Who are the parties to blame for the $2.48 Billion deal in the K-Dow Petrochemicals joint venture?

Thuraiya Alhashmi (I will be taking the 2nd module) 

Background

The case I am going to use social networks to analyze is the fallout of the K-Dow Petrochemicals joint venture deal. K-Dow was an $18 billion dollar joint venture between Petrochemical Industries Company (PIC), a subsidiary of Kuwait Petroleum Company (KPC), and Dow Chemical Company (Dow) in 2008.  The Kuwaiti parliament threatening to impeach the Prime Minister, called the deal corrupt and over valued. The Prime Minster of Kuwait, stating it was a bad deal, terminated the joint venture agreement in the final hours of the grace period. A year later, Dow filed a lawsuit against KPC, resulting in Kuwait paying $2.48 billion to Dow Chemical.

Social Network Question

Primarily, the question I would like to answer using Social Network Analysis is: “Who is to blame for the cost of $2.48 Billion in the K-Dow petrochemicals deal?” Or, “Who are the parties to blame for the cost of $2.48 Billion in the K-Dow Petrochemicals deal?” In the course of my analysis, I might come across an interesting fact that might alter my social network question.

Hypothesis

I predict to find that the Prime Minster of Kuwait was not to be blamed for the K-Dow Petrochemicals joint venture demise. Additionally, I predict to find other parties within Dow Chemical and PIC to be at fault or should have taken different routes to resolve the miscommunication that caused the activation of the arbitration clause in the agreement. That could lead to possible recommendations of significant organizational changes in the oil sector in Kuwait.

Data

I will have to create my own dataset for this project, as the dataset is not available. After creating the network dataset and attribute dataset on Excel, I will transfer them to UCINET. Since K-Dow Petrochemicals case took place in 2007/ 2008, I will need to find all the individuals in both networks from that year. That will require some research from publically available resources on the internet about the case and the key individuals mentioned in the case. Also, looking at the official websites of Kuwait Parliament, KPC and Dow Chemical. The estimated number of individuals in all networks would be around 60. Attributes data such as prior job, age, religious, and party affiliation and education could be found on Kuwait Politics Database website for the Parliament and google search on the other individuals in KPC and Dow Chemical. 

   

Methodology

The networks I am thinking of creating for this case are two main networks; one is the Kuwait side of the deal (Kuwait network) and another network for the U.S. side of the deal (Dow Chemical). The Kuwait network will include members of the Kuwaiti Parliament, Petrochemical Industries Company (PIC), Kuwait Petroleum Company board of directors (KPC) and the Prime Minister Office. On the U.S. network, I will include the board of directors and management of Dow Chemical.

Social network analysis will help me get a clear picture of how individuals from both networks are connected with each other through visualization on NetDraw. In addition, I am planning to use centrality measures such as in-degree and out-degree connections, betweenness and Eigenvector to assess influence of individuals and the possibility of corruption in the decision making process. For example, Eigenvector will help me identify escape goats and behind-the-scenes key players; betweenness will help me identify individuals with the highest number of ties to the decision makers, which will be important to analyze. I will also try to assess the effect of removing these individuals from the network. When creating both networks, I will use 2-mode network to link the individuals with their different affiliations. I think that the most important network measure will be subgroup/ clique analysis, as it will help me narrow down my pool of suspects and determine interesting ties or cohesion in terms of attributes, if any.

Possible expansion to this project is to look at what happened post the lawsuit and if there are any change to the parties involved or the individuals in each network (who got fired? Appointed? Etc.) For that, I will create a post lawsuit network and compare it to the pre lawsuit network. I will look at gaps between both pre and post lawsuit networks and perhaps any organizational restructuring of the board of directors in both KPC and Dow Chemicals. The results would be interesting to look at.

Conclusion

K-Dow Petrochemicals joint venture arbitration case has been resolved; however, there is little to none legal analysis published online about it, as it was agreed to be confidential between both parties. It would be interesting to look at the application of social network analysis in analyzing the key influencers of the Prime Minister’s decision to demise the deal. While the Prime Minister was labeled as the cause of the loss, the Kuwaiti news have called for further investigations within the oil company. Prompting this analysis to question who are the parties at fault, whether the decisions that were made by the leadership of Kuwait and Dow Chemical are the right/ fair, and how could social network analysis help us come up with a conclusion that would have helped both parties avoid such outcome.