visualizing punctuated equilibria in discursive changesuccession, coexistence, and procedures of...

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Visualizing Punctuated Equilibria in Discursive Change Exploring a New Text Analysis Possibility for Management Research Daniel Angus, David Rooney, Bernard McKenna, Janet Wiles

We present a new method of visualizing textual data that brings an evolutionary perspective to management research. The method uses two text analysis software applications, Leximancer and Discursis to provide visualizations of the semantic content and temporal structure of time series data. We use the method to map discursive change in the history of ideas in a journal. Data used in this analysis is comprised of abstracts of empirical articles published in the journal, Management Communication Quarterly, over twenty years. Findings demonstrate the turbulent nature of discursive change, and show how it is structured into punctuated equilibria with phase transitions separating successive discursive regimes. In particular, our findings identify the period of scholarship based on a discursive and critical orientation and the transition point when it began.

Keywords: discourse, discursive change, punctuated equilibrium, turbulence, research methods, data visualization

Introduction Discourse, understood as “a group of statements in so far as they belong to the same discursive formation [that are] ... made up of a limited number of statements for which a group of conditions of existence can be defined” (Foucault, 1972, p. 117). In day-to-day life discursive change is barely perceptible to participants: a case of not being able to see the wood for the trees. As an example, who noticed any change in financial discourse before the US Sub Prime Mortgage Crisis or the ensuing Global Financial Crisis? The answer to this question is that no one who mattered in trading rooms noticed. Closer to home, who can say precisely when the discursive or critical turn happened in social research, or when positivist epistemologies lost their unchallengeable grip on mainstream organizational behavior research? In this study we examine how to meaningfully visualize textual data for long-term discursive change and show how data visualization assists researchers to better understand the processes of such changes in longitudinal qualitative data. Theoretically, we use neo-Foucaultian discourse theory and punctuated equilibrium theory. We show that, if long-term change is

Visualizing Punctuated Equilibria in Discursive Change: Exploring a New text analysis possibility for management research


visualized, researchers can determine whether change is gradual, or – like evolutionary systems – exhibits punctuated equilibria (periods of relative stability) separated by rapid phase transitions. These methods enable the detection and visualization of change in discourse communities, and the identification of periods during which new discursive regimes emerge. More specifically, we show that, if slow longer-term change is visualized, researchers can detect punctuated equilibria and transition phases and in doing so anticipate if particular discourse communities, or the work they do, are getting close to a significant new discursive regime emerging.

Although Foucault's (1972) theory of archaeology was intended to trace discursive patterns over centuries such as from the middle ages to modernism, the concept of fields within that theory is useful here. Discursive formations are never homogenous or conceptually coherent. Discourses evolve by processes of interdiscursivity and, in moments of rupture, by dialectical encounter. To use Bakhtin’s theory, the intellectual encounters within each journal’s history can be understood as processes of centripetal force that centralize and unify the monologic interests of a dominant group or paradigm, as well as centrifugal forces that fragment thought into multiple worldviews (Morris, 1994a, p. 15). Every utterance “enters a dialogically agitated and tension-filled environment of alien words, value judgments and accents, weaves in and out of complex interrelationships, merges with some, recoils from others, intersects with yet a third group” (Bakhtin, 1994/1935, pp. 75-76). This site of contestation between multiple discourses, then, is what constitutes the heteroglossia of a discipline (communication) or disciplinary sub-group (communication management) (Bakhtin, 1994/1935, p. 115), where heteroglossia is understood as “the diversity of social languages . . . [that] are systematically related to one another” (Lemke, 1995, p. 38) and through which it constructs its “beliefs, opinions and values” (p. 24). Because heteroglossia essentially involves oppositional social forces (Morris, 1994b, p. 249), it is a useful concept for investigating discursive tensions between competing paradigms (e.g., instrumentalist or critical), and foci for attention (e.g., interpersonal communication). Consistent with this position is Foucault’s assumption of discontinuity as each new text (a journal article, in this instance) is published, producing systems of dispersion.

