Watts, Duncan – The organizational spectroscope – Medium 20160401

Watts, Duncan – The organizational spectroscope – Medium 20160401

For several decades sociologists have speculated that the performance of firms and other organizations depends as much on the networks of information flow between employees as on the formal structure of the organization [1, 2].

This argument makes intuitive sense, but until recently it has been extremely difficult to test using data.

Historically, employee data has been collected mostly in the form of surveys, which are still the gold standard for assessing opinions, but reveal little about behavior such as who talks to whom. Surveys are also expensive and time consuming to conduct, hence they are unsuitable for frequent and comprehensive snapshots of the state of a large organization.

Thanks to the growing ubiquity of productivity software, however, this picture is beginning to change. Email logs, web-based calendars, and co-authorship of online documents all generate digital traces that can be used as proxies for social networks and their associated information flows. In turn, these network and activity data have the potential to shed new light on old questions about the performance of teams, divisions, and even entire organizations.

Recognizing this opportunity, my colleagues Jake Hofman, Christian Perez, Justin Rao, Amit Sharma, Hanna Wallach, and I — in collaboration with Office 365 and Microsoft’s HR Business Insights unit — have embarked on a long-term project: the Organizational Spectroscope.

The Organizational Spectroscope combines digital communication data, such as email metadata (e.g., time stamps and headers), with more traditional data sources, such as job titles, office locations, and employee satisfaction surveys. These data sources are combined only in ways that respect privacy and ethical considerations. We then use a variety of statistical modeling techniques to predict and explain outcomes of interest to employees, HR, and management.

Predicting team satisfaction

To illustrate the potential of these new data and methods, we analyzed the aggregate email activity patterns of teams of US-based Microsoft employees to predict their responses to an annual employee satisfaction survey. To protect individual employee privacy only email metadata was used (i.e., no content) and all identifiers were encrypted. Email activity and survey responses were aggregated to the manager level, where only managers with at least five direct reports were included, and only these aggregated results were analyzed. Our predictions therefore apply only to teams of employees who share the same manager, not to individuals.

We focused on three survey questions: did teams have confidence in the overall effectiveness of their managers, did they think that different groups across the company collaborated effectively, and were they satisfied with their own work — life balance?

We started by examining the data and found that that the vast majority of teams were pretty happy. Although this result is encouraging, as a practical matter HR managers are less interested in the large majority of happy teams than in identifying the small minority of unhappy teams. After all, it is the latter group on which HR needs to focus its resources. Rather than trying to predict the satisfaction level of every team, therefore, we focused on predicting just the teams in the bottom 15% — i.e., the least satisfied teams.

We considered two statistical models: first, a simple linear logistic regression model of the type that is widely used in quantitative social science; and second, a more complicated model from machine learning called a random forest [3]. Although random forests are generally less interpretable than standard regression models, making them less well suited to the explanation tasks typically found in the social sciences, they can capture nonlinearities and heterogeneous effects that linear models ignore and therefore often perform better at prediction tasks.

In our case the random forest performed much better: if it predicted that a team was in the bottom 15%, it was correct (across all three questions) between 80% and 93% of the time; in contrast, the linear model was correct at best 27% of the time. Critically, a ‘‘baseline’’ model that used only data on respondents’ position and level in the company — i.e., no email activity features — performed between 20 and 40 percentage points worse. In other words, the email activity data added large and significant value over and above the kind of data that HR managers already have (see Table 1).

Table 1. Precision of the random forest model (column 3) compared with a standard logistic model (column 1). Column 2 is for a random forest model that does not include email activity, but does include other features such as job category (engineer, product manager, sales, etc.) and level.

Table 1 also shows the particular features of email activity that were most predictive of low satisfaction. For work — life balance, it was the fraction of emails sent out of working hours: more is worse. For managerial satisfaction, it was manager response time: slower is worse. And for perceptions of company-wide collaboration, it was the size of the manager’s email network: smaller is worse.

At first glance these findings may seem unsurprising, but this reaction misses the point. To see why, consider work — life balance. Although it makes sense that sending an unusual volume of email outside of normal working hours would correspond to low satisfaction, it would have made equal sense that satisfaction was related to the overall volume of email sent or received, or to the relative distribution of email over days of the week. But none of these other factors were useful for predicting low satisfaction. The point, therefore, is not so much about finding results that are surprising and counterintuitive, but about ruling out all the plausible, intuitive explanations that are not in fact correct.

