Social Network Analysis-Opportunities in PPPM

Construction productivity have been a classic concern with negative growth in the period 1964-2012 , the most likely reasons behind this situation may be the highly fragmented project workflows, procurement systems based on competitive rather than collaborative teams, paper-based project delivery systems (PDS) with a low use of technology in their processes affecting the flow of data and information between the stakeholders creating conflicts and lowering the quality of the projects along all their life cycle.

With the introduction of CPM/PERT in 1958’, the use of technology has been gradually extended in the Project Management discipline , but is not until the past decade when their use is being more extensive, the evolution of the technology applied in projects is transforming the Building Industry moving from a paper-based towards a BIM-based type of projects shifting to a lean collaborative processes .

Since 2000’s it has been observed a continuous growth of complexity in projectscaused by the increasingly use of technology, project size/scale, construction methods, stakeholders diversity and overlapping processes in the life cycle of a project, as a result Project Managers have been facing an increase of challenges at strategic, organizational and human resources level.

Projects are team-based developed, currently, as a result of the technological advancements projects are producing an immense amount of data, sorting useful data to transform in coordinate and productive information that flows through all the processes along the life cycle of a project is crucial in their success.

Social Network Analysis (SNA) appears as recently technique that explore, maps and measure data & information flows and relationship between organizations, groups, people or other entities.

The aim of this article is to explore different opportunities in the combined use of Data Analysis and SNA for project managers.

Social Network Analysis(SNA)

Introduction & Historical evolution

Figure 1: Evolution of SNA published articles (Rousseau and Otte, 2002)

Organizations are composed by individuals and, based in their ranks, assembled in different groups organized in a hierarchical structure. Work activities and tasks constitutes the main interaction type between these individuals establishing a formal network.

However, within the same organization, interaction between individuals could be based in their work-space proximity, nationality, sport preference, etc. in this case creating different groups and establishing an informal network.

Individuals and organizations are the main actors from either formal or informal structures and as a result of their interaction within a network or with other networks, different behaviour patterns and influential actors could be identified, the analysis of these network dynamics constitutes the object of study of Social Network Analysis (SNA).

According Freeman the historical evolution of SNA comprises the following periods [7]:

1. Everything before 1929, focused on the interaction and relationship between actors

2. From 1929 to 1939, focused in a systematic collection of data between this actors

3. From 1940 to 1969, with the introduction of graph theories

4. From 1970 until now, the modern era based on the use of mathematical/computational tools to analyse these interactions

In the past years with the birth of internet and the technological advancements, SNA gained relevance; research studies have been reformulated and reconsider their approach, being the use of network studies an important part, not only, of sociological studies but also incorporated as a strategy used by different disciplines [8] (Figure 1).

Stakeholders perspective

Open systems , as organizations, could be influenced by internal or external changes in the environment and at the same time the interconnection between systems could be altered as a result of the above mentioned changes. The propagation of changes would be affected by the network type, cohesive or bridging.

Methodology

In order to identify an asses the outputs of Social Network Analysis-based articles, a structured approach was adopted:

• Search process, different iterations and combination of key words were conducted, the final retrieved articles were consequence of a refine process.

• Distribution of articles per year

• List of journals and account of articles included in each journal

• Categorization of the articles by subject area of study

• Analysis of the abstracts of the retrieved articles, identification of key words an aspects

The following common keywords were considered in the initial search, “Social Network”, “Social Network Analysis” “Stakeholder”, “BIM”, “Lean Construction”, “Engineering Systems”, “Construction Project Management”.

Search process

First iteration, through “DTU Findit” database an initial search of articles was conducted with “Social Network” and “Social Network Analysis” as keywords, the total amount of entries founded were 113.043 and 1.890 respectively.

In a first view of the searched articles it was noticed that “SNA” was used as keyword in “Social Network Analysis” articles and, in many cases, “Social Network” was referred in “Social Media” articles.

To refine the search “Social Network” was discarded.

A second iteration with the keyword “Social Network Analysis” and the operator “AND” in a successive combination with the keywords “Stakeholder”, “BIM”, “Lean Construction” was conducted, the total amount of entries founded were 2.297, 2 and 5 respectively.

