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Bracketing off the actors: Towards an action-centric research agenda
Information and Organization 27 (2017) 137–143
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Information and Organization journal homepage: www.elsevier.com/locate/infoandorg
Bracketing off the actors: Towards an action-centric research agenda
MARK
Brian T. Pentlanda,⁎, Alex P. Pentlandb, Roger J. Calantonea a b
Michigan State University, Broad College of Business, East Lansing, MA, United States Massachusetts Institute of Technology, MIT Media Lab, Cambridge, MA, United States
AB S T R A CT Widespread digitization is creating new sources of data that record sequences of actions and events. These action sequences can be used to trace coherent streams of activity in social, economic and business processes. These sources of data, along with new computational methods, create an opportunity to visualize, analyze and compare patterns of actions as a unit of analysis. This paper discusses the connections of this idea to a variety of related fields and maps out some first steps of an action-centric agenda for research.
1. Introduction Social, economic, and business research has traditionally focused on familiar units of analysis, such as individuals, groups and firms. In this paper, we suggest that patterns of action offer a useful addition to our repertoire as a unit of analysis. Action patterns can be represented in many ways. Here, we focus on action networks, a class of directed graphs where the vertices represent categories of action and the edges represent sequential relations between those categories. By tracing associations between actions, rather than associations between actors or actants, action networks offer a straightforward way to study processual phenomena, such as routines, practices, projects, processes and services, as patterns of action. To see how action networks can provide a novel perspective, consider a phenomenon that has attracted a lot of attention in research on information systems: technology adoption and use (Davis, 1989; Venkatesh & Davis, 2000). The conventional perspective starts with a focal actor who perceives the utility and ease of use of a new tool or a new feature. That actor may also perceive various costs and benefits, including social influence from other actors. Together, these factors shape the intentions of the actor. There have been many variations and refinements over the years, but all of these approaches address the question: what will the actor do? Even in research that considers an array of alternative actions situated in a rich, ecological context (e.g., Jung & Lyytinen, 2013), the focus is on the reasoning and motivation of the actor. Of course, actions do not happen in isolation. Most likely, in practice, actors are already doing something. If so, then actions involving the adoption or use of technology may be related to current actions (and past actions) as part of a larger pattern, project, process, practice or routine. Rather than asking about the actors, we can ask about the actions: what will happen next? And rather than seeing the actions as isolated, independent events, we can see them as related and interdependent, a perspective that is consistent with the assumptions of “strong process theory” (Chia & MacKay, 2007; Hernes, 2016; Langley & Tsoukas, 2016; Mesle & Dibben, 2016). By focusing on actions, and relations between actions, we can gain a novel perspective on processual phenomena. In principle, we can predict what will happen next without any information about actors.
⁎
Corresponding author. E-mail address: [email protected] (B.T. Pentland).
http://dx.doi.org/10.1016/j.infoandorg.2017.06.001 Received 25 August 2016; Received in revised form 16 June 2017; Accepted 17 June 2017 1471-7727/ © 2017 Elsevier Ltd. All rights reserved.
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2. Motivation One motivation for this approach is opportunistic: sequential trace data from digitization of economic and social processes in increasingly available, and improved computational tools make it possible to analyze this data in new ways. Within the context of formal organizations, there are a number of data sources that seem especially promising, such as electronic health records, customer relationship management systems, design processes supported by PLM (Product life-cycle management software), and workflow systems that support a wide range of business processes (van der Aalst, 2011). The deeper motivation is that focusing on actions (rather than actors or actants) provides a new perspective on the phenomena we study. The actor-centric perspective is like Ptolemaic astronomy: the actor is in the center of the universe. This perspective is pervasive. Psychology and behavioral economics are focused on motivations and decisions of actors. In social networks, we trace relations between actors (Lazer et al., 2009). In heterogeneous networks, we trace relations between actants (Latour, 2005). The incorporation of materiality into our thinking has not dislodged actants from this privileged place. Whether we see the social and material aspects of actants as inseparable (Orlikowski & Scott, 2008) or merely imbricated (Leonardi, 2011), the actants remain in the foreground. Everything revolves around the actants. Without question, this line of theorizing has led to an increasingly nuanced understanding of actors as heterogeneous, sociomaterial ensembles. However, like Ptolemaic astronomy, the actor-centric view has become increasingly complicated. We suggest that it may be helpful to bracket off the actors, at least temporarily. When we bracket off the actors, debates about the nature of actors/actants (Niemimaa, 2016; Ramiller, 2016) and models of actors' motivations or intentions become irrelevant. When we put actions in the foreground, distinctions like people vs. things, action vs. behavior and agency vs. necessity fade into the background. These distinctions are useful, but they are old, and they seem tired. Perhaps they could use a vacation. 