Notwithstanding this position, discursive formations develop through discernible regularities (forms of succession, coexistence, and procedures of intervention). By considering the forms of coexistence, all statements in a discursive formation, we can discern movements over time. Coexistence will occur in three fields: presence, concomitance, and memory. Texts belonging to a field of presence are those “formulated elsewhere and taken up in discourse, acknowledged to be truthful, involving exact description, well-founded reasoning, or necessary presupposition” (Fairclough 1992, p. 46). The field of presence also involves those discursive elements deliberately excluded for being incorrect. Concomitant texts can be understood in Fairclough’s variation of Foucault to be emergent discourses, “statements from different discursive formations” (p. 46), although it is unclear in Fairclough’s adaptation whether these emergent discourses are dialogical or dialectical in nature. The field of memory includes those discourses that are considered to be “out of date” (Rosenmann, 1999, p. 42). Statements within a journal article, like any text, have an enunciative function that, according to the



rules of formation, contributes to an enunciative field. It is at this point that finer distinctions of text analysis can take place. Discourse represents a semiotic order above language with “all the inconsistencies, contradictions, and conflicts that can exist within and between such higher-order semiotic systems” (Halliday, 1994, p. 339). More specific information about (inter)discursivity operates, thus is found by considering forms of (inter)textuality, which is analyzed according to lexical and grammatical features. Concepts within discursive formations such as management communication can be categorized by the degree to which they appear, thereby giving us an insight into concept formation over time.

Discourse as an emergent, dialogical, and dialectical phenomenon is able to be considered as a complex adaptive system that can undergo incremental and revolutionary change. Discursive change, therefore, can be illustrated using complex adaptive system analytical methods.

Punctuated equilibrium theory has its roots in paleobiology (Gould & Eldredge, 1977) and explains how species undergo periods of slow evolutionary change, punctuated by periods of rapid change. The theory has been extended to explain aspects of social theory including policy change (Givel, 2006), evolution of conflict (Cioffi-Revilla, 1998), and organizational change (Gersick, 1991). Like biological evolution, these social systems are characterized by periods of stability (equilibrium), punctuated by smaller periods of revolutionary change. However, it is not the case that change necessarily reorganizes these systems across all levels. Typically, the subcomponents present in states of equilibrium are reorganized and restructured during periods of rapid transition: in biological evolution, DNA is still DNA at the end of a mutation event; in social systems people and organizations still exist although they may be organized differently. Likewise, when studying discursive change in management communication research, punctuated equilibrium provides a useful way to identify, interpret and discuss the changes in discourse.

Visualizing punctuated equilibria provides a new method for dealing with textual data in longitudinal studies by treating text as time series data over the long-term with concepts as the system’s subcomponents. One may, for example, want to study the front page of the Wall Street Journal as an unfolding set of connected utterances, or even as a conversation, over decades. There would very likely be an interesting and revealing story to be told in doing such a study. As researchers we are also interested in the conversations we have with each other through our published work. In this article, we focus on the conversation that is work published in a scholarly journal. This kind of conversation runs between authors, journals (particularly the editors and reviewers), and the journal’s readership. To investigate the history of such ideas, we utilized published data from the journal, Management Communication Quarterly (MCQ), which is the leading journal for empirical organizational communication research (Rooney, McKenna, & Barker, 2011).We use text analytic approaches specifically designed for analysis of time-series conceptual data and are therefore ideal methods to explore research questions around decades-long conceptual change in a professional management research discourse community.



In the remainder of this paper, we next discuss issues surrounding content and other forms of analysis of journals. Then we discuss critical issues related to qualitative data visualization in social and management research. Following that, we provide an analysis of data spanning the 20 year history of MCQ from its first issue (1989) until the end of 2009.

Very large datasets, visualization, and complex knowledge Increasingly, the research community is calling for more ambitious ways of dealing with data. Such calls include the call for integration of qualitative and quantitative data. For example, integrating focus group data, and social surveys with ecological and landscape data that can help to create knowledge about the interactions between culture, spirituality, values, social relations, ecotourism, and recreation in ecosystems (Daniel et al., 2012).