It was observed that within the search “Social Network Analysis AND Stakeholder” there was still some entries referred to “Social Media”; with the search of “Social Network Analysis AND BIM” the entries corresponded with the intended search and with the search of “Social Network Analysis AND Lean Construction” there was 2 entries that corresponded with medicine subject areas.

A third iteration was conducted, the prefix “ab: abstract” and the operator “AND” were selected in the formula “ab: (SNA) AND Stakeholder”, a total 63 entries were obtained, 8 entries corresponded with repeated articles in different journals, 2 entries matched with the entries founded in the search “Social Network Analysis AND BIM” and 1 entry corresponded with one paper that reviewed the status of research SNA in construction project management context (CPM) that is coincident with the aim of this article.

Findings

Table 1: Papers distribution by subject area of study (Landivar, 2016)

Figure 2: Evolution of SNA published articles (Landivar, 2016) adapted from (Zheng, Le, Chan, Hu, Li, 2015)

Table 2: Papers distribution by network and organizational environment aspects (Landivar, 2016)

Based in the combination of keywords used in the search the following papers were retrieved:

• Search area “Social Network Analysis AND Stakeholder”, 52 papers, from now on will be referred in tables or figures as “SNA + Stakeholder” or “Stakeholder”

• Search area “Social Network Analysis AND BIM”: 2 papers, from now on will be referred in tables or figures as “SNA + BIM” or “BIM”

• Search area “Social Network Analysis AND Lean Construction”: 3 papers, from now on will be referred in tables or figures as “SNA + Lean” or “Lean Construction” or “Lean”

• One paper retrieved in the search area “SNA + Stakeholder” was assigned with its own search area “Social Network Analysis and Construction Project Management”, from now on will be referred in tables and figures “SNA +CPM” or “CPM”

This paper is a review of 63 studies in a CPM context published in the period 1997-2015.

The distribution of papers by year can be seen in figure 2.

The analysis of the retrieved papers shows different aspects of interests that have been grouped in following categories:

Group 1.Publication contribution, to determine the publication that contributed the most, the amount of papers produced by every journal was accounted, a categorization by the main subject areas of study and the index of research of impact was also included.

It was observed that from the 58 retrieved journals, 10 papers were published in the context of different proceedings, the rest of papers were published in 43 journals.

Group 1.1, journals that published more than 1 paper with an h-index over 90:

• Journal of Cleaner Production with 2 from UK with “Business, Management and Accounting, Energy, Engineering, Environmental Science” as subject area with an h-index of 96

Group 1.2, journals that published more than 1 paper (+ paper from group 1.1):

• Ecology and Society with 3 papers from Canada with “Environmental Science” as subject area with an h-index of 87

• Journal of Environmental Planning and Management with 2 papers from UK with “Chemical Engineering, Environmental Science, Social Sciences” as subject area with an h-index of 46.

• International Journal of Information Technology and Decision Making with 2 papers from Singapore with “Computer Science” as subject area with an h-index of 28

Group 1.3, journals that published papers with an h-index of 90 or above (+ paper from group 1.1):

• Global Environmental Change with 1 paper from UK with “Environmental Science, Social Sciences” as subject area with an h-index of 103

• Waste Management with 1 paper from UK with “Environmental Science” as subject area with an h-index of 92

• Industrial Marketing Management with 1 paper from USA with “Business, Management and Accounting” as subject area with an h-index of 90

Group 2.Type of research, qualitative or quantitative, all the papers have a qualitative approach however in regards of the quantitative type, the papers that even though has not explicit mention of the quantitative aspect or intended to measure some of the results obtained in their studies were included.

The distribution of papers based on the type of research were:

• Qualitative, 58 papers (100%)

• Quantitative, 15 papers (26%)

Group 3.Organizational level aspects, such as strategic, tactical or operational in this category 8 papers (14%) had explicit mention of the strategic value of the paper, it is assumed that the rest of papers covers also the tactical and the operational levels.

Table 1 shows a summary of group 1, 2 , 3, 7.

Table 2 shows a summary of group 4, 5.