2.1. Actions are an interesting unit of analysis Actions offer an alternative perspective, especially in the context of information systems (Aakhus, Agerfalk, Lyytinen, & Te'eni, 2014; Lyytinen, 1985).1 To use actions as a unit of analysis, we need to be clear about their distinctive properties, especially with respect to the formation of action networks. 2.1.1. Actions are situated in an indexical context Actions are always indexed (and therefore bounded) by the specifics of when and where they occur: time (now, later, …), place (here, there, …), subject (me, you, …), and so on (Barnes & Law, 1976; Heritage, 1984). As Hernes (2016, p. 602) suggests, actors are just another aspect of context: people and other objects are “features of events.” The question is, when are we willing to say that two instances of an action or event can be treated as the same for purposes of analysis? If every action is unique – indexed by all of its situational particulars – then no categories are possible and no theorizing is possible. We would have only a blooming, buzzing confusion (James, 1890). If we can group similar actions into categories, we can investigate and generalize about how those categories of action are related. 2.1.2. Actions are related to other actions In addition to indexical context, actions occur in sequential-temporal context. For example, musicians usually do not play one note; they play phrases and songs. The sequential relationships between actions are systematically overlooked in an actor-centric view because actions (e.g., decisions to adopt or use technology) are treated as independent. They are not treated as part of a larger pattern. An action network captures these relations by tracing associations between actions, rather than tracing associations between actants, as in an actor network (Latour, 2005). Like an actor-network, an action network is “stable-for-now”, because action patterns can change constantly (Tsoukas & Chia, 2002). Furthermore, as Hernes (2016, p. 604, emphasis added) argues, events are not merely related: "every event takes active part in performing the temporal trajectory." 2.1.3. Actions do things The performativity of actions is an important reason to put them in the foreground. Actions have consequences, and can even be considered to have agency (Hernes, 2016). Different actions do different things, and there are many taxonomies and vocabularies of action (e.g., Bloom, 1956). Such taxonomies provide a vocabulary that can be used to interpret the nodes (vertices) of the network. Because they are situated, actions are also domain specific, and should be defined at a level of detail (granularity) that is meaningful for participants in the domain. The situation- and domain-specificity of actions means that researchers need to generalize carefully and state boundary conditions clearly. 3. Networks of actions We focus here on network models because they are simplest way to represent this kind of information. Also, the general idea of action networks has been in circulation for years. For example, Czarniawska (1997, 2004) suggested that we focus on “action nets,” rather than organizations, as a unit of analysis. Czarniawska's metaphor can readily be translated into the language of graph theory. 1
By action, we simply mean something that happens. We could also use the term event (Abbott, 1992; Butts, 2008; Griffin, 1993; Heise, 1989).
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Fig. 1. Alternative network representations of the social world.
The basic idea is simple. In the center of Fig. 1, we see a flow of actions, from which we may be able to recover a “trace” that includes indexical particulars about each action (e.g., who, what, when, where …). From that trace, we can construct various kinds of representations. One can construct social networks, which depict relations between actors (Howison, Wiggins, & Crowston, 2011; O'Madadhain, Hutchins, & Smyth, 2005; van Der Aalst, Reijers, & Song, 2005). Alternatively, the same data can be used to construct networks of actions (B. Pentland & Feldman, 2007). The nodes (vertices) of an action network are defined by categories of action. As suggested above, these categories can be defined by any number of contextual indices (e.g., who, what, when where, why and more) (B. Pentland, Recker, & Wyner, 2016). The sequential relationships between these categories define the edges of the network. As Fig. 1 implies, networks of action stand in a dual relationship to networks of actors, composing figure and ground. Reversing figure and ground, and focusing on actions, rather than actors, provides a perspective that is complementary to the conventional focus on social networks or actor networks. Even within the study of social networks per se, relational event models are placing new emphasis on the action side of the picture (Butts, 2008; DuBois, Butts, McFarland, & Smyth, 2013; Leenders, Contractor, & DeChurch, 2016).
3.1. Patterns of action in the wild The action-centered perspective is not limited to processual phenomena within the “organizational container” (Winter, Berente, Howison, & Butler, 2014). When we move outside the context of a formal organization, processes may be less highly structured and less closely monitored, making action patterns more difficult to discern. Consider this example from the MIT Media Lab, which used cell phone data to track the location of 100 individuals over 9 months (Eagle & Pentland, 2006). Using location data from a cell towers and Bluetooth, the researchers trained a hidden Markov model with three states (home, work, and elsewhere). This hidden Markov model is a simple type of event network, where spending time at a particular location is an event, and transitions between events are the actions. Fig. 2 shows two exemplary patterns; the upper one is from a “high entropy” individual, while the lower one is “low entropy.” These figures show variation in the routine pattern of movement between work, home and elsewhere.
