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多极化港口系统的竞争力外文文献和中文翻译(7)

时间:2024-08-19 21:25来源:96667
4. Data Analysis and Results 4.1. Results of Factor Analysis EFA using SPSS 21 determines how clearly and to what extent an observed variable links to the underlying factors, and eliminates potentiall

4. Data Analysis and Results

4.1. Results of Factor Analysis

EFA using SPSS 21 determines how clearly and to what extent an observed variable links to the underlying factors, and eliminates potentially superfluous items. To extract the minimum number of factors which account for co-variation amongst observed variables, principle components analysis with Varimax rotation was adopted because it assumes independence between factors and maximises the sum of the variances of squared loadings. The criteria used for selecting measurement items were eigen-value (>1.0) and factor loading (>0.50) (Hair et al., 2010).

Twenty one items for competitiveness were assessed and EFA grouped the scale of items of competitiveness into four dimensions (Table 3). Each measurement item recorded factor loadings >0.50, but two item (COM 14 (cargo handling charges) and COM 21 (service differentiation)) were eliminated due to low communality <0.50 (Hair et al., 2010), to enhance the reliability and validity of items. Factor loadings for the 19 purified items between 0.682 and 0.825, and communality values >0.50, exceeded acceptable standards (Hair et al., 2010) implying that factor analysis is reliable with variables well represented by the extracted factors. A Kaiser-Meyer-Olkin’s measure of sampling adequacy (85.7%) indicates that observed variables link closely to their underlying facts. The four competitiveness factors extracted explain 64.5% of the inherent variation in their items. Finally Cronbach’s Į >0.70 for all extracted factors indicates constructs which are internally consistent and valid (Hair et al., 2010)).

Table 3

Results of exploratory factor analysis

Items* Factor Analysis Source: Author

Note: Kaiser-Meyer-Olkin Measure of Sampling Adequacy: 0.857

COM: competitiveness; PA: port availability; OE: operational efficiency;

PC: port costs; SQ: service quality.

Based on EFA 19 measurement items incorporating hard, soft and supportive factors were grouped into four sub-dimensions. Taking into account prior work (TongZon, 2009; Yeo et al., 2008), the structure of port competiveness to be a regional gateway in NEA was developed (see Figure 2), using labels of ‘availability’, ‘operational efficiency’, ‘port costs’, and ‘service quality’.

Availability: a regional gateway port is considered a significant component of the local economy and economic cooperation with its surrounding areas (Imai et al., 2013). Port availability as an international logistics hub incorporates physical and functional availability such as port facilities, hinterland development and economic size (Yeo et al., 2011, 2008; Low et al., 2009; Tongzon, 2009). Therefore, a regional gateway port must have competitive capacities not only to accommodate megacontainer ships, but also to perform expanded port functions as a comprehensive logistics centre which boosts global or major regional trade and the local economy, which strengthens hub status (Ducruet and Lugo, 2013; Gelareh et al., 2010; Wang and Cheng, 2010). The components of port availability include local cargo volume (PA1), port infrastructure and facilities utilisation (PA2), market niche (PA3), preference of shipping liners (PA4), and port physical capacity to accommodate additional volumes (PA5).

Operational efficiency: Operational efficiency in port operations is required to be a logistics hub (Low et al., 2009). A higher level of efficiency attracts more port users as the importance of faster turnaround time within the port is critical for hub port operations in NEA (Imai et al., 2013; Yeo et al, 2011, 2008). Besides, the efficiency of inland transport and hinterland connection has become critical in a port’s potential future competitiveness (Notteboom and Rodrigue, 2008). The world’s mega container ports (i.e. Shanghai, Hong Kong, and Busan) already view this as a key factor to support their long-term vision (Yeo and Song, 2006). The elements for operational efficiency include terminal productivity (OE1), hinterland development (OE2), simplification of procedures (OE3), cargo handling speed (OE4), and supply chain cooperation (OE5). 多极化港口系统的竞争力外文文献和中文翻译(7):http://www.youerw.com/fanyi/lunwen_204558.html

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