Results and discussion

Eight factors were extracted from the data and together they explain 100% of the variance in the co-citation matrix. The results of the factor analysis are summarized in Table 3 which shows the factor loadings for the documents in the first 8 factors.

Table 4 Factor analysis Results

Component (Factor) Number of

Documents Author

1 2 3 4 5 6 7 8

v21 Mark Barratt and Alexander Oliverira 0.956

v29 Theodore P. Stank, Patricia J. Daugherty, Chad W. Autry 0.942

v10 YeoNgho Kim, YoungSang Choi and Sang Bong Yoo 0.881

v3 Mihaela Ulieru, Douglas Norrie, Rob Kremer and Weiming Shen 0.862 v31 G. Q. Huang, S. W. Lee, K. L. Mak 0.838

v9 H. Y. Kan, Vincent G. Duffy, Chuan-Jun Su 0.828

v26 Mark R. Cutkosky, Jay M. Tenenbaum, Jay Glicksman 0.745 0.534

Table 5 Factor analysis Results (cont.)

Component (Factor) Number of

Documents Author

1 2 3 4 5 6 7 8

v11 Bernardo A. Huberman, Christoph H. Loch 0.698 v28 Richard L. Daft, Robert H. Lengel 0.486 0.571 0.598

v12 Scott Kownslar 0.554

v13 Bhavani Thuraisingham , Amar Gupta , Elisa Bertino , Elena Ferrari 0.949

v4 Hwagyoo Park, Woojong Suh, Heeseok Lee 0.907

v38 Clyde W. Holsapple and Meenu Singh 0.907

v39 Alea M. Fairchild, Ryan R. Peterson 0.595

v6 Robert H. Guttman, Alexandros G. Moukas and Pattie Maes 0.942 v7 Pattie Maes, Robert H. Guttman and Alexandros G. Moukas 0.827

v35 Ralph L. Keeney 0.685

v5 Anthony Karageorgos, Simon Thompson, Nikolay Mehandjiev 0.672

v14 W.D. Li, W.F. Lu, J.Y.H. Fuh, Y.S. Wong 0.847

v15 Lihui Wang, Weiming Shen, Helen Xie, Joseph Neelamkavil, Ajit

Pardasani 0.79

v36 G. Q. Huang and K. L. Mak 0.598 0.622

We have labeled all 39 documents and ranked them, loading most heavily on each, 0.4 being an arbitrary minimum cutoff point. If a documents does not load 0.4 or higher on any of the factors, the document’s highest loading, whatever it may be, is presented. As is usual in this type of analysis, documents with less than a 0.4 loading were dropped from the final results (J.Hair, R.Anderson, R.Tatham and W.Black, 1998).

We tentatively assigned the documents with high associated loadings. Implicitly, our interpretation of the analysis results is that the CPC field is composed of at least eight different sub fields: Supply Chain partners and CPFR, Organizational Model design and Coordination mechanism, Web Collaborative design Model, Organization Knowledge and Management Knowledge, System and Solve CPC problems, c-commerce combines e-commerce, Design for e-commerce, and CAD system. See Table 4. All 39 papers load on at least one factor, V26 load on two and V28 on three, as shown by subscripts to their documents numbers. The 8 factors help in understanding relationships, such as that between businesses and IS strategies.

Table 6 Documents factor loadings at 0.40 or higher (decimals omitted).^a

Factor 1

Supply Chain partners, CPFR Factor Loading v21 Exploring the experiences of collaborative planning initiatives 0.96 v29 Research paper Collaborative planning: supporting automatic

replenishment programs 0.94

v16 Collaborative planning forecasting and replenishment: new solutions

needed for mass collaboration 0.89

v33 The collaborative supply chain: a scheme for information sharing and

incentive alignment 0.82

v22 Implementing collaborative forecasting to improve supply chain

performance 0.81

v30 Retail exchanges: a research agenda 0.77

v17 Collaborative planning, forecasting, and replenishment 0.63

Factor 2

Organizational Model design, Coordination mechanism Factor Loading v2 A Model for Studying R&D-Marketing Interface in the Product

Innovation Process 0.84

v27 Managing Trust and Commitment in Collaborative Supply Chain

Relationships 0.82

v24 Interdepartmental Interdependence and Coordination: The Case of the

Design/Manufacturing Interface 0.81

v23 Information Links and Electronic Marketplaces: The Roles of

Inter-Organizational Information Systems in Vertical Markets 0.75 v20 Creating a custom mass-production channel on the Internet 0.72 v8 An exploratory study of small business Internet commerce issues 0.70 v18 Competing for the Future-Breakthrough Strategies for Seizing

Control of Your Industry and Creating the Markets of Tomorrow 0.67 v28 ₃ Organizational information requirements, media richness and

structural design 0.49

Factor 3

Web Collaborative design Model Factor Loading v10 Brokering and 3D collaborative viewing of mechanical part models

on the Web 0.88

Table 7 Documents factor loadings at 0.40 or higher (decimals omitted).^a (cont.)

