Authors:Gohar Feroz Khan*

Junhoon Moon**Han Woo Park*

*Department of Media & Communication, YeungNam University, Republic of Ko-rea**Information Management & Marketing, College of Agriculture and Life Sci-ences, Seoul National University, Republic of Korea

Mapping and Visualizing The Core of Scientific Domains:

Information System Research

Prepared for COLLNET 2011, Seventh International Conference on Webometrics, Informetrics and Scientometrics (WIS), 20-23 September, 2011, Istanbul Bilgi Uni-

versity, Istanbul, Turkey, http://collnet.cs.bilgi.edu.tr/program/programme/

An updated version of this article is accepted for publication in the Scientomet-rics journal

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Introduction

Mapping &Visualizing the Scientific Knowl-edge

Visualizing and gauging a network of scien-tific knowledge is an emerging area of inter-est (Blatt, 2009; Perianes-Rodríguez, Olmeda-Gómez, & Moya-Anegón, 2010; R. Zhao & Wang, 2011).

Science of analyzing Science For example, one of the fundamental ap-

proaches is Scientometrics, which is used to gauge and analyze science (Loet Leydesdorff, 2001; Price, 1965).

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Introduction

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 so-cial 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).

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Introduction

Social network approaches, utilized in Scien-tometrics, have the premise of the quantita-tive analysis of scientific knowledge indica-tors (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).

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Introduction

However, in this study

However, in this article, we used social net-work analysis techniques (Wasserman & Faust, 1994) to visualize and gauge the core of scientific knowledge: theoretical constructs, models, and Concepts

This concept is not yet explored in the field of Scientometrics

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Research Question

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 the-oretical constructs and models used in scien-tific literature by employing social network analysis techniques?

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Network of the Core (NC)

NC concept is introduced to achieve three main objects:

1) The NC can be used to reveal the hidden characteristics of a research domain, such as: 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.

2) Conceptualize a research domain and derive the number of possible missing and potential links or researcher hypothesis graphically and mathematically (using directionless NCs).

3) Explore the strengths and limitations of a research domain from the structural characteristics perspective.

Note: throughout the article we use IS research domain to demonstrate NC concept

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Purpose of NC

Quantify a research domain by constructing network of constructs and theories

Assist researchers in finding a missing link, researchable area or hy-pothesis

Increase understanding

Point out possible strengths and shortcomings of a research domain

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NC concept in IS

The concept of graphically presenting or conceptualizing a research domain/sub-domain can be found in EG literature, for example,

Saebo et 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 re-search taking place from developing and developed country perspec-tive

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, identify-ing a missing links within a theoretical domain and a potential re-search hypothesis not visible otherwise.

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Network of the core: IS research

At the core of Scientific domains, particularly IS re-search we have:

Theories Mainly, there are two types of theories: classification and causal-

ity base theories (Parsons & Shils, 1962).

Based on theories we conceptualize Models and Theo-retical constructs.

Models Models are used for a more specific representation of the under-

lying ideas of a theory (Boland, 1989)

Theoretical constructs: Models are constructed using theoretical constructs Constructs can be of two types: directly observable and not di-

rectly observable (latent constructs).

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Network of the core and Information System research

In IS research, mainly, two types of research domain conceptualiza-tions (theory and construct) can be found:

Causal conceptualization and Non-casual conceptualization

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Network of the core and Information System research

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 concep-tualization 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).

Figure 1

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Network of the core and Information System research

Non-casual conceptualization is achieved by constructing graphical models (a whole picture of the domain) based on the gen-eral 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 di-vide research (Dewan and Rig-gins, 2005), and

The shape of e-participation by Saebo et al. (2008) are good ex-amples of non-casual conceptual-ization.

Figure 2: Shape of the literature on e-government issues/topics (Khan et al., 2011)

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Network of the core and Information System research

Casual Conceptualiz-ing using NC:

Swar (2011), utilizing in-ter-organizational relation-ship 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.

Interdependence

Conflict Resolution

Information/ Knowledge

Sharing

Coordination

Trust

Communication

Cooperation

Commitment

Flexibility Cultural Compatibility

IS/IT Outsourcin

g Relationship Success

Figure 3: Relationship among the most frequent used IS/IT outsourcing factors (Swar, 2011).

