The Pre-Customs Release System and Supply Chain Efficiency: The Case of Egypt

References

[1] in answering the paragraph, and the researcher calculated the item discrimination coefficient for the items through (Corrected Item-Total Correlation) ) Corrected correlation coefficients between each item and the total score of its dimension, as shown in the tables that show the results of the individual discrimination coefficient.

[2] First: The Item Discrimination Index for the Independent Variable (Advance Cargo Information Declaration - ACID)

[3] Table (9) Corrected correlation coefficients between each statement and the total score for each dimension of Advance Cargo Information Declaration – ACID, n = 80

[4] phrase numberCorrected Item-Total Correlationphrase numberCorrected Item-Total Correlationphrase numberCorrected Item-Total Correlationphrase numberCorrected Item-Total CorrelationTechnological dimensionOrganizational dimensionHuman dimensionDigital Governance10.89660.912110.711160.67120.88970.812120.763170.76230.81080.709130.666180.70340.82990.851140.738190.78750.788100.726150.672200.591Table ) Corrected correlation coefficients between each statement and the total score for each dimension of Advance Cargo Information Declaration – ACID

[5] () Significant correlation at significance level 0.01

[6] It was clear from the previous table (9) that the phrases of the technological dimension have achieved substantial correlations with the total corrected degree of the dimension to which they belong, ranging between (0.788 – 0.896), and it is also clear that the statements of the organizational dimension have achieved substantial correlations with the corrected total degree of the dimension to which they belong, where they ranged between (0.726 – 0.912), and it is also clear that the statements of the human dimension achieved substantial correlations with the corrected overall score of the axis to which they belong, ranging from (0.666 – 0.763), and it is also clear that the statements of the digital governance dimension have achieved substantial correlations with the corrected overall score of the dimension to which they belong, ranging from (0.591 – 0.787), so the discriminating coefficient of phrases is good.

[7] Second: The Item Discrimination Index for the Dependent Variable (Supply chain efficiency).

[8] Table (10) Corrected correlation coefficients between each statement of the Supply chain efficiency and the total score, n = 80

[9] phrase numberCorrected Item-Total Correlationphrase numberCorrected Item-Total Correlationphrase numberCorrected Item-Total CorrelationSupply chain efficiency10.92870.924130.79420.91680.843140.78930.86090.820150.81840.853100.816160.84050.859110.873170.83260.910120.819180.723Table Corrected correlation coefficients between each statement of the Supply chain efficiency

[10] () Significant correlation at significance level 0.01

[11] It is clear from the previous table (10) that the supply chain efficiency statements achieved significant correlations with the total corrected score of the dimension to which they belong, as they ranged between (0.723 - 0.928), and thus the discrimination coefficient for the statements is considered good.

[12] Internal consistency

[13] Internal consistency is defined as the degree of correlation between items. It is a measure based on the degree of pairwise correlations between different statements in the same test. Since correlations between statements, most of the time, vary in magnitude, using the average correlation between statements is a simple and straightforward approach to show the degree of correlation Among the different statements in the test, internal consistency = mean internal correlation.

[14] Results of Internal Consistency for the Study Variables Items

[15] First: Results of internal consistency of the ACID dimensions

[16] Table (11) shows the minimum, maximum and average scores for the correlations between the paragraphs of the dimensions of ACID, n = 80

[17] Dimensions of sustainable facility managementCorrelations averageCorrelations minimumCorrelations maximumInter-Item Correlations Technological dimension0.7640.5900.941Inter-Item Correlations Organizational dimension0.7040.5910.892Inter-Item Correlations

[18] Human dimension0.5900.5060.678Inter-Item Correlations

[19] Digital Governance0.5790.4480.689Table Shows that all the phrases of the digital technology dimension

[20] The previous table (11) shows that all the phrases of the digital technology dimension achieved statistically significant correlations with each other ranging between (0.590 - 0.941), with an average correlation of (0.764), and this value indicates the existence of good internal consistency between the phrases of the digital technology dimension. Also, all the phrases of the organizational dimension achieved statistically significant correlations with each other ranging between (0.591 - 0.892), with an average correlation of (0.704), and this value indicates the existence of good internal consistency between the phrases of the organizational dimension. Also, all the phrases of the human dimension achieved statistically significant correlations with each other ranging between (0.506 - 0.678), with an average correlation of (0.590), and this value indicates the existence of good internal consistency between the phrases of the human dimension. Also, all the phrases of the digital governance dimension achieved statistically significant correlations with each other ranging between (0.448 - 0.689), with an average correlation of It reached (0.579), and this value indicates the existence of good internal consistency between the phrases of the digital governance dimension, and these values indicate the existence of good internal consistency between the phrases of the advance shipment registration system dimensions.

