# 2022c语言代写澳大利亚格里菲斯大学essay：客户满意度调查结果之因子分析及其四大因素的论述

August 19, 2022essay代写

## 2022c语言代写澳大利亚格里菲斯大学essay：客户满意度调查结果之因子分析及其四大因素的论述

Customer Satisfaction Survey Results客户满意度调查结果

Factor analysis因子分析

19 customer service items were ranked by retailers. Evaluating items separately can only suggest the level of satisfaction on each item. However, it does not recommended to improve every item which has low satisfaction as the degree of item impact on overall satisfaction varies. More importantly, some items might be correlated strongly with others. Items with high correlation can be grouped as specific characteristic of customer service. Analyzing grouped customer service items is more effective. From this point of view, Principal Component Analysis (PCA) with a Varimax rotation was employed to reduce numerous items to a more manageable set of factors (Aaker and Day, 1986). 5 factors were extracted from 19 items in the initial factor analysis. The results are given in Table 5. It’s noted that not all items load strongly to any factors, especially item Product availability and Order lead-time (below 0.5). To ensure the reliability of data, these two items were deleted.零售商分别位列19客户服务项目。分别评估项目只能建议对每个项目的满意程度。然而，它不推荐用于提高每个产品具有低满意度整体满意度的变化对产品的影响的程度。更重要的是，一些项目可能会被强烈相关与他人。关联度高的项目，可以归纳为特定客户服务的特点。分析分组的客户服务项目，是更有效的。从这个角度来看，主成分分析（PCA）与方差最大旋转一个更易于管理的一系列因素（Aaker和日，1986），以减少项目众多。初始因子分析的19个项目中的5个因素分别提取。其结果列于表5。据指出，不是所有的项目加载任何因素强烈，尤其是项目的产品供应情况和订单交货时间（低于0.5）。为了确保数据的可靠性，这两个项目已被删除。Table 5: Factor analysis results on retailers’ satisfaction (19 items)

Principal Component Analysis was run again for the retained 17 items. Four factors were generated and accounted for 70.05 percent of the total explained variance (see Table 6). The four factors were:主成分分析法再次运行保留17项。四个因素，占70.05％的总方差解释（见表6）。这四个因素分别为：

(1)Order & Delivery Performance网上订货及送货性能(2)Sales & Promotion销售及推广(3)Post-sales Support售后支持(4) Product产品http://www.ukassignment.org/dxazessay/

To test the reliability of the extracted factors, reliability analysis was run for each factor. Cronbach’s alpha for the 4 factors were 0.91, 0.79, 0.75, and 0.53, respectively (see Table 6). Considering Cronbach’s alpha criteria (above 0.7), the extracted factors except for factor Product had relatively high reliability and were suitable for further analysis. The reliability of ‘Product’, 0.59, was deemed low. It’s accepted due to considerations of exploratory nature of the present study.Table 6：Factor analysis results on retailers’ satisfaction (17 items)

The first factor, Order & Delivery Efficiency including 7 scale items, accounted for the largest proportion (26.67 percent) of the total explained variance. It was primarily related to the concept of providing easy-to-use order system and efficient delivery service.第一个因素，订货及送货效率，其中包括7个大型项目，占的比例最大（26.67％）的总方差解释。它主要是提供易于使用的订单系统和高效的送货服务的概念。

The second factor, Sales & Promotion including 5 scale items, explained 21.2 percent of the variance. It’s associated with good performance of sales people and attractive sales package.第二个因素，包括5个规模项目的销售及推广，解释21.2％的变异。相关的销售人员和有吸引力的销售包装具有良好的性能。

The third factor, Post-sales Support, accounted for 13.28 percent of the variance. It’s comprised by 3 scale items which are clarity and accuracy of dispatch documents, clarity and accuracy of invoice, and post-sales service.第三个因素，售后支持，占13.28％的差异。它由3个大型项目的清晰度和准确度的调度文件，清晰度和准确性发票，售后服务。

The fourth factor, Product, explained 8.9 percent of the variance and consisted of two items, including product price and variety.第四个因素，产品解释说，8.9％的差异，包括两个项目，其中包括产品的价格和品种。

Before carrying on Regression Analysis, degree of the correlations among the extracted factors should be examined. If the correlations are too high, it suggested that two factors may be measure the same customer service characteristic and need to be modified. High cross correlations may violate the results of regression analysis as variables used in regression analysis are required to be relatively independent (Johnson and Gustafsson 2000). The 4 extracted factors were saved as new variables in the data set, a bi-variate correlation analysis was run for them. The correlation between any two factors should not exceed 0.7, and the lower the values the better (Johnson and Gustafsson 2000, p.115). Based on the results of correlation analysis (see Table 7), there were only one correlations above 0.6. It suggests that cross correlation of the 4 extracted factors is not too severer. Data quality meets the requirements of regression analysis.程度提取的因素之间的相关性进行回归分析之前，应该进行检查。如果相关性过高的话，建议两个因素可能被测量相同的客户服务特性，并需要进行修改。高交可能违反相关性回归分析中使用的变量的回归分析结果都必须相对独立（2000年约翰逊和古斯塔夫森）。提取的因素被保存在数据集中的新变量，双变量的相关性分析运行。任何两个因素之间的相关性应不超过0.7，低的值的更好的（约翰逊和Gustafsson的2000年，第115页）。基于相关性分析的结果（见表7），只有一个0.6以上的相关性。这表明，交叉相关的4提取的因素是不是太严厉。数据质量符合要求的回归分析。Table 7: Cross correlation analysis results on measurements