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Network of the core and Information System research

Casual Conceptualizing using NC:

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,

It does not show the strength of the relationship among the con-structs (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 (connected-ness), or

Density (percentage of actual links verses the possible links) of the domain

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NC of the Swar (2011) model

Construct Degree Betweenness Eigenvector

Trust 9 27.583 0.397

Cooperation 8 11.083 0.406

Mutual Understanding 7 6.583 0.366

Information/Knowledge Sharing 5 3.5 0.332

Communication 4 3 0.273

IT Outsourcing 4 1 0.299

Conflict Handling 3 1 0.233

Flexibility 3 1 0.233

Cultural compatibility 3 1 0.233

Commitment 3 0.25 0.201

Confidentiality 3 1 0.233

Coordination 1 0 0.079

Interdependence 1 0 0.079

Table 1. IS/IT out sourcing key constructs in terms of centrality measures.

No. of Links Density Average Degree Efficiency Hierarchy21 0.19 1.91 0.85 1

Table 2. IS/IT out sourcing domain network level properties.

Figure 4 NC of Swar (2011) model

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Adding and Removing node (s)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 the-ory or casual relationship or research domain/sub-domain characteristics. In a similar fashion, node (s) can be re-moved based on the research area, scope of the study, or theory.

Optional & Mandatory node (s)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 compo-nent (s).

GMM Further issues Continue…

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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.

Direction-Less NCIn 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.

Directional NCThe 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.

Types of NC

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USES of NC

USES of Direction-less NC

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.

USES of Directional NC

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

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Types of NC

Domain level NC

Sub-domain level NC

Cross-Domain Level NC (e.g. for interdisciplinary research venues)

Model level NC

Inter-model Level NC

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Application of NC

In this section, we provide some example of directionless (non-ca-sual visualization) and directional (casual visualization) properties of NC to identify the potential area of research.

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• Consider the graphical Model of e-govt. research domain

Figure 5 Graphical Model: E-government Current Research and Future research Venues from Adoption Perspective

Directional NC to identify possible research areas

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The number of possible ways a researcher can find a possible re-search venue conditioned on the underling theory, can be con-structed from graphical model shown in figure 1

Let us assume that there are n number of societal factors “SF”, m number of organizational factors “OF”, and p number of technologi-cal 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.

possible combinations to choose from EGAF factors, where N= n + m + p

possible combinations to choose from 5 Las

possible combinations to choose from 5 EGD stages

possible combinations to choose from 2 SAs

Application of NC

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Given that at least one option is selected from each level, the num-ber of ways we can choose and combine each of these levels and factors for finding a possible research question is given X

…. Equation 1, possible combinations to choose

from

…………Equation 2

So, equation 1 can be re-written as

As we know that choosing k number of combinations from n of options is given by formula

For N = 6, solving Eq. 2 produces 181,629 ways to choose the factors (constructs) for predicting e-government adoption behavior; given that theory is available or can be constructed. Note that the number generated (181,629) is different than the density property of the NC. It shows the number of unique combinations of factors (constructs) we can choose from in a NC to formulate a research hypothesis, not the total number of ways in which the constructs can be linked (density).

Application of NC

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Generalized form of NCFurthermore, 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.

Figure 6 Generalized form of NC in electronic government research context

Application of NC

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Generalized form of NC

Based on the above assumptions, equation 1 can be re-written as:

Generalized form of NC …………………….Equation 4

Or

Application of NC

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Directionless NC to identify possible research areas

Figure 7 Shape of the Literature on E-Government Issues/Topics (khan et al, 2010)

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• Using figure 7, how many possible ways a researcher question can be formed?

Here we have 6 research methodologies, 5 stages of EG development Two levels of analysis: developing country Scope is three level

Using equation 4

Can be written as:

Solving Eq. 5 produced X = 146,475 (note that it is not density as well) unique ways ofconnecting the nodes. For simplicity reasons, Fig. 5 shows 32 (0.02%) of the possible waysof combining the nodes Fig. 6 shows 32 (0.02%) of the possible waysof combining the nodes. For example, we may be interested into investigate: the number offield studies which talk about social issues related to the ex-ante stage of EG from theperspective of developing countries or number of empirical studies which talk aboutorganizational issues related to the ex-post stage of EG from developed countriesperspective.

Directionless NC to identify possible research areas

……Equation 5

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Figure 8 NC of e-government research domain from adoption point of view

Directionless NC to identify possible research areas

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Conclusion

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.

Directional NCsThe units of analysis in casual conceptualization (or directional NCs) are specific representations of a research domain, for example, research models and constructs.

Thus, they can mainly be used to identify hidden structures that may reside within a complex research domain not visible otherwise using social networking concepts and techniques

Directionless NCsnon-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

Article 1

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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

Future researchMore 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.

Conclusion

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Thank You!Comments and Suggestions