[21] Second: Results of internal consistency of the phrases (Supply chain efficiency)

[22] Table (12) Shows the minimum, maximum and average degrees of correlation between Supply chain efficiency statements, n = 80

[23] Here are some Recommendations and development points for the Egyptian Customs Nafeza System:

[24] Dimensions of sustainable facility managementCorrelations averageCorrelations minimumCorrelations maximumInter-Item Correlations Supply chain efficiency0.7300.4540.987Table shows that all supply chain efficiency statements achieved statistically significant correlations

[25] The previous table (12) shows that all supply chain efficiency statements achieved statistically significant correlations with each other ranging between (0.454 - 0.987), with an average correlation of (0.730). This value indicates the presence of good internal consistency between supply chain efficiency statements.

[26] 3 – reliability

[27] Reliability refers to the extent to which a scale or test (questionnaire) produces the same results when administered multiple times under the same conditions and circumstances. In other words, reliability means that the measurement tool provides stable results and does not significantly change if re-administered to the same sample at different times. The researcher assessed the reliability of the study tool through two methods: split-half reliability and Cronbach's alpha.

[28] First: Results of the stability of the phrases of the dimensions of (ACID)

[29] Table (13) shows the split-half reliability coefficients and Cronbach's alpha for the statements of the dimensions of ACID, n = 80

[30] VariableSplit halfAlpha Cronbach coefficient Correlation BeforeCorrelation afterTechnological dimension0.8700.9330.941Organizational dimension0.8380.9150.923Human dimension0.7820.8810.878Digital Governance0.7470.8600.873Table Variable Split half Alpha Cronbach coefficient

[31] The previous table No. (13) shows the following:

[32] First: Split half: The researcher calculated the stability of the ACID dimensions using the split-half method. The stability values ranged between (0.933) for the technological dimension as a maximum, and (0.860) for the digital governance dimension as a minimum. The above split-half indicators indicate that the ACID dimensions have a high stability coefficient and can achieve the study objectives. The researcher is confident in applying them to the study sample.

[33] Second: Cronbach s alpha reliability coefficient:

[34] The researcher calculated the stability of the ACID dimensions using Cronbach's alpha method. The stability values ranged between (0.941) for the technological dimension as a maximum, and (0.873) for the digital governance dimension as a minimum. The above Cronbach's alpha indicators indicate that the ACID dimensions have a high stability coefficient and can achieve the study's objectives. The researcher is confident in applying them to the study sample.

[35] Second: Results of the stability of the phrases (Supply chain efficiency)

[36] Table (14) Shows the split-half reliability coefficients and Cronbach's alpha for the Supply chain efficiency statements, n = 80

[37] variableSplit halfAlpha Cronbach coefficientCorrelation BeforeCorrelation afterSupply chain efficiency0.9840.9920.980Table Alpha Cronbach coefficient

[38] The previous table No. (14) shows the following:

[39] First: Split half:

[40] The researcher calculated the stability of the supply chain efficiency items using the split-half method, and the stability values were (0.992). The above split-half indicators indicate that the supply chain efficiency statements have a high stability coefficient and can achieve the study objectives.

[41] Second: Cronbach s alpha reliability coefficient:

[42] The researcher calculated the stability of the supply chain efficiency items using the Cronbach's alpha method, and the stability values were (0.980). The above Cronbach's alpha indicators indicate that the supply chain efficiency statements have a high stability coefficient and can achieve the study's objectives.

[43] Normal Distribution

[44] Statisticians use two types of statistical tests to test hypotheses, the first type is parametric tests, and the second type is non-parametric tests. The use of parametric tests requires the natural distribution of the data to be tested statistically, while non-parametric tests are used as an alternative to parametric tests in case the natural distribution condition of the data is not met, but only in the case of small samples with a size less than (30) individuals. For samples with a size greater than (30) individuals, the condition of natural distribution can be abandoned according to the central limit theorem, and Norman (2010)2 concluded that parametric tests can be used with Likert scale data regardless of sample size, and regardless of whether the data follows a normal distribution or not. In this study, parametric tests will be used according to what was mentioned previously, regardless of the natural distribution of the data.