Factor 4

Organization Knowledge, Management Knowledge Factor Loading v1 A Dynamic Theory of Organizational Knowledge Creation 0.97

v37 What’s Your Strategy for Managing Knowledge? 0.95

v19 Competition for Competence and Inter-Partner Learning within

International Strategic Alliance 0.86

v25 Knowledge Management: An Organizational Capabilities Perspective 0.79 v28 ₂ Organizational information requirements, media richness and

structural design 0.57

Factor 5

System, Solve CPC problems Factor Loading v32 Technology Adaptation The case of implementing a collaborative

product commerce system to new product design 0.93 v34 The Mutual Knowledge Problem and Its Consequences for Dispersed

Collaboration 0.93

v11 Collaboration, motivation, and the size of organizations 0.70 v28 1

Organizational information requirements, media richness and

structural design 0.60

v12 Collaborative commerce 0.55

Factor 6

c-commerce combines e-commerce Factor Loading v13 Collaborative commerce and knowledge management 0.95 v4 A role-driven component-oriented methodology for developing

collaborative commerce systems 0.91

v38 toward unified view of electronic commerce, electronic business, and

collaborative commerce: a knowledge management approach 0.91 v39

Business-to-Business Value Drivers and eBusiness Infrastructures in Financial Services: Collaborative Commerce Across Global Markets and Networks

0.60

Factor 7

Design for e-commerce Factor

Loading

v6 Agent-mediated electronic commerce: a survey 0.94

v7 Agents that buy and sell 0.83

v35 The Value of Internet Commerce to the Customer 0.69 v5 Agent-Based System Design for B2B Electronic Commerce 0.67

Table 8 Documents factor loadings at 0.40 or higher (decimals omitted).^a (cont.)

Factor 8

CAD, System Factor

Loading v14 Collaborative computer-aided design—research and development

status 0.85

v15 Collaborative conceptual design—state of the art and future trends 0.79 v36 WeBid: A web-based framework to support early supplier

involvement in new product development 0.62

v26 2 Madefast: Collaborative Engineering over the Internet 0.53

aSubscripts: 1 = first appearance, 2 = second appearance, 3 = third appearance.

Figure 8 is a graph of the visualization of the semantic space derived from 39 core papers.

The sphere and line each represent there relationship between two core papers. The colors of these spheres indicate the “co-citation number in co-citation matrix (from table 2)” of corresponding documents: the first-document and two inclusive all-document co-citation analyses based on the two dataset, we count once when the first document and n document have same co-citation document, no matter how many co-citation appear. For example, From Table 2 showed V5 have connection with V7 and V27 that we count as two lines, it will connect V5 with V7 and V5 with V27 as we can see from Figure 8.

The purple colors represent the core documents have same co-citation document from paper number one to five, black represent six to ten, yellow represent eleven to sixteen, and red represent seventeen to nineteen.

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Figure 8 The Core Documents and their Interrelationships

In the subsequent document co-citation analysis and associated co-citation maps, this tendency becomes even stronger and more intuitive. Figure 9 summarizes the intellectual structure within the core. Documents are co-located according to a multidimensional scaling of their interconnectedness in a two-dimensional space. The relative amount of co-citations of documents is indicated by the thickness circle lines and all factors received clear view.

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Figure 9 The Core documents and their factor analyses

The program UCINET 6.0 was investigated in more detail with the help of an example data set. UCINET 6.0 (a social network analysis software package) is a comprehensive program for the analysis of social networks and other proximity data. It is probably the best known and most frequently used software package for the analysis of social network data and contains a large number of network analytic routines (Huisman M., Marijtje A.J. van Duijn., 2003). By applying the UCINET 6.0 program the given 39 CPC documents identified as a core data set, a social network analysis graph emerges in Figure 10.

Figure 10 Social network analyses for CPC documents

Figure 11 summarizes the intellectual structure within the factors. Factors are co-located according to a multidimensional scaling of their interconnectedness in a two-dimensional space. The relative amount of factors is indicated by the thickness circle lines and all other factors received clear view.

Factor 6

Factor 1

Factor 8

Factor 3 Factor include 2, 4, 5 and 7

Figure 11 The social network analyses and their interrelationship

Overall, the graph visualizes the relative positioning of documents within the core. Their location stems from the fact that the graph reflects interrelations with in the core connected to all co-citation documents. The more similar the co-citation documents, the closer they will be displayed in the graph: the documents linked to many other source documents will be located in areas close to the graph’s center (McCain 1986).

As the figure 8 and 9 show, the different cores are grouped together depending upon the documents interrelationship. The eight resulting group represent different theoretical trends within the CPC study field. Core papers number 19, 24, 28, 34 and 37 are the necessarily cited key paper in this field.

analyzing the future research work of CPC, the researcher mostly focuses on Organizational model, Organizational Knowledge, Management Knowledge and System such as factor two, four, five and seven.

The factor one, three, six and eight been labeled as Supply Chain partners, CPFR, Web Collaborative design Model, c-commerce combines e-commerce and CAD System are not very connected with this field although they may connect CPC for some small idea. For the future research factor two, four, five and seven (Organizational Model design, Coordination mechanism, Organization Knowledge, Management Knowledge, System, Solve CPC problems, Design for e-commerce) is the most clarified for the search area.