Group 4.Network aspects, the aspects that could have an influence and affect the equilibrium in this systems have been included in this category. The following list shows aspects that were grouped in one item:

• Information Flows= Data flows, Knowledge flows, communication

• Relationship= Connectivity, Inter-connectivity

• Collaboration= Sharing, shared information, shared data, shared knowledge, cross-functional collaboration, Big room

• Performance= Team performance, Individual performance, measurements, efficiency, productivity

Figure 3: Distribution of Network aspects (Landivar, 2016)

Figure 4: Distribution of Organizational Environment aspects (Landivar, 2016)

Group 5.Organizational environment aspects, in this category were grouped all the aspects that could have an impact on the equilibrium of an individual or in an organization network.

Impediment aspects were grouped with Barriers as one item.

Group 6.Project Management aspects, in this category were grouped all the concepts that were related with the discipline of Project Management (PM), based on PMBOK 5th edition () standard, initially it was intended to allocate the concepts to each knowledge domain, however as many aspects could be referred to more than one domain this intent was discarded.

Table 3 shows a summary of group 6.

Group 7.Technological aspects, within this aspect different concept were consider and grouped as follows:

• Technology= Technology, Internet, Web-based, Knowledge Management Systems (KMS)

Appendix 1

In this section are listed all the documents used in the tables and figures.

On September the 17th “DTU Findit” database has been used for search documents that lately are used in the conformation of tables and figures.

A first search with the phrase “Social Network” was used with a total of 113043 entries.

A second search with the phrase “Social Network Analysis (SNA)” was used with a total of 1890 entries.

The prefix “ab: abstract” and the operator “AND” were selected for a third search on the formula “ab: (SNA) AND Stakeholder” with a total 63 entries, 3 entries corresponded to repeated papers, finally a total of 60 documents were retrieved.