[45] Sixth: The statistical treatments used in the study

[46] nuuser testtest componentsIn the study, the researcher relied on (spss 27) and (Amos 24) 1Tests to measure the reliability and validity of the study variablesItem Discrimination coefficientinternal consistency Split-Half Coefficient & Cronbach's alpha 2Tests to measure the descriptive analysis of the variables of the studyFrequency tables and percentagesArithmetic mean & Relative weightstandard deviation & Graphics3hypotheses testsSimple Linear Regression Analysis(Stepwise) Multiple RegressionTable No. (15) Statistical tests used in the study.

[47] Table Statistical tests used in the study

[48] After following the steps of scientific research in the methodology, tools, statistical methods, and ensuring the psychometric properties of these tools, and by applying them to a survey sample, it was possible to start the field study and present and discuss the results according to the proposed hypotheses.

[49] (The results of the field study)

[50] Descriptive statistics results for the dimensions of (Advance Cargo Information Declaration - ACID)

[51] First: Analysis of Statements: "Technological dimension"

[52] Table (16) Means and standard deviations of the opinions of the study sample regarding the items (Technological dimension), n = 455

[53] nuitems of Technological dimensionmeanStd. Deviationrelative weightsignificance level1The single window system (window) contributes to improving the efficiency of operations.4.17.55483.40%High2The single window system (window) contains an integrated package of new technologies that facilitate digital practices.4.20.54684.00%High3The single window system (window) positively affects the workflow.4.18.53283.60%High4The single window system (window) helps improve the quality of data available for decision-making.4.15.53783.00%High5The single window system (window) helps reduce costs.4.03.57080.60%Hightotal Technological dimension4.1440.43882.88%HighTable Means and standard deviations of the opinions of the study sample regarding the items (Technological dimension), n = 455

[54] *Items numbers were placed in the order of their occurrence in the questionnaire list in all study tables.

[55] The previous table No. (16) shows the arithmetic means and standard deviations of the study sample's responses to the technological dimension statements. The total score in the table indicates that the study sample's score is (high), as the arithmetic mean of the total score for the technological dimension was (4.144) with a standard deviation of (0.438) and a percentage of (82.88%), which indicates a decrease in the dispersion of the opinions of the study sample and a convergence of opinions.

[56] It is noted from this table that statement No. (2) obtained the highest arithmetic means, which amounted to (4.20) with a standard deviation of (0.546), and a percentage of (84%), and came with a (high) score.

[57] While statement No. (5) obtained the lowest arithmetic means, which amounted to (4.03) with a standard deviation of (0.570), and a percentage of (80.60%), and came with a (high) score.

[58] Second: Analysis of Statements: "Digital Innovation"

[59] Table (17) Means and standard deviations of the opinions of the study sample regarding the items (Organizational dimension), n = 455

[60] nuitems of Organizational dimensionmeanStd. Deviationrelative weightsignificance level6I believe that the regulatory policies in the one-stop shop system (window) are clear and understandable.4.17.61283.40%High7I believe that the window has enough flexibility to respond to changes in the external environment.4.19.53283.80%High8There are enough opportunities for training and development.4.07.53581.40%High9The Egyptian Company for Electronic Commerce Technology supports employees to achieve their career goals.4.14.58882.80%High10The Egyptian Company for Electronic Commerce Technology works to implement regulatory policies effectively and transparently.4.01.64780.20%Hightotal Organizational dimension4.1150.50682.30%HighTable Means and standard deviations of the opinions of the study sample regarding the items (Organizational dimension),

[61] *Items numbers were placed in the order of their occurrence in the questionnaire list in all study tables.

[62] The previous table No. (17) shows the arithmetic means and standard deviations of the study sample's responses to the organizational dimension statements. The total score in the table indicates that the study sample's score is (high), as the arithmetic mean of the total score for the organizational dimension was (4.115) with a standard deviation of (0.506) and a percentage of (82.30%), which indicates a decrease in the dispersion of the opinions of the study sample and a convergence of opinions.

[63] It is noted from this table that statement No. (7) obtained the highest arithmetic means, which amounted to (4.19) with a standard deviation of (0.532), and a percentage of (83.80%), and came in a (high) degree.

[64] While statement No. (10) obtained the lowest arithmetic means, which amounted to (4.01) with a standard deviation of (0.647), and a percentage of (80.20%), and came in a (high) degree.