Fig. 2. Low entropy vs high-entropy patterns in location. (Adapted from Eagle & Pentland, 2006).
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3.2. Related models, methods and perspectives There are a number of established theoretical traditions and perspectives that relate to an action-centric view. 3.2.1. Speech acts, interacts, and conversations for action The performativity of natural language starts with single utterances (Austin, 1962), but it extends to patterns, as well. Weick (1979) and Goffman (1967), among others, highlighted the importance of connected patterns of interaction, such as the double interact and the interaction ritual. In the context of information systems design, Lehtinen and Lyytinen (1986) and Winograd and Flores (1986) direct our attention to extended patterns of speech acts, arranged in conversations. Interaction patterns have been studied in a variety of empirical settings, such as groups and teams (Hewes & Poole, 2012; Kozlowski, Chao, Chang, & Fernandez, 2015; Leenders et al., 2016). However, with some exceptions (e.g., Olson, Herbsleb, & Rueter, 1994), these are usually conceptualized as patterns of interaction between actors rather than patterns of action per se. 3.2.2. Process mining Process mining (van der Aalst, 2011; van der Aalst & Weijters, 2004) provides a well established example of constructing networks from trace data. The network models are Petri nets, a special class of network with two kinds of nodes: places and transitions. Petri nets can be used to represent processes as finite state machines, and the “places” in the network are used to explicitly represent the state of a system. Process mining is not only a robust research stream (Tiwari, Turner, & Majeed, 2008; van Der Aalst, 2012), it has also led to commercial applications (e.g., https://fluxicon.com/). These models facilitate practical applications, such as improving cycle time and throughput, but they rely on fairly strong assumptions about the existence of a discoverable process with stable, discrete states. 3.2.3. Organizational routines and information systems Current research on organizational routines adopts an action-centric perspective (Feldman, 2016; Feldman, Pentland, D'Adderio, & Lazaric, 2016) without making strong assumptions about discoverability of an underlying state machine. In research on routines, action networks have been used for the identification and comparison of organizational routines (B. Pentland, Hærem, & Hillison, 2010), measuring the complexity of routines (Hærem, Pentland, & Miller, 2015), and analyzing handoffs between actions (B. Pentland et al., 2016). Action-centric perspectives have been applied in research on information systems, as well (Aakhus et al., 2014; Goh, Gao, & Agarwal, 2011; Hayes, Lee, & Dourish, 2011; Lyytinen, 1985; Lyytinen, Klein, & Hirschheim, 1991; Yeow & Faraj, 2011). 3.2.4. Activity theory and work systems theory Focusing on relations between actions provides a perspective that is distinct from more holistic perspectives, such as activity theory (Bertelsen & Bødker, 2003; Engeström, Miettinen, & Punamäki, 1999; Kuutti, 1991) and work systems theory (Alter, 2008). In activity theory, activities are the unit of analysis, but the emphasis is on the activity system, which can include subjects, objects and instruments, as well as division of labor, rules and community. Activity systems and work systems are not actor-centric, but specific actions tend to remain in the background. Action networks could be used to identify and compare specific patterns and pathways of action within an activity system or a work system, but bracketing off actors and other features of the activity system would be considered unorthodox, at best. 3.2.5. Sequence analysis in social science There is also a substantial body of sequential data analysis across the social sciences (Abbott, 1995; Abbott & Tsay, 2000; Cornwell, 2015; Poole, Lambert, Murase, Asencio, & McDonald, 2016). Much of this work is based on the analysis of whole sequences, such as careers of musicians (Abbott & Hrycak, 1990) and life courses (Aisenbrey & Fasang, 2010). In research on information systems, Gaskin, Berente, Lyytinen, and Yoo (2014) analyze sequences of action within organizational routines. This research is being facilitated by new tools for visualization and analysis, like TraMineR (Gabadinho, Ritschard, Mueller, & Studer, 2011) and ThreadNet (B. Pentland et al., 2016). Advances in machine learning, such as Latent Dirichlet Analysis (LDA), and advances in computational modeling of action networks, such as Partially Observable Markov Decision Processes (POMDPs), make it possible to construct accurate models of action networks from observational data. LDA was developed to identify recurring themes and story types within literature (Blei, Ng, & Jordan, 2003), and has been used to learn patterns of action that produce changes in individual political opinion (Madan, Cebrian, Morutu, Farrahi, & Pentland, 2012). POMDPs were developed to guide robot actions towards overall goals (Kaelbling, Littman, & Cassandra, 1998) and have been used to model processes ranging from financial trading strategy to emergency search in disasters (A. Pentland, 2014). 4. Towards an action-centric research program In spite of these advances, action-centric research is still quite rare. As a result, there are a lot of opportunities to approach familiar topics from a new perspective (e.g., technology adoption and use), or to approach new topics entirely. We envision a research program that could include: (a) empirical studies; (b) methodological development; (c) data collection and management; and (d) practical applications. 140