[1]C. Prell, K. Hubacek, C. Quinn, and M. Reed, “‘Who’s in the network?’ When stakeholders influence data analysis,” Syst. Pract. Action Res., vol. 21, no. 6, pp. 443–458, Dec. 2008. [2]S. Yang and L. Huang, “Web mining as a valuable tool in technology commercialization potential evaluation,” in 2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008, 2008. [3]D. Vandenbroucke, “A Network Perspective on Spatial Data Infrastructures: Application to the Sub-national SDI of Flanders (Belgium),” Trans. Gis, vol. 13, no. s1, 2009. [4]D. Vandenbroucke, J. Crompvoets, G. Vancauwenberghe, E. Dessers, and J. Van Orshoven, “A network perspective on spatial data infrastructures: Application to the sub-national SDI of flanders (Belgium),” in Transactions in GIS, 2009, vol. 13, no. SUPPL. 1, pp. 105–122. [5]G. F. Secundo G., “Designing, managing and assessing a {Web} 2.0 learning community to enhance inquiry based learning,” Int. J. Web Based Communities, vol. 6, no. 2, pp. 164–182, 2010. [6]Ö. Bodin, “Social Networks and Natural Resource Management: Uncovering the Social Fabric of Environmental Governance,” Soc. Networks Nat. Resour. Manag. Uncovering Soc. Fabr. Environ. Gov., pp. 1–374, 2011. [7]Y. M. Chen and M.-Y. Chen, “Social network analysis aided product development project management: IC Substrates case study,” Manag. Sci. Lett., vol. 1, no. 2, pp. 107–114, Apr. 2011. [8]I. H. El-adaway, “Relational contracting and high-performance project outcomes,” in Proceedings, Annual Conference - Canadian Society for Civil Engineering, 2011, vol. 3, pp. 1946–1955. [9]F. G., T. F.W., and A. D., “Infectious syphilis in New Brunswick: Using data for action in a small Canadian province,” Sex. Transm. Infect., vol. 87, p. A354, 2011. [10]K. Sedereviciute and C. Valentini, “Towards a More Holistic Stakeholder Analysis Approach. Mapping Known and Undiscovered Stakeholders from Social Media,” Int. J. Strateg. Commun., vol. 5, no. 4, pp. 221–239, Oct. 2011. [11]C. Stein, H. Ernstson, and J. Barron, “A social network approach to analyzing water governance: The case of the Mkindo catchment, Tanzania,” Phys. Chem. Earth, vol. 36, no. 14–15, pp. 1085–1092, Jan. 2011. [12]K. Vance-Borland and J. Holley, “Conservation stakeholder network mapping, analysis, and weaving,” Conserv. Lett., vol. 4, no. 4, pp. 278–288, Aug. 2011. [13]G. Woodill, D. Ed, S. Analyst, S. Wright, and M. I. St, “Improving Knowledge Flow in Organizations in Organizations,” 61st Annu. Iie Conf. Expo Proc., no. March, 2011. [14]C. Crisostomo, M. R. Bteich, H. Moschitz, and P. Pugliese, “Organic farming policy in Portugal: Analysis of the policy network,” in New Medit, 2012, vol. 11, no. 4 SPECIAL, pp. 27–30. [15]M. de Miguel Molina, “Proposed use of social network analysis of public policies for the integration of the Unaccompanied Foreign Minors (UFM). Study of the case of the Valencia Region. Propuesta de uso del análisis de redes de actores de políticas públicas para la gestión de,” Rev. Sobre La Infanc. Y La Adolesc., 2012. [16]H. Doloi, “Assessing stakeholders ’ influence on social performance of infrastructure projects,” Facilities, vol. 30, no. 11, pp. 531–550, Aug. 2012. [17]T. L. Frantz, “Advancing complementary and alternative medicine through social network analysis and agent-based modeling.,” Forsch. Komplementarmed., vol. 19, no. suppl 1, pp. 36–41, 2012. [18]A. Herrero Blasco, “Propuesta de uso del análisis de redes de actores de políticas públicas para la gestión de la integración de los Menores Inmigrantes No Acompañados (MINA). Estudio del caso de la Comunidad Valenciana Proposed use of social network analysis of public polic,” Rev. Sobre La Infanc. Y La Adolesc., 2012. [19]N. Meese and C. McMahon, “Analysing sustainable development social structures in an international civil engineering consultancy,” J. Clean. Prod., vol. 23, no. 1, pp. 175–185, Mar. 2012. [20]C. B. Wonodi, L. Privor-Dumm, M. Aina, A. M. Pate, R. Reis, P. Gadhoke, and O. S. Levine, “Using social network analysis to examine the decision-making process on new vaccine introduction in Nigeria,” Health Policy and Planning, vol. 27, no. SUPPL.2. pp. ii27–ii38, 01-May-2012. [21]F. Solis, J. V. Sinfield, and D. M. Abraham, “Hybrid