Another non-obvious finding is that different types of teams had different thresholds for what counted as a “bad” volume of out-of-hours email. The number of out-of-hours emails that predicted an unhappy sales team, for example, was different from that of an unhappy engineering team. Again, this result isn’t surprising (once you know it), but it would have been difficult to guess in advance. This result also highlights the advantages of using a complicated model over a simple one: although in general we believe that, all else equal, simple models are better, when effects are highly context-dependent, complex models can shine.

Lessons for managers and for science

Insights like these are of immediate interest to both employees and managers. In particular, because predictions based on email sending behavior can be made in real time, HR can obtain more timely feedback than surveys allow. Moreover, modern statistical modeling approaches such as ours can help managers in complex situations where many different factors could be at play — e.g., by showing which of many plausible explanations are supported by the evidence, and by cautioning against “one size fits all” solutions. Finally, employees could also benefit from tools that highlight help them quantify their work activity in the same way that personal fitness trackers help them quantify physical activity.

More generally, our results show how the combination of novel sources of digital data and modern machine learning methods — that is, computational social science — can yield insights that would not be available with traditional data sources and methods. Over time, we hope to expand this approach from the specific case of predicting team satisfaction to a much wider range of questions regarding teams, divisions, and even entire organizations.

Finally, it is worth emphasizing that deriving these kinds of insights requires a lot of care. To perform our analysis, we combined three datasets — email activity, the org chart, and poll results— that were collected in different ways at different times by different people. Joining these data sets in a manner that respected privacy and ethical concerns required significant effort and cooperation across teams, which in turn required us to clearly specify, and justify, our substantive research questions and goals. Likewise the realization that we needed to focus on only the least satisfied teams required us to think carefully about the structure of the data and about our research questions. For all the excitement about “big data,” in other words, computational social science works well only when powerful computation is matched with careful social science.

1. Burns, T. and G.M. Stalker, The management of innovation. 1961, London: Tavistock Publications. v, 269.
2. Lawrence, P.R. and J.W. Lorsch, Organization and environment; managing differentiation and integration. 1967, Boston: Division of Research Graduate School of Business Administration Harvard University. xv, 279.
3. Breiman, L., Random forests. Machine learning, 2001. 45(1): p. 5–32.

Communication and change in organizations (readings)


Communication and change in organizations

After Salem, P – The seven communication reasons organizations do not change (2008).

Organizational transformation involves changes in core features e.g. goals, authority relationships and organizational structure, markets, and technologies (Aldrich and Ruef, 2006; Rao and Singh, 1999). Management has made efforts to direct discontinuous second order change strategically (Nadler et al., 1995). These strategic initiatives have been successful only  about one third of the time (Cameron and Quinn, 1999; Meyer et al., 1995). Enduring improvement appears to be impossible without a change of culture (Cameron and Quinn, 1999, p. 9).

Culture is the set of embedded communication practices that distinguishes one group from another. Accomplishing transformational change involves replacing current competencies, routines, and rituals with other stable communication practices. Strategic initiatives whose purpose was to change the organization’s culture have succeeded less than 20 percent of the time (Smith, 2002). What this data suggest is that most legitimate systems – the established cultures – are robust and resistant to strategic initiatives. What management intends as transformational change may be integrated into the organization as simple adaptations.

The complexity of organizational change

The study of change in social systems has a long history. In the 1960s, Buckley (1967, 1968) argued that social systems continually experience natural tensions due to the variety in the system’s environment, to the variety and behaviors of the members within the system, and to the interaction between external and internal sources. The tension stimulates learning and the regrouping of components or actions. The changes may assist adaptation to various tensions, but they may also lead to goals, states, etc. the system has never experienced (Buckley, 1968). Buckley thought of society as a “complex adaptive system,” and he was concerned with how systems developed properties to insure their viability (Buckley, 1967, 1968, 1998). In 1968, Buckley hoped developments in mathematics would soon match the conceptual richness of these ideas.

Advances in non-linear dynamics would appear to be developments Buckley had desired. The two most recent bodies of work concern chaos theory and complexity theory (Holland, 1995; Kauffman, 1993, 1995). Both theories assume system interactions are part of an auto-catalytic process. Auto-catalytic or self-reinforcing processes have three properties:

  • the processes are iterative or repeated;
  • the processes are recursive, meaning the outputs for one iteration are the inputs for the next; and
  • the processes are multiplicative (i.e. non-linear or non-additive), suggesting that small effects may accumulate or aggregate to have bigger impacts later.

When researchers model processes as auto-catalytic, they employ formulas or algorithms with mathematical relationships that reflect these properties.