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4.1. Empirical studies There are a wide range of specific topics where action networks or other action-centric methods could potentially applied. Here are some general categories of research questions that could be investigated. 4.1.1. The relative explanatory power of actions or actors Theoretically, the explanatory power of actions depends on their interdependence. If actions are truly independent, then patterns are not likely to be meaningful. Thus, one can envision a “bake-off” between actor- and action-centric models across various situations. For example, Limayem, Hirt, and Chin (2001) consider the role of habit in technology use, but because they were working in the actor-centric TAM tradition, they did not test the simple question: How well does the one action predict the next, irrespective of the actor? This type of question would be analogous to the study of situational strength in psychology (Meyer, Dalal, & Hermida, 2010) (the extent to which a situation is clear, consistent, highly constrained, and has definite consequences for non-compliance). In strong situations, action-centric models are likely to be better than actor-centric models. 4.1.2. Antecedents, consequences and prediction (Abbott, 1992) argues that action patterns mediate between independent and dependent variables in many kinds of social science phenomena. Thus, in research projects where explanatory mechanisms are desired, action patterns can be connected to outcomes like efficiency, effectiveness, or some other KPI. Also, sequential trace data are inherently useful for predicting events at a fine-grained level, because they speak directly to the critical question of prediction: what happens next? 4.1.3. Comparison Barley (1990) laid out a framework for synchronic and diachronic comparison of action patterns. If we were comparing a simple scalar variable (or a vector), we could refer to simple cross-sectional and longitudinal analysis. The basic idea is the same, of course, but because we are comparing patterns of action (e.g., networks), the synchronic/diachronic vocabulary seems more appropriate. Diachronic comparison naturally leads to the broader questions about emergence and dynamics. 4.1.4. Emergence and dynamics While network models have often been criticized as static, a great deal of current theory and research on networks is focused on network dynamics (Butts, 2008; DuBois et al., 2013; Leenders et al., 2016; Snijders, 2001). Specifically, it is focused on the processes of tie formation and dissolution over time. While there are thousands of papers on social networks, and other classes of networks (e.g., gene regulation networks), there are very few on action networks per se. We cannot assume that action networks have the same properties as other kinds of networks (Butts, 2009). For example, processes that influence dynamics in social networks, such as homophily, preferential attachment, and triadic closure may not be applicable to the dynamics of action networks. Nevertheless, the action network model is well suited to strong process theory (Langley & Tsoukas, 2016) because it provides a convenient way to describe process change over time. 4.2. Methodological development Research on action networks may also require the creation of new capabilities for visualizing, conceptualizing and measuring social and organizational phenomena. Action network methods can be applied to any stream of time-stamped events, so they can be used to visualize processes in any discipline. In addition to networks, a wide variety of tools exist for mining, categorizing and clustering event patterns (e.g., Gabadinho et al., 2011) and causal analysis of event networks (Pearl, 1993). The availability of new methods, such as deep learning (Bengio, 2009), offers possibilities for on-going progress. 4.3. Data collection and management Action-centric research methods require a lot of data, especially if they are applied to longitudinal questions. For some kinds of questions, data may be readily available. For example, modern supply chains leave rich sequences of digital trace data. New technologies, such as electronic sensing built into name badges, and the increasing use of digital telephones and videoconferencing, are beginning to produce digital traces even for face-to-face meetings (Olguin et al., 2009). Developing high-quality sources of data across a range of phenomena would help advance the research program envisioned here. We expect that privacy will be a particular concern, because trace data is often not anonymous. While our focus is on actions, the identity of the actor often provides a dimension that constitutes the trace. To the extent that current traces are considered to be predictive of future actions, this could engender particularly sensitive privacy concerns. Likewise, such data are often considered proprietary, as in product development or customer relationship management. Thus, we need to work out mechanisms to create protocols and tools for collecting and managing trace data in a way that preserves the interests of the data owners and the subjects (or objects) of trace data (A. Pentland, 2007; A. Pentland, 2014). 4.4. Practical applications This is an area where there is good reason to expect that practical applications will drive the science. Organizations are investing 141
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heavily in data science; they have data and they are striving to find better, more effective ways of using it. Current research using predictive analytics and “big data” is often criticized for lack of theory, among other things (Boyd & Crawford, 2012; McFarland, Lewis, & Goldberg, 2015). However, it could be that an action-centric perspective may reveal phenomena that were previously undetected and may lead to new theoretical categories. If so, then on-going practical inquiry will provide the basis for new understanding, and new ways of understanding (Calantone & Dröge, 1984). This suggests that active engagement with practitioners in business, government and non-profit sectors should be a key part of the research program. 5. Conclusion Constructing action networks is not an end in itself, nor are network models the only way to implement an action-centric view. 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Bracketing off the actors: Towards an action-centric research agenda
MARK
Brian T. Pentlanda,⁎, Alex P. Pentlandb, Roger J. Calantonea a b
Michigan State University, Broad College of Business, East Lansing, MA, United States Massachusetts Institute of Technology, MIT Media Lab, Cambridge, MA, United States
AB S T R A CT Widespread digitization is creating new sources of data that record sequences of actions and events. These action sequences can be used to trace coherent streams of activity in social, economic and business processes. These sources of data, along with new computational methods, create an opportunity to visualize, analyze and compare patterns of actions as a unit of analysis. This paper discusses the connections of this idea to a variety of related fields and maps out some first steps of an action-centric agenda for research.