A major advance that quantitative research has enjoyed over the last decade is the ability to data-mine very large data sets. With data-mining technology, complex or difficult to find but important patterns in very large datasets can be identified and analyzed to reveal hitherto unnoticed phenomena. Historically the majority of developments in data mining have focused on exploring numeric data; however tools for qualitative approaches to data mining of textual data have grown in popularity, giving rise to a new group of techniques called text mining or text analytics (Ronen Feldman, 2006; Smith, 2000).

In parallel to developments in data and text mining, researchers in the computer graphics and information visualization field have been developing better ways to make visual sense of the relationships present in complex datasets. The merging of these two fields has seen the birth of visual text analytics, which uses techniques that visually model text data for interpretation by trained analysts and laypeople. Visual text analytic techniques are effective in exploring the processes of changing discourses that are otherwise difficult to detect and inspect using other numeric approaches or more interpretive approaches. An example of this is the use of data visualization tools that show interfirm activity across a whole business ecosystem through integrating heterogeneous data from engineering, economics and social science to help predict development of new products (Basole, Hu, Patel, & Stasko, 2012).

Although the expectation that predicting new innovations through such methods is likely still somewhat optimistic, new data analysis software is nevertheless gaining momentum. Such techniques have been used to discover complex patterns in international trade using large spatio-social network datasets (Luo, MacEachren, Yin, & Hardisty, 2011). A challenge that this paper addresses is to visualize textual data in both conceptual network and time series (longitudinal) forms. Two visual text analytic techniques are employed in our data visualization approach: Discursis (D. Angus, A. Smith, & J. Wiles, 2012a; D. Angus, A. E. Smith, & J. Wiles, 2012b) and Leximancer (Smith, 2000; Smith & Humphreys, 2006), both of which are described in the Methodology section.



Journal Analysis Commissioned articles by respected researchers are a major form for the review and integration of the main ideas that frame a research field such as change management and organizational development (Weick & Quinn, 1999), knowledge (Roberts, 2007), and aspects of communication (Knapp & Comaden, 1979), including organizational communication Deetz, 2001; Krone, Jablin, & Putnam, 1987; McPhee & Tompkins, 1985; Mumby & Stohl, 1996; Putnam & Krone, 2006).

Although systematic evaluations of research are common, use of formal empirical approaches to track intellectual change in a field or a journal is much less common. Collins’ (1998) sociology of philosophy and Burke’s (2000) social history of knowledge are examples of formal empirical analyses of the social history of ideas in Europe over centuries. However, formal analyses of histories of ideas in scholarly journals are harder to find. The clearest examples of quantitative approaches to evaluation and analysis of journals have involved bibliometrics - measures of impact factors, citations and co-citations, for example, in behavioral science and management journals (Extejt & Smith, 1990), information systems management (Culnan, 1987), and supply chain management (Charvet, Cooper, & Gardner, 2008). However, bibliometric analysis is independent of the ideas and their development over time within journals. Bibliometric analysis of this kind is subject to criticisms including whether impact factors are reliable measures of quality (Hecht, Hecht, & Sandberg, 1998); its failure to measure impact on practitioners (Saha, 2003); and critiques that citation analysis is based on naïve understandings of the sociology of knowledge production that do not account for the social construction of knowledge (MacRoberts & MacRoberts, 1996).

Our main concern in this article is to inform about longitudinal change in the ideas underlying an academic discourse. Citation or co-citation analysis, we argue, is not intended to insightfully map the intellectual structure of a journal or field. One needs to be able to map ideas or concepts within a field, rather than just researchers. With such concerns in mind, it is important that recent approaches to evaluation of journals and research generally have adopted a more sociological and cognitive orientation. Raw citation figures, quality measures and impact factors are giving way to a desire to understand, for example, “intellectual structure” (Charvet, et al., 2008; Culnan, 1987), and ways of visualizing or mapping important historical patterns in a particular literature (Cretchley, Rooney, & Gallois, 2010; White & McCain, 1998).