Weick’s sense-making model describes organizing as a function of such an auto-catalytic process. Sense is a function of a cue plus a frame plus a connection between the frame and the cue (Weick, 1995). However, the framing cycle does not occur once. It occurs repeatedly until individuals remove equivocality and make         plausible sense (Weick, 1979, 1995, 2001). When individuals communicate, they may make sense together, and so, communication draws attention to the social and cultural aspects of making sense. Sense-making involves a framing process that may reflect or may change culture. The frames may come from culture, and local sense-making may accumulate to alter cultural frames. When transformational change occurs, there are changes in cultural frames and communication practices.

The formulas also contain parameters or constants that determine the intensity of properties and, especially, the intensity of the interaction between properties or agents. Organizational researchers generally regard parameters as environmental conditions or as aspects of the strategic course of an organization (Thietart and Forgues, 1995). Common organizational parameters include:

  • leadership
  • the diversity of membership and organizational processes, and
  • the richness of the connectivity between social actors (Stacey, 1996).

These are common parameters, and a change in the critical values of these parameters would be necessary to reach a state where transformation was possible. These changes often accompany or are part of changes in core features mentioned in the introduction. Changes in second order parameters are inherent in major strategic initiatives and should produce transformational change. The initiatives, even the changes in second order parameters, have not produced the intended outcomes very often. Mostly, there was no change in the organization’s culture.

Chaos and complexity researchers refer to the result of one iteration of an auto-catalytic process as a phase and any pattern in a sequence of phases as an attractor. For example, a phase may be the configuration of agents after one iteration, and a repetition in a sequence of configurations would suggest an attractor. The pattern of phases around an attractor is a basin of attraction. Of course, the parameters, parameter values, and nature of the process itself limit what phases are possible. Chaos and complexity researchers refer to the range of all possible outcomes as a phase space. A particular account of a particular event would be comparable to a phase, and a pattern in several accounts would be an attractor (Stacey, 2001, 2003). The various accounts that lead to an organizing theme, the attractor, and the variations in the central theme would be part of a basin of attraction. A universe of discourse would be comparable to a phase space. One way of interpreting the failed efforts at transformational organizational change is to regard these strategic efforts as maintaining or just modifying the old organizing themes within the original universe of discourse.

A bifurcation point is a state of turbulence where second order change may be possible. The system may now move between at least one old basin of attraction and one new one (Polley, 1997). It is a time of great tension between the old and the new, and the system must “choose” its future (Prigogine and Stengers, 1984). Once at a bifurcation point, the system may move to one of five states:

  1. The old may dominate, and the system may return to the previous stable state
  2. The new may dominate, and the system may move to a new stable state
  3. The system may maintain a tension and oscillate between two or more states. This pattern may be a relatively stable pattern of oscillation between points, but it may involve so many points in a cycle that it may appear to be unstable.
  4. The system could pass through many bifurcation points, alternating patterns of stability and instability and leading to evolutionary changes in which one transformation builds on previous ones. A particular bifurcation point may be part of a transformational instability.
  5. The system might have passed through many bifurcation points leading to a continuous  unstable pattern. What appears to be random is limited or bounded by the auto-catalytic processes. The system’s “choice” at a particular bifurcation point depends on the general nature of the system, the history of past “choices,” parameters and their values, and the nature of auto-catalytic processes. Studying an organization at a bifurcation point would be an excellent way to learn about communication and organizational change.

Stacey (1996) described organizational change as conflict between a legitimate system and a shadow system. In this model, the natural tensions of everyday life drive an informal and emergent structure, the shadow system, and the accumulation of tensions may challenge the already dominant culture and formal structure, the legitimate system. Stacey’s description parallels Buckley’s (1967) older description of social change involving in- and out-groups. A change in parameter simply speeds the process and movement to one or a succession of bifurcation points.

The complexity of organizational change involves an accumulation of differences. Social actors construct novel behaviors or behaviors repeated with some modifications as part of auto-catalytic processes. Some auto-catalytic processes encourage greater novelty or modification while others discourage deviation. Various behaviors occur in relatively stable or unstable conditions. The tensions between these behaviors and the conditions are the basis for the relative stability of the social system, the system’s structure. Communication patterns may suggest underlying organizing themes, attractors, or there may be permutations around central themes, basins of attraction. The local activities of social actors may disrupt the tension and lead to a state, a bifurcation point, where the system may change its nature. That is, alternative basins of attraction may develop. The localized variety within the system, the shadow system, may naturally accumulate to challenge the established structure and process, the legitimate system. However, there may be some external disruption of parameters that stimulates the shadow system to challenge the legitimate system.