1. Introduction Social, economic, and business research has traditionally focused on familiar units of analysis, such as individuals, groups and firms. In this paper, we suggest that patterns of action offer a useful addition to our repertoire as a unit of analysis. Action patterns can be represented in many ways. Here, we focus on action networks, a class of directed graphs where the vertices represent categories of action and the edges represent sequential relations between those categories. By tracing associations between actions, rather than associations between actors or actants, action networks offer a straightforward way to study processual phenomena, such as routines, practices, projects, processes and services, as patterns of action. To see how action networks can provide a novel perspective, consider a phenomenon that has attracted a lot of attention in research on information systems: technology adoption and use (Davis, 1989; Venkatesh & Davis, 2000). The conventional perspective starts with a focal actor who perceives the utility and ease of use of a new tool or a new feature. That actor may also perceive various costs and benefits, including social influence from other actors. Together, these factors shape the intentions of the actor. There have been many variations and refinements over the years, but all of these approaches address the question: what will the actor do? Even in research that considers an array of alternative actions situated in a rich, ecological context (e.g., Jung & Lyytinen, 2013), the focus is on the reasoning and motivation of the actor. Of course, actions do not happen in isolation. Most likely, in practice, actors are already doing something. If so, then actions involving the adoption or use of technology may be related to current actions (and past actions) as part of a larger pattern, project, process, practice or routine. Rather than asking about the actors, we can ask about the actions: what will happen next? And rather than seeing the actions as isolated, independent events, we can see them as related and interdependent, a perspective that is consistent with the assumptions of “strong process theory” (Chia & MacKay, 2007; Hernes, 2016; Langley & Tsoukas, 2016; Mesle & Dibben, 2016). By focusing on actions, and relations between actions, we can gain a novel perspective on processual phenomena. In principle, we can predict what will happen next without any information about actors.
⁎
Corresponding author. E-mail address: [email protected] (B.T. Pentland).
http://dx.doi.org/10.1016/j.infoandorg.2017.06.001 Received 25 August 2016; Received in revised form 16 June 2017; Accepted 17 June 2017 1471-7727/ © 2017 Elsevier Ltd. All rights reserved.
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2. Motivation One motivation for this approach is opportunistic: sequential trace data from digitization of economic and social processes in increasingly available, and improved computational tools make it possible to analyze this data in new ways. Within the context of formal organizations, there are a number of data sources that seem especially promising, such as electronic health records, customer relationship management systems, design processes supported by PLM (Product life-cycle management software), and workflow systems that support a wide range of business processes (van der Aalst, 2011). The deeper motivation is that focusing on actions (rather than actors or actants) provides a new perspective on the phenomena we study. The actor-centric perspective is like Ptolemaic astronomy: the actor is in the center of the universe. This perspective is pervasive. Psychology and behavioral economics are focused on motivations and decisions of actors. In social networks, we trace relations between actors (Lazer et al., 2009). In heterogeneous networks, we trace relations between actants (Latour, 2005). The incorporation of materiality into our thinking has not dislodged actants from this privileged place. Whether we see the social and material aspects of actants as inseparable (Orlikowski & Scott, 2008) or merely imbricated (Leonardi, 2011), the actants remain in the foreground. Everything revolves around the actants. Without question, this line of theorizing has led to an increasingly nuanced understanding of actors as heterogeneous, sociomaterial ensembles. However, like Ptolemaic astronomy, the actor-centric view has become increasingly complicated. We suggest that it may be helpful to bracket off the actors, at least temporarily. When we bracket off the actors, debates about the nature of actors/actants (Niemimaa, 2016; Ramiller, 2016) and models of actors' motivations or intentions become irrelevant. When we put actions in the foreground, distinctions like people vs. things, action vs. behavior and agency vs. necessity fade into the background. These distinctions are useful, but they are old, and they seem tired. Perhaps they could use a vacation. 2.1. Actions are an interesting unit of analysis Actions offer an alternative perspective, especially in the context of information systems (Aakhus, Agerfalk, Lyytinen, & Te'eni, 2014; Lyytinen, 1985).1 To use actions as a unit of analysis, we need to be clear about their distinctive properties, especially with respect to the formation of action networks. 