Analysis the Factors Affecting Satisfaction of the Quality of Seafood Logistics in Vietnam45311

EDESUS Proceeding 2019 (86 of 1531) Khan K., Manzoor, H.M (2012) The testing of Hall’s Permanent Income Hypothesis: a case study of Pakistan Asian Economic and Financial Review 2(4), 518-522 Perron, P (1989) The great crash, the oil price shock, and the unit root hypothesis Econometrica: Journal of the Econometric Society, 361-1401 Analysis the Factors Affecting Satisfaction of the Quality of Seafood Logistics in Vietnam Hong Van Dao1,2, Van Quang Do3,*, Thi Minh Ngoc Vu2, The Kien Nguyen Hanoi University of Natural Resources and Environment, Hanoi, Vietnam Foreign Trade University, Hanoi, Vietnam Thuyloi University, Hanoi, Vietnam VNU University of Economics and Business, Vietnam National University, Hanoi, Vietnam * Correspondence: quang61qs@gmail.com Abstract: The paper analyzes the factors affecting customer satisfaction with the quality when using Vietnam's seafood logistics service through SEM model From the result of this model, the authors found five groups of factors affecting satisfaction with seafood quality, including human resources in logistics, technology, delivery and product packaging, shipping capacity, and reputation of enterprises All of these factors have a positive impact on this service The meaning of this study is to give the useful references in planning policies for seafood logistics in Vietnam Keywords: Structural Equation Modelling (SEM); seafood; logistics; quality of service Introduction Vietnam is a rich natural resources country, especially water and marine resources This explains why the seafood industry is strongly developed and plays a vital role in the export sector of Vietnam In fact, the seafood industry accounts for a large share of GDP In order to push the seafood industry, it is required to develop a modern seafood logistics system Although many researchers have studied about logistics and seafood industry, the works that combine these two areas in the same topic and delve into the research on how to develop logistics in seafood industry is not popular yet Nguyen (2012) studied the development of a maritime transportation planning support system for car carriers based on genetic algorithm Shipping plays a vital role in an integrated economy, evidence that shipping volumes have increased over the years and are expected to continue to increase in the near future For this reason, logistics providers are forced to enhance their competitiveness in terms of both price and quality by providing efficient services Based on this reason, many researchers have studied how to handle global logistics issues, and in the case of shipping, many studies have compared it to other transport industries because of its importance This research has developed an algorithm EDESUS Proceeding 2019 (87 of 1531) and has succeeded in developing an algorithm for transport planning with algorithms, thus being able to prepare various options, evaluate them and thus support decision makers Megan (2014) explored shipping issues at that time, particularly on how and how much seafood was shipped to the western federal markets of North Carolina The study also identified challenges as well as opportunities in improving the east-to-west supply chain The three biggest challenges for shipping seafood from the east to the west were related to the availability of logistics, products, marketing and education The main reason for the big problem of logistics was lack of centralized distribution facility for seafood in the western part of North Carolina Logistics could be improved by establishing a domestic distribution center with a capacity of cold storage to facilitate road transport The study explored shipping issues in the seafood industry, namely how and how much seafood was shipped to in-state markets in the western part of North Carolina The thesis also identified challenges as well as opportunities in improving the east-to-west supply chain The author identified five major coastal-based seafood wholesale distributors and two inland-based distributors with routes established to serve goods, groceries, retail markets, and food distributors in all major metropolitan areas, including west areas of North Carolina and neighboring states The study identified and strengthened alternative or complementary supply chains and domestic markets along the corridor from North Carolina to Baltimore, Philadelphia, New York and New Bedford North Carolina seafood supplied more to local locations would contribute to brand recognition and avoid price losses Methodology 2.1 Model Building Base On the basis of preliminary research results, research overview, and base on relevant arguments on customer satisfaction with the quality of logistics