[65] Third: Analysis of Statements: " Human dimension "

[66] Table (18) Means and standard deviations of the opinions of the study sample regarding the items (Human dimension), n = 455

[67] nuitems of Human dimensionmeanStd. Deviationrelative weightsignificance level11I was able to adapt easily to the new technology used in the work.3.91.67378.20%High12The training on the new single window system (window) was sufficient to enable me to perform my work tasks effectively.3.94.59678.80%High13The transition to the single window system (window) contributed to increasing the quality of work performance.3.77.51175.40%High14I received adequate support from management during the training process on using the single window system (window).3.81.56976.20%High15I feel confident in my ability to benefit from the technological transformation and use the single window system (window).3.72.63274.40%Hightotal Human dimension3.8320.53576.64%HighTable Means and standard deviations of the opinions of the study sample regarding the items (Human dimension)

[68] *Items numbers were placed in the order of their occurrence in the questionnaire list in all study tables.

[69] The previous table No. (18) shows the arithmetic means and standard deviations of the study sample's responses to the human dimension statements. The total score in the table indicates that the study sample's score is (high), as the arithmetic mean of the total score for the human dimension was (3.832) with a standard deviation of (0.535) and a percentage of (76.64%), which indicates a decrease in the dispersion of the opinions of the study sample and a convergence of opinions.

[70] It is noted from this table that statement No. (12) obtained the highest arithmetic means, which amounted to (3.94) with a standard deviation of (0.596), and a percentage of (78.80%), and came in a (high) degree.

[71] While statement No. (15) obtained the lowest arithmetic means, which amounted to (3.72) with a standard deviation of (0.632), and a percentage of (74.40%), and came in a (high) degree.

[72] Fourth: Analysis of Statements: " Digital Governance "

[73] Table (19) Means and standard deviations of the opinions of the study sample regarding the items (Digital Governance), n = 455

[74] nuitems of Digital GovernancemeanStd. Deviationrelative weightsignificance level16Digital governance contributes to improving transparency within the country4.17.65683.40%high17Digital governance enhances trust between the organization and stakeholders4.16.69383.20%High18Digital governance provides a robust framework for data management and protection.4.20.63284.00%high19Digital governance contributes to improving strategic decision-making.4.18.64483.60%High20Digital governance helps achieve compliance with laws and regulations.4.13.67882.60%Hightotal Digital Governance4.1680.53183.36%HighTable Means and standard deviations of the opinions of the study sample regarding the items (Digital Governance)

[75] *Items numbers were placed in the order of their occurrence in the questionnaire list in all study tables.

[76] The previous table No. (19) shows the arithmetic means and standard deviations of the study sample's responses to the digital governance statements. The total score in the table indicates that the study sample's score is (high), as the arithmetic mean of the total score for digital governance was (4.168) with a standard deviation of (0.531) and a percentage of (83.36%), which indicates a decrease in the dispersion of the study sample's opinions and a convergence of opinions.

[77] It is noted from this table that statement No. (18) obtained the highest arithmetic means, which amounted to (4.20) with a standard deviation of (0.632), and a percentage of (84%), and came with a (high) score.

[78] While statement No. (20) obtained the lowest arithmetic means, which amounted to (4.13) with a standard deviation of (0.678), and a percentage of (82.60%), and came with a (high) score. Technological dimension

[79] dimensions of ACIDNumber of phrasesOrder of importancemeanStd. Deviationrelative weightsignificance levelDigital Governance514.1680.53183.36%HighTechnological dimension524.1440.43882.88%HighOrganizational dimension534.1150.50682.30%HighHuman dimension543.8320.53576.64%HighThte total degree of dimensions of Advance Cargo Information Declaration - ACID

[80] Table (20) Averages, Standard Deviations and Weighted Percentage Average of Study Sample Opinions in Each Dimension of Advance Cargo Information Declaration - ACID

[81] Table Averages, Standard Deviations and Weighted Percentage Average of Study Sample Opinions in Each Dimension of Advance Cargo Information Declaration - ACID

[82] The previous table No. (20) shows the arithmetic means and standard deviations of the study sample’s responses to each dimension of the ACID system, arranged in descending order of importance. The total score in the table indicates that the ACID dimensions score is (high), as the arithmetic mean of this total score was (4.065) with a standard deviation of (0.321) and a percentage of (81.30%), which indicates a decrease in the dispersion of the study sample’s opinions towards the ACID dimensions. It is noted in this table that the dimensions of the ACID system came in a high degree, as the digital governance dimension came in first place with a (high) degree with an arithmetic mean of (4.168), which is higher than the overall arithmetic mean of (4.065), a standard deviation of (0.531), and a percentage of (83.36%), while the human dimension came in last place with an arithmetic mean of (3.832), which is lower than the overall arithmetic mean of (4.065), a standard deviation of (0.535), and a percentage of (76.64%).

