BCPM0049: Social Networks in Project and Enterprise Organisations Project Brief
1.背景：制造研发部门和咨询部门
已聘请您了解是什么驱动着他们的制造研发网络和内部咨询部门之间的协作。该公司正试图了解他们可以采取哪些措施来改善这两个部门的运作方式。
2.数据
邀请您的咨询公司分析该公司收集的数据。该数据集包含四个组织内部网络。两名来自咨询子部门（46名员工），两名来自制造子部门的研究团队（77名员工）。
2.1咨询单位2.1.1网络
在第一个网络中，在信息或建议请求的频率上，联系的范围从0到5分不等（“请指出您在过去三个月中向该人求助有关工作相关主题的信息或建议的频率是多少个月”）。 0：我不认识这个人； 1：从不； 2：很少； 3：有时； 4：经常5：经常。
在第二个网络中，联系根据收到的信息或建议的价值而有所不同（“对于以下列表中的每个人，请表明您对以下陈述的支持或反对程度：总体而言，此人具有专业知识在我从事的工作中很重要的领域。”）该网络中的权重也基于从0到5的标度。 1：非常不同意； 2：不同意； 3：中立； 4：同意； 5：非常同意。
2.1.2属性
除关系数据外，数据集还包含有关人员的信息（节点属性）。咨询公司已知以下属性：组织级别（1个研究助理； 2：初级顾问； 3：高级顾问； 4：管理顾问； 5：合伙人），性别（1：男性； 2：女性），区域（1：欧洲; 2：美国）和位置（1：波士顿; 2：伦敦; 3：巴黎; 4：罗马; 5：马德里; 6：奥斯陆; 7：哥本哈根）。
2.2研究单位
2.2.1网络
在第一个网络中，研究人员之间的联系在建议方面有所区别（“请指出以下人员在多大程度上为您提供完成工作所使用的信息”）。权重基于以下标准：0：我不认识这个人/我从未见过这个人； 1：很少见； 2：很少； 3：不太常见； 4：有点频繁5：经常和6：非常频繁。
第二个网络基于员工对彼此的知识和技能的认识（“我了解此人的知识和技能。这不一定意味着我具有这些技能或在这些领域中知识渊博，但是我了解这是什么技能人拥有的知识和领域”）。该网络中的体重秤为：0：我不认识这个人/我从未见过这个人； 1：非常不同意； 2：不同意； 3：有点不同意； 4：有点同意5：同意； 6：非常同意。
2.2.2属性
对于制造公司的研究人员来说，以下属性是已知的：位置（1：巴黎； 2：法兰克福； 3：华沙； 4：日内瓦）；任期（1：1-12个月； 2：13-36个月； 3） ：37-60个月； 4：61+个月）和组织级别（1：全球部门经理； 2：本地部门经理； 3：项目负责人； 4：研究员）。
3.您的任务
已为您提供了网络数据集。您的任务是使用软件从网络分析的角度解释数据，并在书面的3000字顾问报告中陈述您的解释。您将需要：
选择制造研发网络或咨询单位进行分配
对沟通和满意度网络进行彻底的分析。您应该使用软件（R，UCINET和Netdraw，Gephi或其他软件）来处理数据，并可视化网络中的通信模式。
SNA数据的统计分析将确定：
-网络结构及其特征
-团队成员之间的交互方式及其属性，例如，团队成员之间的平均链接数以及子组的数量和质量。
-对中心性的统计分析将确定网络中的杰出参与者，以及他们在网络中的角色。
1. Background: A manufacturing R&D unit and a consulting unit
You have been hired to understand what drives collaboration in the networks of their manufacturing R&D and their in-house consulting unit. The firm is trying to understand what they could do to improve how the two units function.
2. The data
Your consultancy firm was invited to analyse data collected by the firm. This dataset contains four intra-organizational networks. Two are from the consulting sub-unit (46 employees) and two are from the research team in the manufacturing sub-unit (77 employees).
2.1 The consulting unit 2.1.1 Networks
In the first network, the ties are differentiated on a scale from 0 to 5 in terms of frequency of information or advice requests (“Please indicate how often you have turned to this person for information or advice on work-related topics in the past three months”). 0: I Do Not Know This Person; 1: Never; 2: Seldom; 3: Sometimes; 4: Often; and 5: Very Often.
In the second network, ties are differentiated in terms of the value placed on the information or advice received (“For each person in the list below, please show how strongly you agree or disagree with the following statement: In general, this person has expertise in areas that are important in the kind of work I do.”). The weights in this network is also based on a scale from 0 to 5. 0: I Do Not Know This Person; 1: Strongly Disagree; 2: Disagree; 3: Neutral; 4: Agree; and 5: Strongly Agree.
2.1.2 Attributes
In addition to the relational data, the dataset also contains information about the people (nodal attributes). The following attributes are known for the consultancy firm: the organisational level (1 Research Assistant; 2: Junior Consultant; 3: Senior Consultant; 4: Managing Consultant; 5: Partner), gender (1: male; 2: female), region (1: Europe; 2: USA), and location (1: Boston; 2: London; 3: Paris; 4: Rome; 5: Madrid; 6: Oslo; 7: Copenhagen).
2.2 The research unit
2.2.1 Networks
In the first network, the ties among the researchers are differentiated in terms of advice (“Please indicate the extent to which the people listed below provide you with information you use to accomplish your work”). The weights are based on the following scale: 0: I Do Not Know This Person/I Have Never Met this Person; 1: Very Infrequently; 2: Infrequently; 3: Somewhat Infrequently; 4: Somewhat Frequently; 5: Frequently; and 6: Very Frequently.
The second network is based on the employees’ awareness of each others’ knowledge and skills (“I understand this person’s knowledge and skills. This does not necessarily mean that I have these skills or am knowledgeable in these domains but that I understand what skills this person has and domains they are knowledgeable in”). The weight scale in this network is: 0: I Do Not Know This Person/I Have Never Met this Person; 1: Strongly Disagree; 2: Disagree; 3: Somewhat Disagree; 4: Somewhat Agree; 5: Agree; and 6: Strongly Agree.
2.2.2 Attributes
For the researchers in the manufacturing company, the following attributes are known: location (1: Paris; 2: Frankfurt; 3: Warsaw; 4: Geneva), tenure (1: 1-12 months; 2: 13-36 months; 3: 37-60 months; 4: 61+ months) and the organisational level (1: Global Dept Manager; 2: Local Dept Manager; 3: Project Leader; 4: Researcher).
3. Your task
You have been provided with network datasets. Your task is to interpret the data from a network analysis perspective, using software, and present your interpretation in a written 3000-word consultant’s report. You will need to:
- Select EITHER the manufacturing R&D network or the consulting unit for your assignment
- Carry out a thorough analysis of the communication and satisfaction networks. You should use software (R, UCINET and Netdraw, Gephi, or other) to process the data and produce a visualisation of the patterns of communication in the networks.
- Statistical analysis of SNA data will identify: – Network structure and its characteristics
– The patterns of interaction among team members, as well as its properties such as average number of links between team members, and the number and qualities of subgroups.
– Statistical analysis of centrality will identify prominent actors within the networks, and their roles in the network.