The intellectual structure approach to journal analysis has attempted to build ideas or concepts into its graphs and maps (Bourret, Mogoutov, Julian-Reynier, & Cambrosio, 2006). This movement coincides with recent technological advancements in Computer Assisted Text Analysis (CATA) that now allows concept and cognitive mapping. Interesting applications of this kind of analysis have been done in relation to organizational change (Kuhn & Corman, 2003; Rooney et al., 2010), medical research (Bourret, et al., 2006), government media responses to terrorist acts (McKenna & Waddell, 2007), public policy discourse (Rooney, 2005), and semantic analysis of journals (Cretchley, et al., 2010). We



propose another important application of CATA that facilitates clearer mapping of the history or evolution of ideas in a journal or field of research (Bourret, et al., 2006).

Methodology In this study we use visual text analytics techniques based on computational concept analysis to track the prominence of particular concepts over time within the corpus under study. The purpose of our methods is to reveal the temporal structure of discursive change highlighting evolutionary and revolutionary change within a particular discourse community over two decades. In this section we introduce the methods used and how they have been adapted for this study.

Visual Text Analytics Central to our analysis are the Leximancer and Discursis textual data analysis software applications. Leximancer (Smith, 2000; Smith & Humphreys, 2006) uses word occurrence and co-occurrence counts to extract major thematic and conceptual content from an input corpus. This automated process generates a tailored taxonomy from the input corpus which can be displayed graphically via an interactive concept map, or as tables indicating key concepts and conceptual relationships. Put simply, Leximancer generates a list of the major concepts in the input corpus, what terms invoke these particular concepts, and how those concepts relate to each other. Leximancer is an alternative analysis methodology to hand-coding and is equally suited to the analysis of small or very large datasets. Unlike alternative qualitative analysis tools (e.g., NVivo) which require analysts to design the list of concepts and coding rules themselves, Leximancer uses machine learning to generate its own lists and relationships from the input corpus. An advantage of a software generated concept list is that the list is highly reliable, being generated from the input corpus itself, whereas manual lists require checks for coding reliability and validity.

Discursis (Angus, et al., 2012a; Angus, et al., 2012b) is a visual text analytic application that produces visualizations and metrics from text that has an inherent temporal structure, examples being conversation transcripts, social media feeds, or forum communications. Discursis automatically builds an internal language model from an input corpus using the Leximancer concept engine described above. Discursis tags each temporal unit based on its conceptual content, and generates an interactive visual representation of the input corpus by linking semantically similar temporal units. The Discursis visual representation enables an analyst to quickly overview the thematic content of the text over time, and regions of thematic coherence over short (turn-by-turn), medium (10 temporal unit) and long (whole dataset) time scales. Discursis is useful for locating periods of activity where temporal units use similar topics or lack topical coherence.

In the present study, Discursis has been adapted for the analysis of journal abstracts. Each abstract is treated as an individual temporal unit for concept tagging and analysis purposes. In conversation



data, Discursis is able to easily highlight changes in patterns of concept use over time due to unequal distribution of concepts throughout a conversation text and the rapid change of concept use in time. Given the high density of concepts present in journal abstracts, and a fairly even distribution of these concepts in time, it is unlikely that the traditional recurrence plot visualization that Discursis provides can effectively reveal detail of the conceptual coherence of the abstracts over time. Instead a Theme River (Havre, Hetzler, Whitney, & Nowell, 2002) visualization approach is used.

Theme River (Havre, et al., 2002) depicts thematic change over time as a series of “rivers” or ribbons of color that alter their thickness depending on their prominence at various time points. Theme River does not perform native semantic processing; instead it takes existing conceptual relationships to create a visual depiction of these relationships over time. For the present study, Discursis provides the conceptual coding of the individual abstracts and these values are used to produce Theme Rivers by the following steps:

1. The entire corpus of abstract data is loaded into Discursis. 2. Discursis uses the Leximancer semantic engine to identify semantic relationships between key

concepts and terms in the input data. 3. Discursis is used to determine the key concepts (concept vectors) that are present in each

abstract based on the text contained in that abstract. 4. The concept vectors for abstracts from each year of data are combined into summary vectors

indicating the relative prominence of particular concepts for that year. 5. The 20 concepts with highest variance over the many years of data are then shown using a

theme river visualization.