[83] Figure shows the average and percentage values of the study sample’s opinions on the dimensions of the ACID system

[84] Chart No. (11) shows the average and percentage values of the study sample s opinions on the dimensions of the ACID system.

[85] Descriptive Statistics Results (Supply chain efficiency)

[86] Table (21) Means and standard deviations of the opinions of the study sample regarding the Supply chain efficiency statements, n = 455

[87] nuitems of Supply chain efficiencymeanStd. Deviationrelative weightsignificance level1Suppliers always meet delivery deadlines.3.98.48379.60%high2Costs associated with the supply chain are in line with the organization’s budget.4.10.50882.00%High3Inventory management in the organization is efficient and reduces surpluses and shortages.3.85.46377.00%high4Costs associated with the supply chain are in line with the organization’s budget.3.67.50573.40%High5Supply chain operations are transparent as required.3.81.46776.20%High6There is effective coordination between different departments in the supply chain.3.95.49079.00%high7Technology used in the supply chain supports operational efficiency.3.97.49379.40%High8The system used for forecasting demand helps reduce waste.4.08.51081.60%high9There are effective strategies for managing risks in the supply chain.3.87.46977.40%High10The organization’s supply chain adheres to environmental and social sustainability standards.3.67.51673.40%High11The organization’s supply chain is flexible and able to adapt to unexpected circumstance3.80.48476.00%high12Clearance time became shorter in duration.3.96.50679.20%high13The single window system (window) always works stably in the system without problems.4.12.51182.40%high14After implementing the system, costs decreased.3.84.46376.80%high15Production lines were not affected due to the shortage of raw materials after implementing the new system.3.66.49673.20%high16External suppliers accepted the new work system well.3.79.48875.80%high17Training was available for supply chain workers.3.94.50878.80%high18The system got rid of fake suppliers with poor quality and there was transparency in the work3.99.47879.80%hightotal Supply chain efficiency3.8910.40377.82%HighTable Means and standard deviations of the opinions of the study sample regarding the Supply chain efficiency statements

[88] *Items numbers were placed in the order of their occurrence in the questionnaire list in all study tables.

[89] The previous table No. (21) shows the arithmetic means and standard deviations of the study sample's responses to the supply chain efficiency statements. The total score in the table indicates that the study sample's score is (very high), as the arithmetic mean of the total score for supply chain efficiency was (3.891) with a standard deviation of (0.403) and a percentage of (77.82%), which indicates a decrease in the dispersion of the study sample's opinions and a convergence of opinions.

[90] It is noted from this table that statement No. (13) obtained the highest arithmetic means, which amounted to (4.12) with a standard deviation of (0.511), and a percentage of (82.40%), and came in a (high) degree.

[91] While statement No. (15) obtained the lowest arithmetic means, which amounted to (3.66) with a standard deviation of (0.488), and a percentage of (73.20%), and came in a (high) degree.

[92] Analyze the suitability of data to test study hypotheses

[93] Before starting to test the study hypotheses, which are “the impact of the single window system on the efficiency of the supply chain”, for regression, there are a set of procedures that must be carried out to fit the data to the assumptions of the regression analysis, which are represented by the Tolerance test and the Variance Inflation Factor (VIF) coefficient.

[94] Table (22) shows the results of the variance inflation test and the permissible variance

[95] undimensions of ACIDVariance Inflation Factor (VIF)Tolerance1Technological dimension1.3810.7242Organizational dimension1.0840.9233Human dimension1.2640.7914Digital Governance1.0830.924Table shows the results of the variance inflation test and the permissible variance

[96] The results in Table (22) show that there is no multicollinearity between the independent variables, and this is confirmed by the values of the variance inflation factor (VIF) test standard for the dimensions of the independent variable (ACID), which ranged between (1.071 - 1.377), which is a well-accepted value; since whenever the VIF value is greater than (3), there is a possibility of multicollinearity, and if it is greater than (10), this confirms the presence of multicollinearity between the variables, and since the VIF value for the study variables is less than (3) and less than (10), there is no multicollinearity.

