Mapping and Visualizing The Core of Scientific Domains: <br />Information System Research<br />Authors:<br />GoharFeroz Khan*<br />Junhoon Moon**<br />Han Woo Park*<br />*Department of Media & Communication, YeungNam University, Republic of Korea<br />**Information Management & Marketing, College of Agriculture and Life Sciences, Seoul National University, Republic of Korea<br />Prepared for COLLNET 2011, Seventh International Conference on Webometrics, Informetrics and Scientometrics (WIS), 20-23 September, 2011, Istanbul Bilgi University, Istanbul, Turkey, http://collnet.cs.bilgi.edu.tr/program/programme/<br />An updated version of this article is accepted for publication in the Scientometrics journal<br />
Introduction<br /><ul><li>Mapping &Visualizing the Scientific Knowledge
Visualizing and gauging a network of scientific knowledge is an emerging area of interest (Blatt, 2009; Perianes-Rodríguez, Olmeda-Gómez, & Moya-Anegón, 2010; R. Zhao & Wang, 2011).
For example, one of the fundamental approaches is Scientometrics, which is used to gauge and analyze science (LoetLeydesdorff, 2001; Price, 1965).</li></ul>2<br />
Introduction<br /><ul><li>Scientometrics analyses are mainly based on bibliometrics methods, such as citation (Leydesdorff, 1998) and content analysis (Wiles, Olds, & Williams, 2010).
One of the interesting and emerging areas in the field of Scienctometrics is the use of social network concepts for analyzing scientific knowledge (Hou, et al., 2008; Lee & Jeong, 2008; Nagpaul, 2002; Park, Hong, & Leydesdorff, 2005; Park & Leydesdorff, 2009; Pritchard, 1969; Wang, et al., 2010).</li></ul>3<br />
Introduction<br /><ul><li>Social network approaches, utilized in Scientometrics, have the premise of the quantitative analysis of scientific knowledge indicators (e.g. no. of publications and patents) and the collaborative network among researchers (e.g. citation analysis and co-authorship analysis) (Hou, et al., 2008; Lu & Feng, 2009; Newman, 2001; Park, et al., 2005; Park & Leydesdorff, 2008; Pritchard, 1969).</li></ul>4<br />
Introduction<br /><ul><li>However, in this study
However, in this article, we used social network analysis techniques (Wasserman & Faust, 1994) to visualize and gauge the core of scientific knowledge:
Concepts</li></ul>This concept is not yet explored in the field of Scientometrics<br />5<br />
Research Question<br /><ul><li>Can a network among theoretical constructs, models, and concepts used in a particular scientific text be constructed?
Can we visualize and model the underlying casual or theoretical relationship among theoretical constructs and models used in scientific literature by employing social network analysis techniques?</li></ul>6<br />
Network of the Core (NC)<br /><ul><li>NC concept is introduced to achieve three main objects:</li></ul>1) The NC can be used to reveal the hidden characteristics of a research domain, such as:<br /><ul><li>Density (overall density or cohesion of a theoretical domain);
Centrality—to determine the most important or central theories and constructs of a research domain;
Bridge—to determine bridging theories or constructs, etc.</li></ul>2) Conceptualize a research domain and derive the number of possible missing and potential links or researcher hypothesis graphically and mathematically (using directionless NCs).<br />3) Explore the strengths and limitations of a research domain from the structural characteristics perspective.<br />Note: throughout the article we use IS research domain to demonstrate NC concept<br />7<br />
Purpose of NC<br /><ul><li>Quantify a research domain by constructing network of constructs and theories
Assist researchers in finding a missing link, researchable area or hypothesis
Point out possible strengths and shortcomings of a research domain</li></ul>8<br />
NC concept in IS<br /><ul><li>The concept of graphically presenting or conceptualizing a research domain/sub-domain can be found in EG literature, for example,
Saeboet al., (2008) presented the graphical shape of e-participation.
Similarly, Dewan and Riggins (2005), constructed graphical view of digital divide research domain.
More recently, Khan et al., (2010a) proposed the shape of EG research taking place from developing and developed country perspective
Khan et al., (2010b) proposed mapping and visualizing e-government research theoretical constructs using mathematical and conceptual models to identify certain strengths and limitations, such as, identifying a missing links within a theoretical domain and a potential research hypothesis not visible otherwise. </li></ul>9<br />
Network of the core: IS research<br /><ul><li>At the core of Scientific domains, particularly IS research we have:
Network of the core and Information System research<br /><ul><li>Causal conceptualization is done by constructing models showing linked prepositions (casual relations) among constructs supported by theory and empirical studies. Normally, in literature, such a conceptualization is well-known as a research model or model (Boland, 1989).