2.1.1. Actions are situated in an indexical context Actions are always indexed (and therefore bounded) by the specifics of when and where they occur: time (now, later, …), place (here, there, …), subject (me, you, …), and so on (Barnes & Law, 1976; Heritage, 1984). As Hernes (2016, p. 602) suggests, actors are just another aspect of context: people and other objects are “features of events.” The question is, when are we willing to say that two instances of an action or event can be treated as the same for purposes of analysis? If every action is unique – indexed by all of its situational particulars – then no categories are possible and no theorizing is possible. We would have only a blooming, buzzing confusion (James, 1890). If we can group similar actions into categories, we can investigate and generalize about how those categories of action are related. 2.1.2. Actions are related to other actions In addition to indexical context, actions occur in sequential-temporal context. For example, musicians usually do not play one note; they play phrases and songs. The sequential relationships between actions are systematically overlooked in an actor-centric view because actions (e.g., decisions to adopt or use technology) are treated as independent. They are not treated as part of a larger pattern. An action network captures these relations by tracing associations between actions, rather than tracing associations between actants, as in an actor network (Latour, 2005). Like an actor-network, an action network is “stable-for-now”, because action patterns can change constantly (Tsoukas & Chia, 2002). Furthermore, as Hernes (2016, p. 604, emphasis added) argues, events are not merely related: "every event takes active part in performing the temporal trajectory." 2.1.3. Actions do things The performativity of actions is an important reason to put them in the foreground. Actions have consequences, and can even be considered to have agency (Hernes, 2016). Different actions do different things, and there are many taxonomies and vocabularies of action (e.g., Bloom, 1956). Such taxonomies provide a vocabulary that can be used to interpret the nodes (vertices) of the network. Because they are situated, actions are also domain specific, and should be defined at a level of detail (granularity) that is meaningful for participants in the domain. The situation- and domain-specificity of actions means that researchers need to generalize carefully and state boundary conditions clearly. 3. Networks of actions We focus here on network models because they are simplest way to represent this kind of information. Also, the general idea of action networks has been in circulation for years. For example, Czarniawska (1997, 2004) suggested that we focus on “action nets,” rather than organizations, as a unit of analysis. Czarniawska's metaphor can readily be translated into the language of graph theory. 1
By action, we simply mean something that happens. We could also use the term event (Abbott, 1992; Butts, 2008; Griffin, 1993; Heise, 1989).
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Fig. 1. Alternative network representations of the social world.
The basic idea is simple. In the center of Fig. 1, we see a flow of actions, from which we may be able to recover a “trace” that includes indexical particulars about each action (e.g., who, what, when, where …). From that trace, we can construct various kinds of representations. One can construct social networks, which depict relations between actors (Howison, Wiggins, & Crowston, 2011; O'Madadhain, Hutchins, & Smyth, 2005; van Der Aalst, Reijers, & Song, 2005). Alternatively, the same data can be used to construct networks of actions (B. Pentland & Feldman, 2007). The nodes (vertices) of an action network are defined by categories of action. As suggested above, these categories can be defined by any number of contextual indices (e.g., who, what, when where, why and more) (B. Pentland, Recker, & Wyner, 2016). The sequential relationships between these categories define the edges of the network. As Fig. 1 implies, networks of action stand in a dual relationship to networks of actors, composing figure and ground. Reversing figure and ground, and focusing on actions, rather than actors, provides a perspective that is complementary to the conventional focus on social networks or actor networks. Even within the study of social networks per se, relational event models are placing new emphasis on the action side of the picture (Butts, 2008; DuBois, Butts, McFarland, & Smyth, 2013; Leenders, Contractor, & DeChurch, 2016).
3.1. Patterns of action in the wild The action-centered perspective is not limited to processual phenomena within the “organizational container” (Winter, Berente, Howison, & Butler, 2014). When we move outside the context of a formal organization, processes may be less highly structured and less closely monitored, making action patterns more difficult to discern. Consider this example from the MIT Media Lab, which used cell phone data to track the location of 100 individuals over 9 months (Eagle & Pentland, 2006). Using location data from a cell towers and Bluetooth, the researchers trained a hidden Markov model with three states (home, work, and elsewhere). This hidden Markov model is a simple type of event network, where spending time at a particular location is an event, and transitions between events are the actions. Fig. 2 shows two exemplary patterns; the upper one is from a “high entropy” individual, while the lower one is “low entropy.” These figures show variation in the routine pattern of movement between work, home and elsewhere.
Fig. 2. Low entropy vs high-entropy patterns in location. (Adapted from Eagle & Pentland, 2006).