The communication reasons organizations do not change

Insufficient communication

When organizational members communicate during intense change, they will generate organizing themes about uncertainty or a lack of information about specific changes. Uncertainty is an inability to describe, predict, or explain (Salem and Williams, 1984), and complaints of inadequate information are common in organizations (Daniels and Spiker, 1983). However, information is not part of artifacts such as memos, reports, or websites. Organizational members create information and knowledge as they make sense (Salem, 2007; Weick, 1995). Communication is a social process in which individuals can make sense together, and artifacts are only an opportunity for making sense, an opportunity for conversation. Complaints about inadequate information are complaints about the lack of opportunities to make sense together.

Many approaches to change assume management will direct and control the process (Miller and Cardinal, 1994). Often, it is impossible to involve many people in making everyday decisions, and managers or a small group tend to simply “download” decisions to others. Management expects compliance, but this approach fails to gain acceptance or support for routine management decisions or decisions during change processes (Clampitt and Williams, 2007; Robbins and Finely, 1996). Commitment to transformational change will not happen without communication, and lots of it.

Uncertainty, a lack of information, and a sense that there were few opportunities to reduce uncertainty were common themes in all the studies.

Organizations fail to change when too many people believe they are not getting enough information about the changes. It may be impossible to meet everyone’s information needs. However, the need to know more is less disruptive when there are many opportunities for everyone to make sense of the changes. Without the entire organization participating in conversations about change, transformational change will not occur.

Local identification

When organizational members communicate during periods of intense change, they will generate organizing themes about identification. Self-concept is the organized set 339 of perceptions one has about one’s self (Cushman and Cahn, 1985). An aspect of self-concept is self-identity, and the organization of various self perceptions associated with organizational roles constitutes one’s organizational identity (Pratt and Foreman, 2000). Describing one’s self as female is part of one’s self-identity, but describing one’s self as a department head is part of one’s self-identity and also part of one’s organizational identity. A person may have multiple identities (Mead, 1934/1962), and multiple organizational identities (Cheney, 1991). For example, an organizational member may identify one’s self by one’s professional role, as part of a sub-unit, a unit, a department, a division, the company, or as a worker.

Individuals develop self-perceptions through interaction (Mead, 1934/1962), and organizational identification emerges in the communication members have with each other about each other. There are many ways members’ communication works to develop identification (Cheney, 1991; Lammers and Barbour, 2006; Scott, 2007). One avenue during change efforts is to develop a shared vision and another is to involve many in strategic planning processes (Robbins and Finely, 1996; Senge et al., 1999). Change will disrupt organizational identities, and members want to know what they will become and what the unit, division, or organization will become. Without communication that builds global and shared identification, members will resort to the older more local and independent identities.

Global distrust

During periods of intense change, organizational members will communicate about trust. Trust is an expectation, assumption, or belief of positive or non-negative outcomes that one can receive from another person’s future actions during uncertainty (Bhattacharya et al., 1998). Uncertainty implies vulnerability, and most contemporary definitions of trust include some belief in the positive intentions, behavior, or outcomes of another (Rousseau et al., 1998). Distrust is characterized by fear, skepticism, cynicism, and wariness (Lewicki et al., 1998). Mistrust, undefined in the literature, would be an inability to predict the value of engaging with another.

When organizational members distrust the agents of change or each other, strategic initiatives fail. Employees often distrust management during periods of planned change. A common way for members to express this distrust is to discuss organizational politics and the distrust members feel about how management might distribute resources.

Lack of productive humor

Humorous communication increases during intense organizational change. Humor is a form of communication that promotes laughter from discordant meanings or relationships (Duncan, 1982). Humorous communication works as a reframing mechanism (Wendt, 1998), and humor can be a norm and value as part of the culture (Trice and Beyer, 1993). Humor can be productive in the workplace by bringing social actors closer together, reducing stress, managing paradox, and building cohesiveness, but it can also be negative by being self-defeating, derisive, or part of anger (Geddes and Callister, 2007; Malone, 1980; Martin et al., 1993, 2003; McPherson, 2005; Romero and Cruthirds, 2006; Stacey, 1996). Organizational members can encourage or discourage change by how they use humor.

Poor interpersonal communication skills

The level of interpersonal communication skill will affect the direction of organizational change. Communication competence is an ability to accomplish goals with appropriate communication behaviors (Spitzberg and Cupbach, 1984).

Appropriateness refers to meeting the normative expectations of others in the social situation as well as using those behaviors most appropriate for the task at hand. Competence requires the performance of various communication skills and the perception of others that the performance was appropriate.