Data MCQ publishes empirical research in four issues every year. For this study we analyze the abstracts for each empirical article published in the journal since its inception. A small number of conceptual articles and essays published in MCQ during the period of our study were omitted from the analysis because this analysis focusses only on empirical articles. We chose to analyze abstracts because of their lexical density in encapsulating the central ideas and arguments explained in their articles. The total number of abstracts included in this study is 390.

Concepts were automatically generated from the entire set of abstracts and the top 20 concepts over the lifetime of the journal were determined. For each year, the relative use of each concept was determined, normalized by the number of articles in that year. For example, a score of 1.0 would indicate that every article in the year contained the concept, a score of 0.0 would indicate that no articles in the year contained the concept.



Results and Discussion

Slow Change in Discourse Of the top 20 concepts, 9 were removed as they pertained to methodological issues or discourse-participant words such as “authors” which would be used in contexts such as “the authors argue that…” (McKenna, 1997). The remaining 11 concepts were used to create a theme river where each time point was normalized to determine the relative prominence of each concept at this time point (see Figure 1).

We observe a long term change in the use of concepts over two decades with a selection of concepts that were relatively prominent initially steadily decreasing over the measured time course, while other concepts emerge later and rise to prominence towards the end of the time period.

If we begin by comparing 1987 and 2009 it is immediately clear that the balance of concepts is radically different at each point (Figure 1). This contrast indicates that considerable change has occurred throughout the research history of MCQ. In 1987 interpersonal, internal, and relations are the most prominent concepts. Relation falls away rapidly before re-emerging in 1990. In 2009 discursive, resistance, and action are the most prominent concepts, yet in 1990 discursive, and resistance do not appear and action is a weak concept. Interpersonal, internal, and relations have only recently re-emerged. That there are 8 concepts in 2009 and five in 1987 indicates that there is greater conceptual diversity in 2009.

Another important analytical feature of the Theme River is what we term discursive turbulence, derived from measurement of acceleration and deceleration of change in the theme river multi-conceptual structure. Each individual theme in the river undergoes significant change in its usage from year to year and the overall profile made of the aggregation (and interaction) of themes within the river banks from year to year identifies the complexion of the heteroglossic conversation. In this instance, the theme river representation shows that from year to year the discourse can be dominated by many or few concepts. An effective metric of change, discursive turbulence, is derived by combining the usage statistics for the set of concepts into a vector for each year of the journal. The turbulence metric first calculates the dot product of the concept vectors (their similarity) and then calculates the rate of change of this value over time. The turbulence metric captures periods where the system enters and exits periods of stability, where stability is defined as a period of constant or no change. As an example, a system changing at a constant rate would have a turbulence value of zero, but a system that is moving from a period of sustained steady change to a period of no change would record a positive value for turbulence. In Figure 1, the turbulence metric is shown as a black line superimposed over the prominence of the concepts. Figure 1 shows that between 1987 – 1990 five main terms dominated the discourse, and that the discourse was in a period of equilibrium. In 1991 and 1992 the rise in usage of the concept “relations” and brief lack of concepts “conflict”,



“interpersonal” and “style” caused a large disruption (punctuation) to the stability of the discourse, reflected in the turbulence measure. The discourse stabilised back into a semi-stable state through 1992 – 1993, but was perturbed (punctuated) again in 1993-1994 leading to a fairly constant rate of change in the discourse through 1993 – 2000. The constant rate of change between 1993 – 2000 is reflected by a lower value for turbulence in this period. In 2001 the discourse ceases this change trajectory, and several concepts are seen to be used in a fairly consistent way until 2009. The cessation of change is seen as a spike in turbulence in 2001 which signals the end of the shift from the previous stable state to this new stable state. The peaks in turbulence in 1991 and 2001 can be interpreted as markers which signal the system moving from a period of stability before 1991 into a period of constant but significant discursive change, and then from this period of change back to a period of stability after 2001.

Figure 1 • Concept turbulence over 22 years of the MCQ journal.