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3.2. Related models, methods and perspectives There are a number of established theoretical traditions and perspectives that relate to an action-centric view. 3.2.1. Speech acts, interacts, and conversations for action The performativity of natural language starts with single utterances (Austin, 1962), but it extends to patterns, as well. Weick (1979) and Goffman (1967), among others, highlighted the importance of connected patterns of interaction, such as the double interact and the interaction ritual. In the context of information systems design, Lehtinen and Lyytinen (1986) and Winograd and Flores (1986) direct our attention to extended patterns of speech acts, arranged in conversations. Interaction patterns have been studied in a variety of empirical settings, such as groups and teams (Hewes & Poole, 2012; Kozlowski, Chao, Chang, & Fernandez, 2015; Leenders et al., 2016). However, with some exceptions (e.g., Olson, Herbsleb, & Rueter, 1994), these are usually conceptualized as patterns of interaction between actors rather than patterns of action per se. 3.2.2. Process mining Process mining (van der Aalst, 2011; van der Aalst & Weijters, 2004) provides a well established example of constructing networks from trace data. The network models are Petri nets, a special class of network with two kinds of nodes: places and transitions. Petri nets can be used to represent processes as finite state machines, and the “places” in the network are used to explicitly represent the state of a system. Process mining is not only a robust research stream (Tiwari, Turner, & Majeed, 2008; van Der Aalst, 2012), it has also led to commercial applications (e.g., https://fluxicon.com/). These models facilitate practical applications, such as improving cycle time and throughput, but they rely on fairly strong assumptions about the existence of a discoverable process with stable, discrete states. 3.2.3. Organizational routines and information systems Current research on organizational routines adopts an action-centric perspective (Feldman, 2016; Feldman, Pentland, D'Adderio, & Lazaric, 2016) without making strong assumptions about discoverability of an underlying state machine. In research on routines, action networks have been used for the identification and comparison of organizational routines (B. Pentland, Hærem, & Hillison, 2010), measuring the complexity of routines (Hærem, Pentland, & Miller, 2015), and analyzing handoffs between actions (B. Pentland et al., 2016). Action-centric perspectives have been applied in research on information systems, as well (Aakhus et al., 2014; Goh, Gao, & Agarwal, 2011; Hayes, Lee, & Dourish, 2011; Lyytinen, 1985; Lyytinen, Klein, & Hirschheim, 1991; Yeow & Faraj, 2011). 3.2.4. Activity theory and work systems theory Focusing on relations between actions provides a perspective that is distinct from more holistic perspectives, such as activity theory (Bertelsen & Bødker, 2003; Engeström, Miettinen, & Punamäki, 1999; Kuutti, 1991) and work systems theory (Alter, 2008). In activity theory, activities are the unit of analysis, but the emphasis is on the activity system, which can include subjects, objects and instruments, as well as division of labor, rules and community. Activity systems and work systems are not actor-centric, but specific actions tend to remain in the background. Action networks could be used to identify and compare specific patterns and pathways of action within an activity system or a work system, but bracketing off actors and other features of the activity system would be considered unorthodox, at best. 3.2.5. Sequence analysis in social science There is also a substantial body of sequential data analysis across the social sciences (Abbott, 1995; Abbott & Tsay, 2000; Cornwell, 2015; Poole, Lambert, Murase, Asencio, & McDonald, 2016). Much of this work is based on the analysis of whole sequences, such as careers of musicians (Abbott & Hrycak, 1990) and life courses (Aisenbrey & Fasang, 2010). In research on information systems, Gaskin, Berente, Lyytinen, and Yoo (2014) analyze sequences of action within organizational routines. This research is being facilitated by new tools for visualization and analysis, like TraMineR (Gabadinho, Ritschard, Mueller, & Studer, 2011) and ThreadNet (B. Pentland et al., 2016). Advances in machine learning, such as Latent Dirichlet Analysis (LDA), and advances in computational modeling of action networks, such as Partially Observable Markov Decision Processes (POMDPs), make it possible to construct accurate models of action networks from observational data. LDA was developed to identify recurring themes and story types within literature (Blei, Ng, & Jordan, 2003), and has been used to learn patterns of action that produce changes in individual political opinion (Madan, Cebrian, Morutu, Farrahi, & Pentland, 2012). POMDPs were developed to guide robot actions towards overall goals (Kaelbling, Littman, & Cassandra, 1998) and have been used to model processes ranging from financial trading strategy to emergency search in disasters (A. Pentland, 2014). 4. Towards an action-centric research program In spite of these advances, action-centric research is still quite rare. As a result, there are a lot of opportunities to approach familiar topics from a new perspective (e.g., technology adoption and use), or to approach new topics entirely. We envision a research program that could include: (a) empirical studies; (b) methodological development; (c) data collection and management; and (d) practical applications. 140