Approach to the Study of Inter-Organization High Performance Teams,” J. Constr. Eng. Manag., vol. 139, no. April, pp. 379–392, Apr. 2013. [22]M. Caniato, M. Vaccari, C. Visvanathan, and C. Zurbr??gg, “Using social network and stakeholder analysis to help evaluate infectious waste management: A step towards a holistic assessment,” Waste Manag., vol. 34, no. 5, pp. 938–951, May 2014. [23]H. Doloi, “A framework for supporting planning and development of infrastructure projects from a societal perspective,” in 31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings, 2014, pp. 904–909. [24]M. Furmankiewicz, ??ine Macken-Walsh, and J. Stefa??ska, “Territorial governance, networks and power: Cross-sectoral partnerships in rural Poland,” Geogr. Ann. Ser. B Hum. Geogr., vol. 96, no. 4, pp. 345–361, Dec. 2014. [25]S. Gretzinger, S. Lemke, and W. Matiaske, Managing Human Resource Based Intellectual Capital in a Global Setting: the Impact of Cultural Practices on the Effectiveness of Retention Incentives. Academic Conferences Publishing International, 2014. [26]D. Jarman, E. Theodoraki, H. Hall, and J. Ali-Knight, “Social network analysis and festival cities: an exploration of concepts, literature and methods,” Int. J. Event Festiv. Manag., vol. 5, no. 3, pp. 311–322, Oct. 2014. [27]S. F. M. Nogueira and J. C. M. R. Pinho, “Examining tourism stakeholder networks and relationship quality: The specific case of peneda gerês national park (PNPG),” Rev. Port. Estud. Reg., vol. 36, no. 1, pp. 22–33, 2014. [28]H. Quang, X. Shen, and A. Akbarnezhad, Proceedings of the 31st International Symposium on Automation and Robotics in Construction and Mining : ISARC 2014 theme : automation, construction and environment. 2014. [29]R. K. Shepherd and S. D. Pryke, “Regional rail planning; a study of the importance of ‘steering’ and ‘pragmatism’ in stakeholder networks,” Eur. Manag. J., vol. 32, no. 4, pp. 616–624, Aug. 2014. [30]R. Taylor, J. Forrester, L. Pedoth, and N. Matin, “Methods for Integrative Research on Community Resilience to Multiple Hazards, with Examples from Italy and England,” Procedia Econ. Financ., vol. 18, pp. 255–262, 2014. [31]R. J. Yang, “Stakeholder-associated risk networks in green buildings: China Versus Australia,” Proc. 30th Annu. Assoc. Res. Constr. Manag. Conf. Arcom 2014, 2014. [32]R. J. Yang and P. X. W. Zou, “Stakeholder-associated risks and their interactions in complex green building projects: A social network model,” Build. Environ., vol. 73, pp. 208–222, Mar. 2014. [33]L. Calvet-Mir, S. Maestre-Andrés, J. L. Molina, and J. van den Bergh, “Participation in protected areas: A social network case study in catalonia, Spain,” Ecol. Soc., vol. 20, no. 4, p. art45, 2015. [34]D. Contandriopoulos and C. Larouche, “009 op: a sociogram is worth a thousand words: proposing a method for the visual analysis of narrative data,” BMJ Open, vol. 5, no. 4, p. UCLSymposiumAbstracts9, Apr. 2015. [35]E. A. Cudney, S. M. Corns, and S. K. Long, “Improving knowledge sharing in healthcare through social network analysis,” Int. J. Collab. Enterp., vol. 4, no. 1/2, p. 17, 2015. [36]E. A. Cudney, S. M. Corns, and S. K. Long, “Improving knowledge sharing in healthcare through social network analysis,” Int. J. Collab. Enterp., vol. 4, 2015. [37]H. Doloi and B. Raphael, “Drivers And Impediments Of Building Information Modelling From A Social Network Perspective,” in 32nd International Symposium on Automation and Robotics in Construction and Mining: Connected To the Future, Proceedings, 2015. [38]J. Georis-Creuseveau, “Analyse de réseaux sociaux des usages des Infrastructures de Données Géographiques Le cas des acteurs coˆtiers en France et des IDG qu’ils Mobilisent,” CEUR Workshop Proc., vol. 1535, 2015. [39]J. Hauck, C. Stein, E. Schiffer, and M. Vandewalle, “Seeing the forest and the trees: Facilitating participatory network planning in environmental governance,” Glob. Environ. Chang., vol. 35, pp. 400–410, Nov. 2015. [40]G. Hosseininia, P. R. Khachak, M. Nooripoor, S. Van Passel, and H. Azadi, “Understanding communicational behavior among rangelands’ stakeholders: application of social network analysis,” J. Environ. Plan. Manag., vol. 59, no. April, pp. 1–22, Feb. 