E.g. Technology Acceptance Model (TAM) (Davis, 1989) and the Information System Success Model (William H. Delone & McLean, 2003).</li></ul>Figure 1<br />12<br />
Network of the core and Information System research<br /><ul><li>Non-casual conceptualization is achieved by constructing graphical models (a whole picture of the domain) based on the general understanding (regardless of casual linkages) of a research domain under consideration: For example,
The shape of EG research by Khan et al. (2011a)
A graphical view of the digital divide research (Dewan and Riggins, 2005), and
The shape of e-participation by Saebo et al. (2008) are good examples of non-casual conceptualization.</li></ul>Figure 2: Shape of the literature on e-government issues/topics (Khan et al., 2011)<br />13<br />
Interdependence<br />Coordination<br />Cooperation<br />Conflict Resolution<br />IS/IT Outsourcing Relationship Success<br />Trust<br />Commitment<br />Information/ Knowledge Sharing<br />Communication<br />Flexibility<br />Cultural Compatibility<br />Network of the core and Information System research<br /><ul><li>Casual Conceptualizing using NC:</li></ul>Swar (2011), utilizing inter-organizational relationship literature, identify the direction of a relationship (or the influence of one factor on another) of the most frequent used IS/IT outsourcing constructs and their influence on IS/IT outsourcing relationship success.<br />Figure 3: Relationship among the most frequent used IS/IT outsourcing factors (Swar, 2011).<br />14<br />
Network of the core and Information System research<br /><ul><li>Casual Conceptualizing using NC:</li></ul>Fig. 3 is useful in understanding some general facts about the IS/IT outsourcing domain, it still conceals a lot of useful information that might be visible by applying social networking analysis techniques. For example,<br /><ul><li>It does not show the strength of the relationship among the constructs (i.e. which constructs are associated strongly in terms of theoretical backing)
Type of association (i.e. positive or negative), and
Relatively important constructs in terms of theoretical/empirical backing (centrality),
The extent to which the constructs are connected (connectedness), or
Density (percentage of actual links verses the possible links) of the domain</li></ul>15<br />
NC of the Swar (2011) model<br />Table 1. IS/IT out sourcing key constructs in terms of centrality measures.<br />Table 2. IS/IT out sourcing domain network level properties.<br />16<br />Figure 4 NC of Swar (2011) model<br />
<ul><li>GMM Further issues Continue…</li></ul>Adding and Removing node (s)<br /><ul><li>A research domain/sub-domain or theory may require us to add a new node (s) to the NC model. This can be simply done by locating the position of the new node based on theory or casual relationship or research domain/sub-domain characteristics. In a similar fashion, node (s) can be removed based on the research area, scope of the study, or theory.
There may be situations where a node (s) may be optional (or can be skipped) while constructing the NC. Again theory, casual relationship, researcher’s choice, or characteristics of research domain/sub-domain will determine the optional node (s). Similar conditions apply to the mandatory component (s). </li></ul>17<br />
Types of NC<br /><ul><li>Direction of the association among nodes
Based on the concept of direction in which one node can affect other, we can construct two types of NC networks. Let’s call them directional NC and direction-less NC.
In the direction-less NC, we are mainly interested in obtaining all possible ways (links) in which one node (s) can affect other in a research domain/sub-domain regardless of the theoretical or causal relationship among the nodes. In other words, in the direction-less NC, theoretical casual relationship among the node (s) is not considered.
The directional NC can only be constructed if a domain/sub-domain is well established and investigator has knowledge of all available theories and casual relationships (links) among nodes of a research domain/sub-domain under study.
All other types of NCs disused below can be either directional or direction-less in nature.</li></ul>18<br />
USES of NC<br />USES of Direction-less NC<br /><ul><li>Researchers may use direction-less NC to find previously un-known research opportunities (see example in later section), guide research directions, or use it identify and develop new theories and relationships among nodes.
Direction-less NC may be applied in situations, for example, where researcher is interested in getting graphical view of a research domain which is new, or does not have enough theoretical background, or is not yet fully recognized discipline.
