periods are, in turn, closed by the emer- gence of new dominant designs or industry standards. Thus begins a period of incremental change that is ultimately destabilized by a subsequent technological discontinuity (e.g., radial tires and digital cameras).

Effective temporal leadership involves the entrainment of internal organizational change to external technology cycles. Major organizational changes must be entrained with technological discontinuities (Miller, 1994; Rosenbloom, 2000; Sastry, 1997; Tushman & Romanelli, 1985). For example, USA Today's move into web-based news on demand was coupled with sweeping shifts in the firm's structure, controls, systems, and culture. As previously mentioned, Curley replaced nearly all of his senior team during this time. In contrast, at Polaroid and Firestone, a stagnant senior team and organizational inertia stunted the companies' ability to mesh with the changing technology cycles (Tripsas & Gavetti, 2000).

The frame-breaking organizational changes required to entrain to shifting markets and/or technologies are often initiated by transformed senior teams (Ancona, 1990; Meyer, Brooks, & Goes, 1990; Romanelli & Tushman, 1994). While reorientations are risky, and often done incom- petently (e.g., Carroll & Teo, 1998; Henderson, 1993), persistence in the face of a changing innovation stream is even riskier. Further, if strategic reorientations are not done proactively, they are done reactively-as with Polaroid in digital cameras and Firestone in radial tires. Reactive reorientations (turnarounds) are more risky than proactive reorientations, because they must be implemented under crisis conditions and under considerable time pressure (Hambrick, Nadler, & Tushman, 1998; Rosen- bloom, 2000).

The most effective discontinuous changes are initiated rapidly and are directed by a senior team with an integrated change agenda (Nadler & Tushman, 1998). Proactive moves across inno-

vation streams, as at USA Today, are also associated with shifts within middle management (Kanter, Stein, & Jick, 1992; Pettigrew, 1985, 1987). If implemented incrementally, reorientations run the risk of being sabotaged by the politics, structures, and competencies of the status quo (Kearns & Nadler, 1992; Sabherwal, Hirschheim, & Goles, 2001; Virany, Tushman, & Romanelli, 1992).

Top management teams exhibiting temporal leadership do not simply entrain organizational change to key technology changes-they act to directly shape the timing and nature of those external cycles. Managerial actions shape and direct both technological discontinuities (e.g., Michelin's action with radials), as well as the closure on dominant designs (Cusumano, Mylonadis, & Rosenbloom, 1992). Equally important, eras of incremental change typically are punctuated by managerial actions to initiate product, process, or service discontinuities. For example, the initiation of radial tires, digital cameras, or instantaneous news was less a function of technological possibilities than it was a choice that senior teams made regarding when to act on technological discontinuities and/or when to act on creating a dominant design (Van de Ven & Garud, 1994). While there are different rates of technological change for high- versus low-technology industries, managerial and community action shape the timing, pacing, and nature of technology cycles (Chesbrough, 1999; Rosenkopf & Tushman, 1998; West, 2000).

Managing in Multiple Time Frames

Senior teams must build organizational capabilities that allow them to function in multiple temporal environments, each paced by fundamentally different technological and market time frames. These dynamic capabilities mean that incumbents must exploit their current product/market position even as they explore new product/market positions (Foster & Kaplan, 2001; Tushman & O'Reilly, 1997). These streams of innovation, each with their own technology/ market time frames, must be executed not sequentially or in rhythmic shifts, but simultaneously. For example, Tom Curley was able to incrementally innovate his traditional newspaper business even as he experimented with on- line and dot.com innovations. Similarly, to be successful over time, Polaroid had to become




ever more efficient in its analog business, even as it experimented with digital technology and business models (Tripsas & Gavetti, 2000).

Dynamic capabilities require that an organization have two temporal orientations: the present and the future (Brown & Eisenhardt, 1998). In the present, exploitation dominates through sustained incremental innovation and short-term learning that is entrained to the dominant industry design and its associated era of incremental technological change. In contrast, preparing for the future involves learning-by- doing, creating new product designs, and luck that allow the organization to drive possible new designs, architectural innovations, and/or product substitutes. Whether done internally or externally, through alliances or acquisitions, senior teams learn about alternative futures from entrepreneurial activity that allows entrainment to technological discontinuities and evolving technology cycles (McGrath, 2001). Thus, temporal leadership is rooted in the senior team's ability to operate simultaneously in the present and the future.

Organizational Architectures and Time(s)

Dynamic capabilities are rooted in streams of innovation-in simultaneously exploiting and exploring. These contrasting strategic, task, and temporal requirements must be reflected in differentiated organizational forms (Nadler & Tushman, 1998; Tushman & O'Reilly, 1997). Se- nior teams that exhibit temporal leadership design organizational architectures in a way that allows them to operate in multiple time frames.

