**Literature**

According to the study conducted by Aydın and Öğüt (2008), parallel to the growth of trade volume in the world, the competition has become very tough. This caused various changes in marketing strategies and different policies have been followed to increase profitability. All this have led to a number of changes in the types of work and business processes. These differences have indicated that services should be very cost effective, fast, integrated, and economical in scale economics. All of these logistical activities and service areas that have emerged to achieve this have played an important role in trade.

Karacan & Kaya (2011) define logistics activities as including designing, controlling and regulating the flow of information and materials between the business processes. They include relations with employees, companies and the customers. In a broad sense, the task of logistics is to guarantee the delivery of a product or service to a specific place, at a certain time and in the desired quality. In a narrow sense, the duty of logistics is to carry, store and sort.

Cakaloz & Tuna (2013) have conducted their study on ogistics activities efficient at the beginning of the efforts to increase the profitability and to provide the cost advantage of the businesses especially in the global competitive environment.

One of the earliest examples of this is the “quality of service model” by Grönroos (1988) and it measures the perceptions of customers about service quality.

Jun Yu (2006) states that the rate of Internet penetration in China has increased rapidly, and it resulted with a huge increase in the number of people using Internet.

Mentzer et.al. (1999) state that the SERVQUAL scale is not suitable for all service sectors and subsequently created 9 dimensions in order to determine the quality of the logistic service.

Han and Baek (2004) state that customer satisfaction and loyalty are crucial factors for businesses seeking to gain competitive advantage in the market. The high quality of service means that customer attitudes are determined positively.

Bolton and Drew (1991) point out that customer satisfaction will reveal the quality of service and it is accepted that service quality is an important concept in customer satisfaction.

Jaiswal (2008) defined customer satisfaction as “an assessment of the service offered to meet the customer’s needs and expectations”. The demands of customers change in accordance with their needs and the environment they are in.

In a study on Iran, Farinnia (2011) addressed consumers’ online purchasing trends. According to the results of the study, the most important factors affecting the customers’ shopping behaviour on Internet are determined as defects in after-sales service, product deliveries not realized on time, and exaggerated pictures of the product on website. These factors negatively affect the customer’s tendency towards Internet shopping.

Karadeniz and Isık (2014) have concluded that the relationship between logistic service quality and customer satisfaction was addressed in the studies on e-commerce and logistics, however logistic activities determining the quality of logistic service were not included in this association. In this study, the direct relation between electronic commerce and logistics activities will be covered and this gap in the literature will be filled.

**Impact Of Logistics Activities On The Intention To Re-Purchase From E-Commerce Perspective**

**4.1. Research Methodology **

This section of the study will provide information about the purpose, hypothesis, methodology and findings of the research conducted to measure the effect of the students attending e-commerce course at İstanbul Gelisim University on the intention of re-purchasing logistics activities in the e-commerce perspective. This study, which aims to determine the effect of logistics activities on the students who attend e-commerce course in terms of their intention to re-purchase, and it is a quantitative based general screening and relational screening model. In the same time, an evaluation will be made whether the results obtained in the research are statistically meaningful and whether the hypothesis has been verified will be tested.

**4.2. Method and Sampling of the Study **

The main purpose of the study is to examine the impact of logistics activities on the intent to re-purchase from e-commerce perspective. Questionnaire technique is used for data gathering in the study. The sample of the research are the students who attend e-commerce course at İstanbul Gelisim University. According to data of January-March 2018, 272 students have attended e-commerce course. Since it takes a long time and cost to reach all of the students, the study is conducted through a sample that will represent the main mass. Taking into consideration the sample size (α = 0.05) graph prepared to determine the sample size, the size was determined as 0.05 sample error p = 0,5 and q = 0,5 confidence interval. It was considered that a sample group consisting of a person (n = 159) of (α = 272) as a result of the examinations could represent at a level of 0.05 significance and 5 % tolerance (Altunışık et al., 2010: 135). Sampling method was preferred to determine the students to be included in the survey. The main principle in convenience sampling is to include each respondent into sampling (Altunışık et al., 2010).

From an e-commerce perspective, logistics activities questionnaires were created by scanning the relevant field literature. In a Five Point Likert scale, 5 is I strongly agree and 1 is I strongly disagree. After expert opinions are obtained, exploratory factor analysis was conducted on the questionnaires. Pilot implementation was carried out on 100 students. The Kaiser-Meyer-Olkin sample measurement capability was .817. This suggests that the data set is suitable for conducting factor analysis (Kalayci et al., 2010; Karagöz, 2016) Barlett test indicated that 1761,136 degrees of freedom of the Chi-Square value was significant at 276 p <0.01. This suggests that the data set very suitable for factor analysis (Alpar, 2011; Aksu and Eser, 2017). Value statistics λ1=6,500 explain 27,08 % of total variance, λ2= 4,291 explain17,88 % of total variance, λ3=3,421 explain 14,25 % of total variance, λ4=2,118 explain 8,82 % of total variance and λ5=1,423 explain 5,93 % of total variance. The cumulative percentage table indicates that 73.98 % of the total variance is explained by five factors. Providing the stated variance ratio of p≥2 / 3 or p≥0.66 conditions is considered as an important basic component (Büyüköztürk, 2005). The 0.74 value obtained in the analysis results for the data set indicate that five important factors to be derived would be sufficient. The factor loads range between .624 and .919. The factor load value is 0.45 or higher, which is a good measure to prefer (Büyüköztürk, 2005). In order to explain the structure, factor loads between 0.30 and 0.40 can be defined as acceptable loads with minimum levels; loads with 0.50 and above are defined as significant loads and loads above 0.70 are loads that can best describe the structure (Alpar, 2011). This means that factor loads are high for the scale. Scale internal consistency analysis results are; Crombach (α)= .960 for the order process dimension, Crombach (α)= .901 for the distribution dimension, Crombach (α)= .960 for the customer service dimension, Crombach (α)= .854 for inventory management dimension, Crombach (α) = .845 for recycling dimension and Crombach (α) = .771 for the general scale. Spearman Brown and Guttman Split Half technique is used for the two half-test reliability of the scale. The two half-test reliability calculated using the Spearman Brown formula was .778, and the two half-test reliability calculated using the Guttman Split-Half technique was .768. The reliability coefficient is generally considered as sufficient for the test scores of 0.70 and above (Kalaycı, 2010: 405, Büyüköztürk, 2005). These results indicate that the internal consistency of the scale and the reliability of the two half-tests are high.