But what are the significant discursive changes apparent in this data? To complement the time series analysis shown in the evolution of ideas in Figure 1, we created a concept map of the key terms using Leximancer. This concept map used the MCQ abstracts divided into five year subsets. Leximancer was then asked to treat each period (or subset of the data) as a theme and to create the concept map using a discriminating analytic technique. The discriminating analysis focuses on what the key



differences are between each period rather than similarities shared by different periods. However, the locations of periods on the concept map and the degrees of overlapping between concepts are indicative of their shared semantic content.

Figure 2

It is easy to see the degree of separation between the 1987 – 91 and 1992 – 96 periods in Figure 2, which coincide with the turbulence peaks of 1991 and 1994 in Figure 1. The 1997 – 2001 (Figure 2) period pulls further away from the two earlier periods, coinciding with the 2000 – 01 turbulence peak (Figure 1). The remaining periods (Figure 2) contain overlaps and cluster together, coinciding with the rapid reduction of turbulence and stability for the remainder of the time series (Figure 1). The big theoretical change in this data is the “discursive turn”, that is, when discourse is seen as the intersection

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of textual representation, meaning, and culture to constitute an appropriate set of understandings about a particular area of human activity (Hall, 1997). This is seen in both visualizations beginning with the 2000 – 01 peak (Figure 1) and the closeness of the final three periods in Figure 2. The Leximancer findings (see Figure 2) provide confirmation and more specific lexical detail about this evolution of MCQ from a quantitative, manager-focused journal to its contemporary shape that includes qualitative research as well as critical poststructural issues (e.g., resistance, identity) within organizations. Using such a method can provide useful insights into the effects of editorial dispositions (Rooney, et al., 2011) that promote centripetal (conservative) or centrifugal (diverse) forces in disciplinary areas. Although Figure 2 shows how the academic discourse is different (and how it is differently distributed in concept space as shown by the concept map) in different 5 year periods of the journal’s history, it shows little of the micro dynamics of change in the discourse in a truly longitudinal way. Figure 2 could mislead a reader to see long sweeping arcs in change running smoothly from period to period in step-wise increments suggesting regularity of the discourse instead of its discontinuities and turbulence. Figure 1 however, graphically indicates a more turbulent and even stuttering process of discursive change.

Along with the discursive turn is the big shift in research focus from informing a narrower managerial skills agenda based on modernist concepts of organizational structures to more knowledge-based and person-centered approaches (Morgan, 2006). By looking for the presence of large waves representing dominant aspects of discourse during particular times, a more informative story emerges. On the left side of Figure 1 we see big waves (that take up large areas) that represent the themes of training (green), style (brown), interpersonal (red), relations (dark green), action (sky blue), and conflict (orange). On the right side of the Theme River we see discursive (crimson), resistance (pink), action (sky blue), and labor (dark blue). The middle section of the Theme River (roughly from 1998 to 2000) with only resistance and action taking dominant positions. Action is the only theme with a dominant position in all three segments of the Theme River. Resistance exists in the middle and late phases of this time series. For MCQ, the final two years of the twentieth century appear to be the point at which the change in discourse to its present regime took place: a surprisingly quick transition.

These discursive changes can be readily categorized, then, into fields of presence, concomitance, and memory by organizing the lexical items of, say, resistance and action into discernible (critical) discourses in the field of presence whereas lexical items of interpersonal and internal can be seen as more instrumentalist concepts from the field of memory. Utilizing the technologies of Leximancer, Discursis and Theme River provides immense benefits in tracking the intellectual shifts of any discipline. It does so by providing broad thematic patterns both visually and textually of Leximancer concepts and Discursis themes. But, significantly, it can provide excellent granularity and specificity by charting the rate of change, or turbulence (rate of vectoral changes) as well as identifying the lexical items that characterize such discourses.