Exploitation requires more highly structured processes, roles, and systems; more tightly con- trolled cultures; and a greater emphasis on hierarchy than exploration (Bradach, 1998; Levinthal, 1997). The time frames for exploitation activities are shorter than those for exploratory activities (Jaques, 1956). Further, for incumbent organizations, exploratory activities take place in the context of larger, older exploitation units, which have their own sense of both time and history (Milliken & Lant, 1991). Because organizational inertia is so strong, exploratory units must be physically and culturally distinct from the exploitative units and must have incentive structures rooted in different time horizons (Burgelman, 1991).

Ambidextrous organizational designs are composed of radically differentiated subunits with weak tactical integration but with strong integration within the senior team (Tushman & O'Reilly, 1997). These subunits represent different "time zones," each with its own culture of time, mapping of activities to time, and experience of time (Ancona et al., 2001). As such, the cultures, goals, and processes of these highly differentiated zones are inconsistent with each other. For example, the dot.com unit at USA To- day had a completely different set of structures, norms, and values. The unit's job was to report the news instantaneously, whereas the newspaper reported the news once a day. Curley and his new senior team separated the dot.com unit from the newspaper; helped it develop its own competencies, structures, and processes; and managed the integration across these units. In contrast, Firestone's radial unit was embedded in its bias-ply unit. The history, inertia, and time frames rooted in the incumbent technology pre- vented the company from successfully imple- menting the new radial technology.

By building ambidextrous organizational capabilities, the senior management team can maximize the probability that it will have both the expertise and the luck from which to make proactive, industry-shaping decisions, rather than react to others' activities. Ambidextrous organizations create options that allow the senior team to make informed bets on the future (Burgelman & Grove, 1996; McGrath, 1999). While correct strategic bets can be identified only in retrospect, managerial action within the firm- and with collaborators, alliance partners, and governmental agencies-can affect the ultimate selection of a new industry standard or the success of a product substitute (Cusumano & Yoffee, 1998; Rosenkopf & Tushman, 1998).

Integration Through a Time-Free Vision

Operating in multiple time frames to develop streams of innovation that entrain to different technology cycles requires organizational architectures composed of subunits that are themselves inconsistent with one another. Beyond the actions of the senior team, what prevents these internally incongruent organizational units from destroying each other? The answer is a clear, emotionally engaging vision that provides a strategic anchor from which the senior




leadership balances the contrasting requirements of different innovation streams (Hamel & Prahalad, 1994; Rotemberg & Saloner, 2000). Sim- ple, direct organizational aspirations allow the members of an organization to simultaneously host incremental and discontinuous innovations (Hurst, 1995; Nonaka & Takeuchi, 1998). Strategic integration is further reinforced by senior management's consistent behaviors in support of the vision through a small set of overarching core values and the use of common-fate reward systems for the senior team (Pfeffer & Sutton, 2000).

With a clear, consistent vision the senior team can support the internally contradictory organizational architectures associated with ambidextrous organizations and still be seen as consistent and credible. In the context of multiple inconsistent time frames, a clear vision and a few core values provide timeless anchors. For example, at USA Today the dot.com and newspaper units were united by Tom Curley's overall network vision and by shared values of fairness, accuracy, and trust. It is the senior team's role as a temporal leader to link these paradoxical requirements-to exploit and explore, to act both in the present and the future-together through its substantive and symbolic actions. Indeed, a key integrating device may be the coupling of multiple time frames associated with ambidextrous organizations with a vision and a set of core values that are time free.

Conclusion

Choosing a few temporal parameters and then applying them to the role that senior teams play in organizational leadership and the development of dynamic capabilities provide a fresh point of view to guide inquiry and practice. Tem- poral leadership on the part of the senior team involves the entrainment of organization change to key technology and competitive cycles. It involves managing across multiple temporal orientations, creating appropriate organizational architectures, and providing a timeless vision that both integrates and focuses temporal decisions. Temporal leadership is about mapping and shaping technology cycles and integrating across disparate units.

Entraining to key technology cycles, moving in multiple time frames, and creating temporal structures are, we believe, the keys to an organization's dynamic capabilities. Time has been in

the background of our field's theory and research, but it clearly needs to be brought to the foreground. By applying a temporal lens, we may discover a fundamentally new view of leadership, organizations, and dynamic capabilities.

Future researchers can clearly build on this work by further exploring the temporal variables described here or by examining others. In the area of entrainment, we have focused on technology cycles. Clearly, there are additional cycles that are of importance, including customer cycles, supplier cycles, and economic cycles. We still need to understand which cycles are most important. Furthermore, we need to understand how to entrain to multiple cycles with conflicting demands. Finally, we need to explore how many cycles can act as external pacers before the internal cycles of the firm overwhelm us with their speed and complexity. Or, put another way, what kinds of internal pacing and cycles allow for maximum entrainment to external ones?