[97] Tolerance test is one of the measures that indicate the presence or absence of the problem of multicollinearity. If the value of Tolerance is greater than (0.05), this indicates the absence of the problem of multicollinearity. However, if the value of Tolerance is less than (0.05), this indicates the presence of the problem of multicollinearity. The values of Tolerance ranged between (0.726 - 0.933), and all these values are considered greater than (0.05). From the above, there is no multicollinearity between the dimensions of the independent variable (Hamadoush, 2019).

[98] Results of the study hypotheses

[99] main hypothesis: There is a statistically significant impact relationship between the dimensions of Advance Cargo Information Declaration - ACID on the Supply chain efficiency

[100] The following sub-hypotheses derive from this hypothesis:

[101] First sub-hypothesis:

[102] There is a statistically significant impact relationship of technological dimension on supply chain efficiency.

[103] To validate the first sub-hypothesis, the researcher used simple linear regression.

[104] Table No. (23) shows the results of simple linear regression analysis of the impact of technological dimension on supply chain efficiency.

[105] Dependent variableRSquarefSigDFRegression coefficient

[106] ²tSigsupply chain efficiency0.5940.353246.6910.00Regression1Constant29.16415.7060.00Residual453technological dimension1.972Total454Prediction equation

[107] (Simple linear regression)supply chain efficiency = 29.164 + 1.972 technological dimension.

[108] It is clear from the previous table No. (22) that there is a relationship of the impact of the technological dimension on the efficiency of the supply chain, where the correlation coefficient R (0.594) at the level of significance (0.01), while the coefficient of determination reached (0.353), meaning that the value of (35.3%) of the change in achieving supply chain efficiency, resulting from the change in the technological dimension, and the value of the degree of regression coefficient ² (1.972), meaning that a one-degree increase in the technological dimension leads to an increase in supply chain efficiency by (1.972), and the significance of this effect is confirmed by the calculated F value (246.691), which is a function of a significant level (0.01), and the calculated T value (15.706) It is a function at a significant level (0.01), and from the above it is clear to us the acceptance of the first sub-hypothesis, which states: There is a statistically significant impact relationship of the technological dimension on supply chain efficiency.

[109] Second sub-hypothesis:

[110] There is a statistically significant impact relationship of organizational dimension on supply chain efficiency.

[111] Table No. (24) shows the results of simple linear regression analysis of the impact of organizational dimension on supply chain efficiency.

[112] Dependent variableR

[113] SquarefSigDFRegression coefficient

[114] ²tSigsupply chain efficiency0.5390.290185.3050.00Regression1Constant38.28813.6130.00Residual453organizational dimension1.546Total454Prediction equation

[115] (Simple linear regression)supply chain efficiency = 38.288 + 1.546 organizational dimension.

[116] It is clear from the previous table No. (24) that there is an impact relationship of the organizational dimension on the efficiency of the supply chain, as the correlation coefficient R reached (0.539) at a significance level of (0.01), while the coefficient of determination reached (0.290), meaning that the value of (29%) of the change in achieving supply chain efficiency is a result of the change in the organizational dimension, and the value of the regression coefficient ² reached (1.546), which means that an increase of one degree in the organizational dimension leads to an increase in the efficiency of the supply chain by an amount of (1.546), and the significance of this effect is confirmed by the calculated F value, which reached (185.305), which is significant at a significance level of (0.01), and the calculated T value reached (13.613), which is significant at a significance level of (0.01). From the above, it is clear to us that the second sub-hypothesis is accepted, which states that: There is a statistically significant impact relationship of organizational dimension on supply chain efficiency.

[117] Third sub-hypothesis:

[118] There is a statistically significant impact relationship of the human dimension on the efficiency of the supply chain.

[119] Table No. (23) shows the results of simple linear regression analysis of the impact of human dimension on supply chain efficiency.

[120] Dependent variableR

[121] SquarefSigDFRegression coefficient

[122] ²tSigsupply chain efficiency0.6050.366261.1020.00Regression1Constant38.63016.1590.00Residual453human dimension1.640Total454Prediction equation

[123] (Simple linear regression)supply chain efficiency = 38.630 + 1.640 human dimension.Table shows the results of simple linear regression analysis of the impact of human dimension on supply chain efficiency

[124] It is clear from the previous table No. (23) that there is an impact relationship of the human dimension on the efficiency of the supply chain, as the correlation coefficient R reached (0.605) at a significance level of (0.01), while the coefficient of determination reached (0.366), meaning that (36.6%) of the change in achieving supply chain efficiency is a result of the change in the human dimension. The value of the regression coefficient ² reached (1.640), which means that a one-degree increase in the human dimension leads to an increase in the efficiency of the supply chain by an amount of (1.640). The significance of this effect is confirmed by the calculated F value, which reached (261.102), which is significant at a significance level of (0.01), and the calculated T value reached (16.159), which is significant at a significance level of (0.01). From the above, it is clear to us that the third sub-hypothesis is accepted, which states that: There is a statistically significant impact relationship of the human dimension on the efficiency of the supply chain.