Re-purchase intention questionnaires were created by reviewing the relevant literature. The questionnaire is a Five Point Likert scale, 5 is I strongly agree and 1 is I strongly disagree.

After expert opinions are obtained, exploratory factor analysis was conducted on the questionnaires. Pilot implementation was carried out on 100 students. The Kaiser-Meyer-Olkin sample measurement capability was .919. This suggests that the data set is suitable for conducting factor analysis (Kalayci et al., 2010: ; Karagöz, 2016:) The Barlett test indicated that approximately 692,048 degrees of freedom of the Chi-Square value was significant at 36 p <0.01 level. This suggests that the data set very suitable for factor analysis (Alpar, 2011; Aksu and Eser, 2017). The self-explanatory statistic λ1 = 6,123 accounts for 68,035 % of the total variance. It is understood that the scale is explained by single factor. Providing the stated variance ratio of p≥2 / 3 or p≥0.66 conditions is considered as an important basic component (Büyüköztürk, 2005). The 0.68 value obtained in the analysis results for the data set indicate that one important factor to be derived would be sufficient. The factor loads range between .740 and .881. The factor load value is 0.45 or higher, which is a good measure to prefer (Büyüköztürk, 2005). In order to explain the structure, factor loads between 0.30 and 0.40 can be defined as acceptable loads with minimum levels; loads with 0.50 and above are defined as significant loads and loads above 0.70 are loads that can best describe the structure (Alpar, 2011). This means that factor loads are high for the scale. Scale internal consistency analysis results was Crombach (α) = 941 for the scale in general. Spearman Brown and Guttman Split Half technique is used for the two half-test reliability of the scale. The two half-test reliability calculated using the Spearman Brown formula was .875, and the two half-test reliability calculated using the Guttman Split-Half technique was .864. The reliability coefficient is generally considered as sufficient for the test scores of 0.70 and above (Kalaycı, 2010, Büyüköztürk, 2005). These results indicate that the internal consistency of the scale and the reliability of the two half-tests are high.

Kolmogorov Simirnow test was performed to test the distributions of the data. According to the test results, the measurement data (KS Test Statistic: 0.62, p = .200) are normally distributed according to the reverse hypothesis (p> .05). In order to avoid Type I and Type II errors in analysis date, parametric analyses were conducted for the data. For the analysis of the data, Friedman two-way Anova test, Cronbach’s Alfa reliability test, descriptive statistics, simple linear regression analysis and Pearson moment multiplication correlation was used. And for the comparison of both groups, independent samples T Test and for the comparison of multiple groups; one-way variance analysis Anova is used. The level of significance of the study was taken as p <0,05. The findings obtained as a result of the analysis were interpreted by converting into graphs in accordance with research hypotheses.

**4.3. Hypotheses of Research**

The basic hypothesis and sub-hypotheses of this study, which examines the impact of logistics activities on the re-purchase intentions of students who attend e-commerce courses from e-commerce perspective, are;

Basic Hypothesis: From e-commerce perspective, logistics activities have a significant impact on the intention to re-purchase (Ŷ =b0+b1X1+b2X2+b3X3+b4X4+b5X5+ε).

Sub Hypothesis 1: Perception of logistic and perception of intention to repurchase averages are different among the students who attend e-commerce course on a gender basis (μ1-μ2≠0).

Sub Hypothesis 2: Perception of logistic and perception of intention to repurchase averages are different among the students who attend e-commerce course on educational level basis (μ1-μ2≠0).

Sub Hypothesis 3: Perception of logistic and perception of intention to repurchase averages are different among the students who attend e-commerce course on age basis (μ1-μ2≠0).

Sub Hypothesis 4: Perception of logistic and perception of intention to repurchase averages are different among the students who attend e-commerce course depending on income level (μ1-μ2≠0).

Sub Hypothesis 5: Perception of logistic and perception of intention to repurchase averages are different among the students who attend e-commerce course on the basis of expenditure amount (μ1-μ2≠0).

Sub Hypothesis 6: Perception of logistic and perception of intention to repurchase averages are different among the students who attend e-commerce course on the basis of expenditure area (μ1-μ2≠0).

( to be continued)

**Seren Kaya** Öğr.Gör., İstanbul Gelişim Üniversitesi, MYO, Lojistik Programı

**Muhammed Turğut** Öğr.Gör., İstanbul Gelişim Üniversitesi, MYO,Hava Lojistiği Programı

**Makale Organizasyonunu Yapan;**

Proje Geliştirme Koordinatörü, Ayşe KARAKAYA,

Lojistikcilerinsesi.biz