The primary purpose of directional NC, for example, can be to obtain a graphical view (or network) of a research domain/sub-domain which is well established and needs new nodes for expansion (e.g. interdisciplinary research); or
we are interested to model (graphically and mathematically) the relationship among nodes in a particular research domain/sub-domain; or
to identify the missing links; reveal hidden structures and characteristics of a research domain, for example, connectedness, centrality, density, etc </li></ul>19<br />
Application of NC<br /><ul><li>In this section, we provide some example of directionless (non-casual visualization) and directional (casual visualization) properties of NC to identify the potential area of research.</li></ul>21<br />
Directional NC to identify possible research areas<br /><ul><li> Consider the graphical Model of e-govt. research domain</li></ul>Figure 5 Graphical Model: E-government Current Research and Future research Venues from Adoption Perspective<br />22<br />
Application of NC<br /><ul><li>The number of possible ways a researcher can find a possible research venue conditioned on the underling theory, can be constructed from graphical model shown in figure 1
Let us assume that there are nnumber of societal factors “SF”, m number of organizational factors “OF”, and pnumber of technological factors “TF” that can affect EG adoption behavior.
So, in total we have n + m + p= N number of e-government adoption factors “EGAF” that can affect adoption behavior.</li></ul>possible combinations to choose from EGAF factors, where N= n + m + p <br />possible combinations to choose from 2 SAs<br />23<br />
Application of NC<br /><ul><li>Given that at least one option is selected from each level, the number of ways we can choose and combine each of these levels and factors for finding a possible research question is given X</li></ul>As we know that choosing k number of combinations from n of options is given by formula<br />…………Equation 2<br />24<br />
Application of NC<br /><ul><li>Generalized form of NC
Furthermore, for flexibility reasons, we can generalize the equation 2 so that it can accommodate different setups.
Let’s assume that there are M numbers of “EGAF” factors affecting EG adoption behavior, N number of levels for analyzing these factors, O stages of “EG” development, and P numbers of scopes available as shown in figure 2. </li></ul>Figure 6 Generalized form of NC in electronic government research context<br />25<br />
Application of NC<br />Generalized form of NC<br /><ul><li>Based on the above assumptions, equation 1 can be re-written as:</li></ul>Or<br />Generalized form of NC …………………….Equation 4<br />26<br />
27<br />Directionless NC to identify possible research areas<br />Figure 7 Shape of the Literature on E-Government Issues/Topics (khan et al, 2010)<br />
28<br />Directionless NC to identify possible research areas<br /><ul><li> Using figure 7, how many possible ways a researcher question can be formed?
Can be written as: </li></ul>Solving Eq. 5 produced X = 146,475 (note that it is not density as well) unique ways of<br />connecting the nodes. For simplicity reasons, Fig. 5 shows 32 (0.02%) of the possible ways<br />of combining the nodes Fig. 6 shows 32 (0.02%) of the possible ways<br />of combining the nodes.<br /> For example, we may be interested into investigate: the number of<br />field studies which talk about social issues related to the ex-ante stage of EG from the<br />perspective of developing countries or number of empirical studies which talk about<br />organizational issues related to the ex-post stage of EG from developed countries<br />perspective.<br />……Equation 5<br />
Directionless NC to identify possible research areas<br />Figure 8 NC of e-government research domain from adoption point of view<br />29<br />
Conclusion<br /><ul><li>We showed that, mainly, NCs can be constructed using two broad principles introduced in this article i.e., casual and non-casual conceptualization of a research domain.
non-casual conceptualization (or directionless NCs) can be employed to graphically model (i.e., produce a whole picture or layout) concepts and phenomena residing within a research domain/sub-domain and mathematically derive the number of missing and potential links or researcher hypotheses</li></ul>Article 1<br />30<br />
Conclusion<br /><ul><li>It must be noted that not all scientific domains can be conceptualized or graphically represented due to the complex nature of theories, models, and concepts used.
Thus, the NC approach, particularly directionless NC, can only be applied to a scientific domain given that
the investigator has a deep understanding of the area under consideration,
a conceptual view of the area can be constructed, and
the theory is available or can be constructed to a network association among theoretical constructs, models, and concepts
More research is needed to better understand the NC concept.
For example, future research may construct a network of all the constructs and theories used in EG research and reveal its hidden structural characteristics which will help understand the structural differences among theories.
Other areas open for future studies are constructing domain level, sub-domain level, cross-domain level, and model level NCs for MIS research or social science research as a whole.</li></ul>31<br />
32<br />Thank You!<br />Comments and Suggestions<br />