[125] Fourth sub-hypothesis:

[126] There is a statistically significant impact relationship of digital governance on supply chain efficiency.

[127] Table No. (24) shows the results of simple linear regression analysis of the impact of digital governance on supply chain efficiency.

[128] Dependent variableR

[129] SquarefigDFRegression coefficient

[130] ²tgsupply chain efficiency0.5560.309203.0370.00Regression1Constant38.31314.2490.00Residual453digital governance1.522Total454Prediction equation

[131] (Simple linear regression)supply chain efficiency = 38.313 + 1.522 digital governance.Table ) shows the results of simple linear regression analysis of the impact of digital governance on supply chain efficiency

[132] It is clear from the previous table No. (24) that there is a relationship between the impact of digital governance on supply chain efficiency, as the correlation coefficient R reached (0.556) at a significance level of (0.01), while the coefficient of determination reached (0.309), meaning that (30.9%) of the change in achieving supply chain efficiency is due to the change in digital governance. The value of the regression coefficient ² reached (1.522), which means that a one-degree increase in digital governance leads to an increase in supply chain efficiency by an amount of (1.522). The significance of this effect is confirmed by the calculated F value, which reached (203.037), which is significant at a significance level of (0.01), and the calculated T value reached (14.249), which is significant at a significance level of (0.01). From the above, it is clear to us that the fourth sub-hypothesis is accepted, which states that: There is a statistically significant impact relationship for digital governance on supply chain efficiency.

[133] Major hypothesis: There is a statistically significant impact relationship between the dimensions of Advance Cargo Information Declaration - ACID on the Supply chain efficiency.

[134] To verify the validity of this hypothesis, the researcher used the multiple regression analysis method to test this hypothesis, and before conducting the multiple regression test, the researcher made sure of the validity of the model as shown in the following table.

[135] Table (25) results of the regression analysis to ensure the validity of the model in testing the first main hypothesis

[136] ModeldfSum of SquaresMean SquareFSig. FRegression0.66748990.1952247.549605.1260.00Residual5201931.3753.714Total52410921.570Table ) results of the regression analysis to ensure the validity of the model in testing the first main hypothesis

[137] The statistical results shown in the previous table No. (25) indicate that the model is valid for testing the main hypothesis, due to the higher calculated F value (605.126) than its tabular value at a significance level of 0.01 and degrees of freedom (4,520,524). It is clear from the same table that the dimensions of the independent variable (Advance Cargo Information Declaration - ACID) in this model explain an estimated (82.3%) of the change in the dependent variable (Supply chain efficiency), based on the value of the coefficient of determination = (0.823), which is a very good explanatory ability to explain the variance in the dependent variable (Supply chain efficiency).

[138] Based on the stability of the model’s validity, the main hypothesis was tested using stepwise multiple regression analysis to test the priority of entering the dimensions of Advance Cargo Information Declaration - ACID into the regression analysis model with the aim of determining the explanatory power of each dimension of Advance Cargo Information Declaration - ACID in the dependent variable (Supply chain efficiency). Table No. (28) shows the results of the stepwise multiple regression analysis.

[139] Table (26) shows the relationship between the dimensions of Advance Cargo Information Declaration - ACID and Supply chain efficiency

[140] dimensions of Advance Cargo Information Declaration - ACIDR²ConstantFSig. FTSig. THuman dimension0.6050.3661.181-11.816261.1020.0019.0800.00Digital Governance0.7950.6321.158387.5630.0020.0010.00Organizational dimension0.8840.7821.028540.2990.0016.7890.00Technological dimension0.9010.8120.673486.2340.008.4450.00Prediction equation

[141] (Multiple linear regression)Supply chain efficiency = -11.816 + 1.181 Human dimension + 1.158 Digital Governance + 1.028 Organizational dimension + 0.673 Technological dimension.Table shows the relationship between the dimensions of Advance Cargo Information Declaration - ACID and Supply chain efficiency

[142] It is clear from the previous table No. (26) that:

[143] The entry of the dimensions of the Advance Cargo Information Declaration - ACID (the independent variable) into the regression equation, the dimension (the human dimension) occupied the first place in its entry into the regression equation and explains (36.6%) of the value of the impact strength on the Supply chain efficiency (the dependent variable) based on the coefficient of determination (), and the value of the degree of the regression coefficient ² reached (1.181), which means that a one-degree increase in the dimension of the human dimension leads to an increase in the Supply chain efficiency by a value of (1.181), and the significance of this effect is confirmed by the calculated F value, which reached (261.102), which is significant at a significance level of (0.01), and the calculated T value reached (19.080), which is significant at a significance level of (0.01).

