Elementos del Debido Proceso en el Sistema Oral

The application of SNA in this thesis links to the coming chapters, particularly involve the questionnaire data analysis in section 5.6. The details of application of SNA in this thesis show as follows:

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As this research is developed from the framework of logistics triad which only consists of the core players within the logistics network, and we are also mostly interested in the main players and the relationships among them in the maritime logistics network, we do not look into the whole players in the full network. Instead, this research utilizes only what is called the ego network, which consists of: a focal actor (known as ego); the set of actors with any kind of tie to ego (known as alters); all ties among the alters and between the alters and the ego (Borgatti and Li 2009, see Figure 3.3). Works of Lee (2005) and Kim et al. (2011) discussed in the previous section are two examples for this.

(source: Borgatti and Li, 2009)

Figure 3.3 An example of ego network

Borgatti and Li (2009) point out that in practice many studies which apply the network theoretical perspective do not actually take into account the full network, but only focus on the ego network. The main reason to restrict attention to the ego network is the belief that more distant connections are not relevant to the specific mechanisms at hand. In addition, it is much more convenient to collect ego network data than full network data, and there is little reason to collect the whole network, if the ego network could provide a reasonable proxy for position in the larger structure. In addition, methodologically, the ego network approach is fairly easy, although in a complex organization it may be necessary to ask the same information from a number of different organizational members (each with a limited view of the organization’s activities) in order to construct a complete ego network.

(2) Data collection

In terms of the data collection, this research adopted a strategy of aggregation. This strategy has been used in several fields, such as ecology food web research and sociology. In ecological

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food web research, there are often too many species to work with, and so sets of similar species are grouped together into what are called compartments, and these are taken as the units of analysis instead of individual species (Borgatti and Li 2009). The social capital theorists Lin, Fu and Hsung (2001) have advocated the use of ‘‘position generators’’ which is a survey technique in which the respondent is asked not for their ties to specific others, but to categories of others, such as ‘‘priests’’ or ‘‘managers’’ or ‘‘marketing people’’. In the supply chain case, this corresponds to aggregating by industry, technology, some other convenient variable or varying levels of sector (Borgatti and Li 2009).

Accordingly, strategy of aggregation can be done with firms as well, so that, in this research, instead of asking for the relationship strength with individual trading partners, the firm was asked for inputs from different categories of trading partners (cargo owners, ocean freight forwarders, shipping carriers, port operators) in line with the research objectives.

(3) Data analysis The nature of data

This research adopted the SNA related to the directional and weighted network. Since the thesis is developed based on the network perspective, the data of relationship strengths will be collected from each main player’s perception. This will generate a directional network of tangible or in tangible flow, which focus on either the flow initiated (out-degree) or flow received (in-degree) (Kim et al. 2011). On the other word, the maritime logistics network would be the ego network defined by the set of all main players with a direct relationship (in or out) to the other main players (Borgatti and Li 2009).

Further, referring to Chapter 2, this thesis intends to measure the inter-organizational relationship strength by six dimensions (communication, cooperation, relationship duration, commitment, trust and dependency) which are interval measures. According to Hanneman and Riddle (2005), continuous measures of strengths of relationships allow the application of a wider range of mathematical and statistical tools to the exploration and analysis of the data. Many of the algorithms that have been developed by social network analysts, originally for binary data, have been extended to take advantage of the information available in full interval measures. This research thus will use the SNA metrics adopted in the weighted network.

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Node-level analysis

In terms of the selection of SNA metrics, the simplest method of measuring network centrality for a node is by the way of degree centrality, which takes account the number of direct connections with the nodes (Iyengar et al. 2012). This degree is a measure of the importance of a node in a network, nodes with strong connections should be accorded more importance than nodes with only weak connections. Every link of weight n can be replaced with n parallel links of weight 1 each, connecting the same nodes. Therefore, techniques that can normally be applied to non-weighted graphs can be applied to the weighted graphs as well (Newman, 2001). Degree can be extended to the sum of weights when analysing weighted networks, and this measure has been formalized as follows (Barrat et al. 2004; Newman 2004; Opsahl et al. 2008):

where w is the weighted adjacency matrix, in which wij is greater than 0 if the node i is

connected to node j, and the value represents the weight of the tie. This is equal to the definition of degree if the network is binary, i.e. each tie has a weight of 1. The degree centrality will be measured by the out-degree centrality, in-degree centrality and the conjunction of these two respectively. On the other hand, the degree in the weighted network need to be normalized in order to sum to one for the purpose of comparison (Liu 2008). As Kim et al. (2011) used degree centrality to determine the integrator and allocator in the supply chain cited in Section 2.3.2, the questionnaire survey in this research has used such weighted degree and normalized weighted degree to identify the position of each main player in the maritime logistics network. In addition to the figures derived from the degree of a node in the SNA, the strength of the connection can also be depicted by the thickness of the line, the number of lines, or other graphical means (Lee 2005).

Second, the individual degree of connectedness was explored in order to identify the most connected node (main player), or the node (main player) gives output and receives input more

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than other nodes. This degree of connectedness was derived from the data consisting a main player’s output and input to other main players.

Network-level analysis

With regards to the network-level metric, the network density which is a measure of the overall connectedness of a network were adopted. Network density refer to the number of total ties in a network relative to the number of potential ties (Scott 2000). The research has applied this to compare the SCI degree of different networks according to three types of service complexity, as Lee (2005) did cited in Section 2.3.2. As applied in the weighted node- level SNA, the network density for weighted network can refer to the number of total weighted ties in a network relative to the number of potential weighted ties.

3.9 Summary

This chapter has outlined the main methods used in this thesis, providing an overview of the alternative as well as more detail on the specific approaches chosen. This thesis is underpinned by a realism epistemology, based on which mixed methods were carried out in order to provider a wider and reliable context to understand the relationship structure in the maritime logistics network. Being applying realism-based mixed methods is corresponding to the latest trends in SCM and logistics research (Voss et al. 2002; Mangan et al. 2005; Woo et al. 2011b).

Given the research questions to be considered, three main methods were selected: semi- structured interview; questionnaire survey and social network analysis (SNA). The first is used to establish the framework of relationship structure in the maritime logistics network which is still absent in the literature. The second is used to measure the quantitative level of the relationship strength and value generated within the network. The third one is a contemporary technique for further understanding the insights of the maritime logistics network. By applying such methods, a more comprehensive and dynamic picture of the maritime logistics network from different perspectives can be revealed.

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