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4.1. Empirical studies There are a wide range of specific topics where action networks or other action-centric methods could potentially applied. Here are some general categories of research questions that could be investigated. 4.1.1. The relative explanatory power of actions or actors Theoretically, the explanatory power of actions depends on their interdependence. If actions are truly independent, then patterns are not likely to be meaningful. Thus, one can envision a “bake-off” between actor- and action-centric models across various situations. For example, Limayem, Hirt, and Chin (2001) consider the role of habit in technology use, but because they were working in the actor-centric TAM tradition, they did not test the simple question: How well does the one action predict the next, irrespective of the actor? This type of question would be analogous to the study of situational strength in psychology (Meyer, Dalal, & Hermida, 2010) (the extent to which a situation is clear, consistent, highly constrained, and has definite consequences for non-compliance). In strong situations, action-centric models are likely to be better than actor-centric models. 4.1.2. Antecedents, consequences and prediction (Abbott, 1992) argues that action patterns mediate between independent and dependent variables in many kinds of social science phenomena. Thus, in research projects where explanatory mechanisms are desired, action patterns can be connected to outcomes like efficiency, effectiveness, or some other KPI. Also, sequential trace data are inherently useful for predicting events at a fine-grained level, because they speak directly to the critical question of prediction: what happens next? 4.1.3. Comparison Barley (1990) laid out a framework for synchronic and diachronic comparison of action patterns. If we were comparing a simple scalar variable (or a vector), we could refer to simple cross-sectional and longitudinal analysis. The basic idea is the same, of course, but because we are comparing patterns of action (e.g., networks), the synchronic/diachronic vocabulary seems more appropriate. Diachronic comparison naturally leads to the broader questions about emergence and dynamics. 4.1.4. Emergence and dynamics While network models have often been criticized as static, a great deal of current theory and research on networks is focused on network dynamics (Butts, 2008; DuBois et al., 2013; Leenders et al., 2016; Snijders, 2001). Specifically, it is focused on the processes of tie formation and dissolution over time. While there are thousands of papers on social networks, and other classes of networks (e.g., gene regulation networks), there are very few on action networks per se. We cannot assume that action networks have the same properties as other kinds of networks (Butts, 2009). For example, processes that influence dynamics in social networks, such as homophily, preferential attachment, and triadic closure may not be applicable to the dynamics of action networks. Nevertheless, the action network model is well suited to strong process theory (Langley & Tsoukas, 2016) because it provides a convenient way to describe process change over time. 4.2. Methodological development Research on action networks may also require the creation of new capabilities for visualizing, conceptualizing and measuring social and organizational phenomena. Action network methods can be applied to any stream of time-stamped events, so they can be used to visualize processes in any discipline. In addition to networks, a wide variety of tools exist for mining, categorizing and clustering event patterns (e.g., Gabadinho et al., 2011) and causal analysis of event networks (Pearl, 1993). The availability of new methods, such as deep learning (Bengio, 2009), offers possibilities for on-going progress. 4.3. Data collection and management Action-centric research methods require a lot of data, especially if they are applied to longitudinal questions. For some kinds of questions, data may be readily available. For example, modern supply chains leave rich sequences of digital trace data. New technologies, such as electronic sensing built into name badges, and the increasing use of digital telephones and videoconferencing, are beginning to produce digital traces even for face-to-face meetings (Olguin et al., 2009). Developing high-quality sources of data across a range of phenomena would help advance the research program envisioned here. We expect that privacy will be a particular concern, because trace data is often not anonymous. While our focus is on actions, the identity of the actor often provides a dimension that constitutes the trace. To the extent that current traces are considered to be predictive of future actions, this could engender particularly sensitive privacy concerns. Likewise, such data are often considered proprietary, as in product development or customer relationship management. Thus, we need to work out mechanisms to create protocols and tools for collecting and managing trace data in a way that preserves the interests of the data owners and the subjects (or objects) of trace data (A. Pentland, 2007; A. Pentland, 2014). 4.4. Practical applications This is an area where there is good reason to expect that practical applications will drive the science. Organizations are investing 141
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heavily in data science; they have data and they are striving to find better, more effective ways of using it. Current research using predictive analytics and “big data” is often criticized for lack of theory, among other things (Boyd & Crawford, 2012; McFarland, Lewis, & Goldberg, 2015). However, it could be that an action-centric perspective may reveal phenomena that were previously undetected and may lead to new theoretical categories. If so, then on-going practical inquiry will provide the basis for new understanding, and new ways of understanding (Calantone & Dröge, 1984). This suggests that active engagement with practitioners in business, government and non-profit sectors should be a key part of the research program. 5. Conclusion Constructing action networks is not an end in itself, nor are network models the only way to implement an action-centric view. 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