In the area of dynamic capabilities and ambidextrous organizations, we have argued that strategic linkages must be present within the senior team to ensure strategic control and co- ordination of innovation streams. However, sub- stantial theoretical and empirical differences exist regarding how to effectively design and implement structures that enhance dynamic capabilities (e.g., Brown & Eisenhardt, 1998; Siggelkow, in press). Other models suggest that the entrepreneurial units must be completely separated or spun out from the business unit (Christensen & Overdorf, 2000; Klepper & Sleeper, 2000). Still other work suggests lower- level integration mechanisms, such as temporal crossing periods and temporal boundary objects (Ancona et al., 2001). Research is required to further understand when and under what conditions highly differentiated units should be integrated versus split out from the incumbent unit. Furthermore, what range of mechanisms can coordinate across these differentiated units?

In the area of vision, we have argued that a clear and consistent vision, reinforced by consistent managerial action, is needed. Future researchers need to focus on other properties of such a vision and the mechanisms that help organizational members internalize key values and behaviors. Future researchers also can address the difficulties of changing certain tempo-




ral elements of a culture while maintaining core values; how can we continue to offer high levels of service but do it faster and more efficiently?

While we have addressed entrainment, multiple time frames, ambidextrous organizations, and vision, future researchers could examine the interplay between the temporal issues we raise and other strategic, interpersonal, cultural, and design issues. In addition, by examining other temporal parameters (such as speed, rhythm, and scheduling), we can identify additional aspects of temporal leadership. Through such inquiry the concept of temporal leadership can take hold.

CONCLUSION

The goal of this article--and the issue as a whole-is to advance our understanding of time in organizational research. Adopting a temporal lens provides a new and powerful way to view organizational phenomena. It makes us speak in a different language (cf. Ancona et al., 2001), ask different questions, and use a different framework in the methodological aspects of our research (cf. Mitchell & James, 2001).

The use of the temporal lens can play out in two different ways. First, it provides a new way to understand phenomena where the pri- mary focus is on nontemporal issues. Your research may be about individual decision making, group performance, or organizational transformation. In any of these cases, using the language of time in addition to other ana- lytical methods will sharpen our contextual understanding of this research. While temporal norms may not be central to your research, they might provide new insights as powerful moderators or mediators. If you think carefully about the duration of X and Y and when Y might occur, the theoretical formulation and methodological approaches of your work will be different. Using the temporal lens in this way, it seems to us, is a minimum condition for advancing organizational work. The researcher will have a richer understanding of the "context of time," which should provide new opportunities for explanation and predic- tion.

At a second level, temporal lenses focus our attention on specific timing research. Through- out this issue, you have seen models of temporal research at the individual, group, and organiza-

tional levels of analysis. Here, the language of time is translated into specific temporal concepts, such as pacing, timing, and sequencing, which are the direct objects of research. In this article the discussion of timing norms and the discussion of temporal leadership are exem- plars of time-focused research. Taken as a group, the articles in this issue chart a broad range of research challenges surrounding temporal issues.

We must acknowledge that working at this level is much harder. There is not yet a rich set of theoretical and methodological tools regarding time. We argue, in this article, that there are many obstacles to temporal research. There are inherent difficulties in doing this research, as well as strong institutional forces that work against the development of this form of research. However, there are also tremendous opportunities for examining issues of time in current and future research.

While we do not expect this issue to spark a sudden proliferation of time-based research, we believe the temporal lens can be an important intellectual tool for both refocusing our work and creating new intellectual opportunities. This issue presents valuable ideas, perspectives, and models for doing research on temporal issues.

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Deborah G. Ancona is the Seley Distinguished Professor of Management and a

professor of organization studies at the Massachusetts Institute of Technology. Her research centers on team process and performance, time, and entrainment.

Paul S. Goodman holds the Richard M. Cyert Professorship and is a professor of

organizational psychology at Carnegie Mellon University. His research centers on

groups, organizational errors, and tools for multilevel research.

Barbara S. Lawrence is an associate professor of management at The Anderson School at UCLA. Her research focuses on organizational reference groups, norms, organizational demography, and social comparison processes.

Michael L. Tushman is the Paul R. Lawrence, MBA Class of 1942 Professor of Business Administration at the Graduate School of Business, Harvard University. His research focuses on the relations between technological change, executive leadership, and

organization adaptation, and on managing R&D laboratories.



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Time: A New Research Lens Author(s): Deborah G. Ancona ... · BARBARA S. LAWRENCE University of California Los Angeles MICHAEL L. TUSHMAN Harvard Business School We tend to use different