Three skills appear on most lists of communication skills related to competence:

  1. Responsiveness refers to those behaviors that attempt to understand the other and to communicate that understanding. These include verbal behaviors such as  paraphrasing, validating, and asking questions and nonverbal behaviors such as head nods, vocal encouragers, and back channeling.
  2. Openness refers to those behaviors an actor employs to improve the other’s understanding of the actor. Behaviors such as using personal language, being specific about experiences and feelings, and self disclosure may be part of openness.
  3. Flexibility is the ability to change communication behaviors in different situations. Being flexible means adjusting to different goals, tasks, people, and situations, and the competent communicator makes these adjustments in an appropriate way.

When members lack communication skills, communicating about change will be more difficult. Members will have difficulty making sense of change, feel greater uncertainty, identify less with the organization and its changes, and distrust others more.

Conflict avoidance

Intense change is a turbulent time, and the likelihood for conflict increases. Conflict is an expressed struggle over perceived differences (Folger et al., 2005). Individuals manage conflict in one of three general ways. Avoidance means never having to confront differences directly. Competitive tactics involve direct confrontations but may vary from argument about positions and ideas, to bids and counter offers, to verbal aggression and even violence. Integrative communication involves creating common goals, offering to help each other achieve individual goals, brainstorming to develop action plans, and creating common systems of accountability. People perceive integrative conflict communication as competent, competitive or controlling strategies as effective but inappropriate, and avoidant strategies as least competent (Gross et al., 2004).

In a time of intense organizational change, confronting differences is important. Conflict should exhibit a clash between newer conversational themes and older ones. Such conversations provide an opportunity to test strategic initiatives against older assumptions and expectations, and these conversations are the means for constructing emerging alternative identities, relationships, accounts, routines, and values (Griffin, 2002; Shaw, 2002). Members contrast emerging communication practices with older ones.

An inappropriate mix of loose and tight coupling

Getting to a bifurcation point capable of producing transformational change involves an accumulation of differences and a natural loose coupling of current behaviors. But when the system moves to a transformed state, it exhibits tighter coupling and the emergence of order from disorder. The development of some hierarchy of activity is common when systems emerge from transformational phase transitions such as the bifurcation points far from equilibrium (Barabasi, 2002). Decentralized structures may be best at initiating innovation and change, but there must be some centralization to implement (Rogers, 1995). The combination of factors noted above suggests organizational members may resist transformational change by loosening the couplings between each other as they cope with the initial disruptions of change and failing to construct tighter couplings as part of moving to a different set of routines and rituals.

Organizational members can decouple their system in three ways (Kingdon, 1973):

  1. Fragmentation is a process of decoupling goals. Fragmentation is a process of emphasizing local or individual goals at the expense of organizational wide segmentation is the global distrust, primarily of management. The distrust plays a role in the tendency to avoid conflict. Organizations experiencing dissociation and segmentation will have a difficult time accomplishing a unified effort. goals, and fragmentation is the last type of decoupling to occur.
  2. Dissociation is a process of decoupling horizontal units. There was evidence of dissociation in the tendencies to localize identities. Members identified with their local units and had little appreciation for other units or the whole.
  3. Segmentation is a process of decoupling vertically.

Sustaining transformational change involves the proper mix of loose and tight coupling.


Communication during failed change efforts seldom involves enough communication opportunities, lacks any sense of emerging identification, engenders distrust, and lacks productive humor. These problems are compounded by conflict avoidance and a lack of interpersonal communication skills. Members’ communication decouples the system, sheltering the existing culture until it is safe for it to re-emerge later. No change in the intended direction is likely.

Results from this research point to the limitations of management communication and impersonal communication. Much of management literature assumes an exclusive place for management, as if managers were not a part of the organizations they manage. There is also the tendency to associate communication with the production of a message, as if finding the right words in the announced change would automatically bring commitment to the changes. Changing an organization’s culture is a task in and of itself, a task in addition to the tasks already going on as part of the routine business of an organization. Changing the communication practices of organizational members involves a give-and-take in which the change agents might change. Change is a messy business, and transformational change will not happen unless management is willing to tolerate the ambiguity and the sense that emerges in communication.

Results also reinforce the importance of communication skills in hiring practices. Communication occurs when two or more people in a social relationship create messages to make sense of the episodes they are creating. The process is inherently interpersonal. Hiring people with basic communication skills and training people in these skills not only improves the chances for sustaining a vibrant organization, but it also assists people in the rest of their lives as well.


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Culture, innovation, and collaboration in organizations (readings)

G Schmidt & L Jackson