services, from the bridge angle (customers), due to seafood logistics characteristics , through the customer's need and feel research, to apply angles of view of the SERVQUAL model, the authors have built a set of criteria to evaluate the quality of seafood logistics services, including components: (1) Company's reputation , (2) Manpower Logistics, (3) Factor shipping capability, (4) Delivery and packaging factors, (5) Technological factors, (6) Satisfaction factor, and (7) Loyalty factor To verify the relationship between the studied impacts, the linear structure model (Structural Equation Modelling-SEM) is the most suitable model proposed The SEM model specifies the relationship between the underlying variables (which are not direct measurements) and the variables that can be observed, while clearly pointing out the relationship between the underlying variables together 2.2 Data collection According to Hair and Associates (1998), for the implementation of the EFA factor analysis, the size of the sample applied in the study must be at least times the total number of observed variables The study has 33 observation variables, so the minimum sample EDESUS Proceeding 2019 (88 of 1531) number is 33 * = 165; for multi-variable regression analysis: The minimum sample size to be achieved by the formula is 50 + * m (m: Independent variable number) (Tabachnick and Fidell, 1996) The study has independent variables, so the minimum sample size is: 50 + * = 98 observations As such, to determine the factors that affect the level of satisfaction of seafood logistics service, research has conducted in-depth interviews and survey with structural inquiry for aquaculture enterprises, exploiting and processing seafood on the whole country The number of votes obtained is 500 votes, of which 492 are enough information to be used for analysis (98.4% ratio) The interview card is designed with closed questions to ask businesses to evaluate, compare conditions, current status of seafood logistics services with their expectations, about the aspects related to the credibility of the company's logistics responsibilities, logistics manpower, transportation capacity, forwarding and packaging, technology, satisfaction, and loyalty Reviews using Likert scale with levels: = strongly disagree/very dissatisfied, = disagree/dissatisfied, = neutral, = agree/satisfied and = strongly agree/very dissatisfied 2.3 Data processing In this study the authors applied the method of analyzing the model equation structure (Structural Equation Model – SEM), which uses the SPSS 22.0 and AMOS software version 20.0, which consists of steps: The analysis of the reliability coefficient Cronbach's Alpha, Exploratory Factor analysis (EFA); The Affirmation analysis (Confirmatory Factor Analysis – CFA), analyzing the structural equation model (Structural Equation Model – SEM), specifically as follows: Step 1: Evaluate the reliability of the scale Cronbach's ALPHA (CA) coefficient is used to assess the reliability of the ladder for each observation variable belonging to the factor groups Peterson (1994) said that any factor with a CA less than 0.6 should be excluded from the research model According to Bernstein and Nunnally (1994), observation variables with a total correlation coefficient of less than 0.3 are considered garbage variations, which lead to a conclusion of rejecting the model Step 2: Analyze the discovery factor (EFA) EFA allows to describe the correlation between impact variables, known as "factors" EFA is used in cases where the relationship between the observed variables and the underlying variable is unclear or uncertain EFA analysis is conducted in an explored manner to determine the scope, level of the relation between observation variables and the base factors, as the basis for a set of measurements to shorten or reduce the number of observations which are uploaded to the base factor Meyers and Associates (2016) said that, in EFA, the method of extraction of Principal Component Analysis in conjunction with Varimax rotation is the most commonly used method A condition for EFA analysis is the following requirements: factor loading > 0.3; 0.5 ≤ KMO ≤ 1; Bartlett test having statistical significance (Sig 50% Step 3: Analyzing the assertions factor (CFA) EDESUS Proceeding 2019 (89 of 1531) CFA is used appropriately when researchers have information about underlying variable structures CFA may be the next step of EFA to test whether there is a theoretical model that is the basis of a set of observations Indicators for measuring the suitability of the model with the data of a limb (CMIN); The genus (CMIN/DF); Comparative relevance index (CFI); Tucker & Lewis Index (TLI); An estimate of the original average median (RMSEA) According to Hair and Associates (1998), if