[144] This is followed by the dimension of (digital governance), which explained, along with the dimension of (human dimension), a percentage of (63.2%) of the value of the power of influence on the Supply chain efficiency (the dependent variable), as the value of the degree of influence ² reached (1.158), which means that an increase of one degree in each of the human dimension and digital governance leads to an increase in the Supply chain efficiency by a value of (1.158), and the significance of this effect is confirmed by the calculated F value, which reached (387.563), which is significant at a significance level of (0.01), and the calculated T value reached (20.001), which is significant at a significance level of (0.01).

[145] This is followed by the dimension (organizational dimension), which explained, along with each of (human dimension and digital governance), a percentage of (78.2%) of the value of the power of influence on the Supply chain efficiency (the dependent variable), as the value of the degree of influence ² reached (1NOS^_efwxyz{€‚†‡‘’“¹º»¼ñæÜÏÁÏÜÏÁ´¦š‘š‘š…š¦yk\Mhþ@§h˜gCJOJQJaJhþ@§h^~ CJOJQJaJhÃ8fh^~ 6�OJQJZ�hÃ8fh0ã6�OJQJhÃ8fh`@6�OJQJh`@6�OJQJhÃ8fhÃ8f6�OJQJh:øhÃ8f6�H*OJQJhÃ8fhÃ8fOJPJQJh:øh"iH*OJPJQJhÃ8fh"iOJPJQJh"iOJPJQJhþ@§h^~ OJQJhþ@§h0ãCJ(OJPJQJNOy’º»¼¬­UVWdeéê�‚õèÚõõÈȾ¾¾¾¾¤��¾¾¾¤gd:ø$ &F6„7„Éýd¤^„7`„Éýa$gd:øm$ $¤a$gd:ø„ñÿ„ dð¤]„ñÿ^„ gd_)| [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]

[146] $„öÿ¤`„öÿa$gd:ø$dð¤a$gd_)| $¤a$gd:ø¼ÄÆ\ ] ]]«¬­»TUVWcdee§éê�îÜÈ´ÈÈÈÈ´´È£’~tmdXXXK;h:øhæd?CJOJQJ\�aJh:øCJOJQJ\�aJh:øhæd?CJ\�aJh:øCJ\�aJ [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]

@article{1707417,
      author = {Dr. Mostafa Gad, Dr. Saber Shaker},
      title = {The Pre-Customs Release System and Supply Chain Efficiency: The Case of Egypt},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {9},
      pages = {230-252},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707417.pdf},
      abstract = {This research investigates the relationship between the Advance Cargo Information (ACI) system and supply chain efficiency, with a focus on its impact on international trade and customs clearance procedures in Egypt. As part of Egypt?s digital transformation strategy, the ACI system was introduced to modernize customs administration, enhance transparency, and improve operational efficiency in logistics. The study explores how the implementation of the ACI system influences customs clearance times, reduces bureaucratic inefficiencies, mitigates risks, and ensures compliance with international trade regulations. Through a mixed-methods approach combining qualitative and quantitative data, including surveys, case studies, and statistical analysis, the research assesses the real-world effects of ACI on shipping, logistics operations, and customs procedures. Findings reveal that the ACI system contributes to improving supply chain efficiency by reducing delays, automating processes, and increasing the accuracy of trade documentation. However, challenges such as system adaptability, training for stakeholders, and integration with global supply chain networks remain critical concerns. The study concludes with recommendations for policymakers, businesses, and logistics professionals on optimizing ACI implementation to maximize economic and trade benefits. These insights contribute to the broader understanding of digital customs transformation and its role in enhancing supply chain management.},
      keywords = {Supply Chain Efficiency, Advance Cargo Information (ACI), Customs Clearance, Digital Transformation, Trade Facilitation, Logistics, International Trade.},
      month = {March},
  }