2015. [41]C. Z. Li, J. Hong, F. Xue, G. Q. Shen, X. Xu, and M. K. Mok, “Schedule risks in prefabrication housing production in Hong Kong: A social network analysis,” Journal of Cleaner Production, vol. 134, pp. 482–494, Oct-2015. [42]W. Lyles, “Using social network analysis to examine planner involvement in environmentally oriented planning processes led by non-planning professions,” J. Environ. Plan. Manag., vol. 58, no. 11, pp. 1961–1987, Nov. 2015. [43]M. Petrescu-Prahova, B. Belza, K. Leith, P. Allen, N. B. Coe, and L. A. Anderson, “Using Social Network Analysis to Assess Mentorship and Collaboration in a Public Health Network,” Prev Chronic Dis, vol. 12, no. 8, p. E130, Aug. 2015. [44]K. Still, J. Huhtamaki, and M. G. Russell, “New insights for relational capital.,” Electronic Journal of Knowledge Management, vol. 13, no. 1. pp. 13–28, 2015. [45]A. von der Fehr, “Validation of Social Networks from a Snowball Sampling Study of Municipal Science Education Stakeholders,” Int. J. Res. Method Educ., 2015. [46]B. Yamkovenko and J. P. Hatala, “Methods for Analysis of Social Networks Data in HRD Research,” Adv. Dev. Hum. Resour., vol. 17, no. 1, pp. 40–56, Feb. 2015. [47]S. Badi, L. Wang, and S. Pryke, “Relationship marketing in Guanxi networks: A social network analysis study of Chinese construction small and medium-sized enterprises,” Ind. Mark. Manag., May 2016. [48]Ö. Bodin, A. Sandström, and B. Crona, “Collaborative Networks for Effective Ecosystem-Based Management: A Set of Working Hypotheses,” Policy Stud. J., p. n/a–n/a, Jan. 2016. [49]B. C. Chaffin, A. S. Garmestani, H. Gosnell, and R. K. Craig, “Institutional networks and adaptive water governance in the Klamath River Basin, USA,” Environ. Sci. Policy, vol. 57, pp. 112–121, Mar. 2016. [50]K. Ekker, “Emergency management training: Handling rich qualitative and quantitative data,” J. Intell. Fuzzy Syst., vol. 31, no. 2, 2016. [51]J. Georis-Creuseveau, C. Claramunt, and F. Gourmelon, “A modelling framework for the study of Spatial Data Infrastructures applied to coastal management and planning,” Int. J. Geogr. Inf. Sci., pp. 1–17, May 2016. [52]J. Hauck, J. Schmidt, and A. Werner, “Using social network analysis to identify key stakeholders in agricultural biodiversity governance and related land-use decisions at regional and local level,” Ecol. Soc., vol. 21, no. 2, 2016. [53]B. J. Kreakie, K. C. Hychka, J. A. Belaire, E. Minor, and H. A. Walker, “Internet-Based Approaches to Building Stakeholder Networks for Conservation and Natural Resource Management,” Environ. Manage., vol. 57, no. 2, pp. 345–354, Feb. 2016. [54]J. N. Lamb, K. M. Moore, J. Norton, E. C. Omondi, R. Laker-Ojok, D. N. Sikuku, D. S. Ashilenje, and J. Odera, “A social networks approach for strengthening participation in technology innovation: lessons learnt from the Mount Elgon region of Kenya and Uganda,” Int. J. Agric. Sustain., vol. 14, no. 1, pp. 65–81, Jan. 2016. [55]T. Luthe and R. Wyss, “Resilience to climate change in a cross-scale tourism governance context: a combined quantitative-qualitative network analysis,” Ecol. Soc., vol. 21, no. 1, p. art27, 2016. [56]T. A. Muñoz-Erickson and B. B. Cutts, “Structural dimensions of knowledge-action networks for sustainability,” Current Opinion in Environmental Sustainability, vol. 18. pp. 56–64, Feb-2016. [57]A. Paletto, J. Balest, I. Demeo, G. Giacovelli, and G. Grilli, “Power of Forest Stakeholders in the Participatory Decision Making Process: A Case Study in Northern Italy,” Acta Silv. Lignaria Hungarica, vol. 12, no. 1, pp. 9–22, Jan. 2016. [58]J. I. Romero Gelvez and M. Garcia-Melon, “Influence Analysis in Consensus Search — A Multi Criteria Group Decision Making Aproach in Environmental Management,” Int. J. Inf. Technol. Decis. Mak., vol. 15, no. 04, pp. 1–23, Jul. 2016. [59]W. Williamson and K. Ruming, “Using Social Network Analysis to Visualize the Social-Media Networks of Community Groups: Two Case Studies from Sydney,” J. Urban Technol., pp. 1–21, Jul. 2016. [60]X. Zheng, Y. Le, A. P. C. Chan, Y. Hu, and Y. Li, “Review of the application of social network analysis (SNA) in construction project management research,” Int. J. Proj. Manag., vol. 34, no. 7, pp. 1214–1225, Oct. 2016.