Conclusion Data visualization and computer analysis of qualitative textual data has great potential in management research. Time series data in longitudinal studies is highly prized by researchers, and new methods for dealing with such data are worthy of consideration. This is particularly the case with understanding discursive change through the analysis of text. The visualization of discursive change using the Theme Rivers visual text analysis approach has demonstrated the process of punctuated equilibrium as discourse changes. It is also important that Theme River picks up on the landscape perspective that this journal is interested in. Visual text analysis creates a topography that tells a story about textual data and discourse by demonstrating the flow of turbulence and moments where punctuated equilibria emerge over the course of two decades.

Discourse theory presents discursive change as a set of contested ideas that unfold in a sometimes uneven way as tensions emerge and attempts are made to resolved them. Using our methods of longitudinally mapping discourse we were able to reveal what participants in the process of change cannot necessarily see on a day to day basis and what other methods find difficult to discern in high resolution.

This quantitative approach is intended to complement rather than supplant the interpretive approaches to discourse analysis. It provides consistency of analysis that augments human memory, freeing the analyst to use the intuitive, creative, and imaginative processes of human thought most effectively. This quantitative approach can also provide confirmation of judgments arrived at by different methodologies: for example, in this instance, the claims of a “discursive turn” are strengthened by our results. This methodology can also be used as an inductive approach providing objective analyses that will provide the foundations of more elaborate research, and so greatly assists more complex analysis and understanding.

Future research can use these and similar techniques for a range of research tasks. Micro conversation analysis, discourse in online discussion groups, exchanges between organizations and customers on Twitter, the history of ideas in annual reports of major corporations, and even the evolution of discourse in doctoral dissertations from business school in comparison with, for example, sociology and psychology schools. Taken in the context of data mining and very large data sets, text visualization analysis methods like those used in this paper are important in general terms for knowledge. Not only is it an advantage to be able to visualize complex patterns in large and heterogeneous data sets, but complex knowledge itself can be rendered more understandable by a wider audience when visualization methods are used. Finally, it is also possible to take research to even high units of analysis if, for example, one wanted to see if our form of analysis could be applied to predict changes in the business cycle from boom to depression not by studying numbers in the form of money and interest rates, but the discourses that operate in key parts of the economy; one could also track scientific and technological change (innovation) over the course of centuries not my counting patents or R&D investment but by analyzing discourse operating in journals of long standing



(for example, Nature and Science), or records of the learned societies (for example, The Royal Society). Doing this kind of analysis may answer questions about whether or not technological change is indeed speeding up or not.

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Author Bios

Dr. Daniel Angus received a BS/BE double degree in research and development, and electronics and computer systems, and a PhD degree in computer science from Swinburne University of Technology, in 2004 and 2008, respectively. He is currently a lecturer in the School of Information Technology and Electrical Engineering and School of Journalism and Communication at the University of Queensland. His research focuses on the development of visualization and analysis methods for conceptual data, with a specific focus on human discourse.

David Rooney is Associate Professor at UQ Business School, The University of Queensland. He has researched, taught and published in the areas of the knowledge economy, knowledge management, wisdom, leadership, and organizational change. His books include Public Policy in the Knowledge-Based Economy, the Handbook on the Knowledge Economy, Knowledge Policy, and Wisdom and Management in the Knowledge Economy. Rooney has also published in many leading academic journals including The Leadership Quarterly, Public Administration Review, Human Relations and Management Communication Quarterly.

Bernard McKenna is an Associate Professor in the University of Queensland Business School where he teaches mostly at graduate level. He has published extensively in such journals as Applied Linguistics, Social Epistemology, Public Administration Review, Management Communication Quarterly, Critical Discourse Studies, and Leadership Quarterly and is on the editorial board of five journals as he has diverse research interests. He recently co-authored Managing Wisdom in the Knowledge Economy (Routledge, 2010).

Janet Wiles holds a Ph.D. from the University of Sydney, and is Professor of Complex and Intelligent Systems at the University of Queensland. She recently completed a five-year project leading the Thinking Systems Project, supervising a cross-disciplinary team studying fundamental issues in how information is transmitted, received, processed and understood in biological and artificial systems. Her research interests include complex systems biology, computational neuroscience, computational modeling methods, artificial intelligence and artificial life, language and cognition.

visualizing punctuated equilibria in discursive changesuccession, coexistence, and procedures of...

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