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"[ABSTRAKSkripsi ini membahas tentang determinan intensi pembelian atau intentions to purchase travel online terhadap tiket.com.Menggunakan gabungan tiga model yaitu TRA, TPB dan IDT. Determinan dari TRA yaitu attitude, TPB yaitu perceived behavioral control, IDT yaitu compatibility, complexity dan perceived relative advantage. Terdapat 100 responden yang pernah menggunakan tiket.com untuk mencari tiket. Dengan menggunakan Smart PLS 2.0 terdapat tujuh hipotesis yang signifikan diantaranya attitude, compatibility, trust, perceived relative advantage, perceived behavioral control, complexity dalam mempengaruhi intentions to purchase travel online

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ABSTRACTThis research investigates determinant of intentions to purchase travel online toward tiket.com. Using 3 model TRA,TPB and IDT. Attitude derived from TRA, perceived behavioral control from TPB and compatibility, complexity, perceived relative advantage derived from IDT. 100 respondent have using tiket.com for searching tikets are concluded in this research. Using Smart PLS 2.0, 7 out of 16 were significantly proven which is attitude, compatibility, trust, perceived relative advantage,perceived behavioral control, complexity to affect intentions to purchase travel online;ABSTRAKSkripsi ini membahas tentang determinan intensi pembelian atau intentions to purchase travel online terhadap tiket.com.Menggunakan gabungan tiga model yaitu TRA, TPB dan IDT. Determinan dari TRA yaitu attitude, TPB yaitu perceived behavioral control, IDT yaitu compatibility, complexity dan perceived relative advantage. Terdapat 100 responden yang pernah menggunakan tiket.com untuk mencari tiket. Dengan menggunakan Smart PLS 2.0 terdapat tujuh hipotesis yang signifikan diantaranya attitude, compatibility, trust, perceived relative advantage, perceived behavioral control, complexity dalam mempengaruhi intentions to purchase travel online

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ABSTRACTThis research investigates determinant of intentions to purchase travel online toward tiket.com. Using 3 model TRA,TPB and IDT. Attitude derived from TRA, perceived behavioral control from TPB and compatibility, complexity, perceived relative advantage derived from IDT. 100 respondent have using tiket.com for searching tikets are concluded in this research. Using Smart PLS 2.0, 7 out of 16 were significantly proven which is attitude, compatibility, trust, perceived relative advantage,perceived behavioral control, complexity to affect intentions to purchase travel online, ABSTRAKSkripsi ini membahas tentang determinan intensi pembelian atau intentions to purchase travel online terhadap tiket.com.Menggunakan gabungan tiga model yaitu TRA, TPB dan IDT. Determinan dari TRA yaitu attitude, TPB yaitu perceived behavioral control, IDT yaitu compatibility, complexity dan perceived relative advantage. Terdapat 100 responden yang pernah menggunakan tiket.com untuk mencari tiket. Dengan menggunakan Smart PLS 2.0 terdapat tujuh hipotesis yang signifikan diantaranya attitude, compatibility, trust, perceived relative advantage, perceived behavioral control, complexity dalam mempengaruhi intentions to purchase travel online

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ABSTRACTThis research investigates determinant of intentions to purchase travel online toward tiket.com. Using 3 model TRA,TPB and IDT. Attitude derived from TRA, perceived behavioral control from TPB and compatibility, complexity, perceived relative advantage derived from IDT. 100 respondent have using tiket.com for searching tikets are concluded in this research. Using Smart PLS 2.0, 7 out of 16 were significantly proven which is attitude, compatibility, trust, perceived relative advantage,perceived behavioral control, complexity to affect intentions to purchase travel online]"

"Aktivitas logistik memiliki peran penting dalam kehidupan manusia untuk memenuhi kebutuhan sehari-hari. Populasi dan ekonomi terus berkembang begitu pula dengan aktivitas logistik karena permintaan barang bergantung pada pertumbuhan keduanya. Semakin banyaknya permintaan barang maka semakin banyak pula transportasi yang mengangkut dan akan menyebabkan kemacetan serta polusi. Kemacetan akan menunda pergerakan barang dan manusia yang nantinya mengakibatkan lambatnya pertumbuhan ekonomi. Penelitian ini menganalisis pola perjalanan dan freight trip generation yang berada di Pusat Perdagangan Tanah Abang. Dalam hal ini, penulis menganalisis karakteristik perjalanan angkutan barang Tanah Abang dan melakukan pemodelan bangkitan perjalanan angkutan barang yang diproduksi dan ditarik oleh tiap toko yang ada di Tanah Abang. Untuk melakukan analisis tersebut penulis melakukan survei wawancara langsung ke lapangan untuk mendapatkan data. Data tersebut didapatkan dari responden yang merupakan karyawan atau pemilik toko. Pemodelan dilakukan menggunakan analisis regresi linear dengan menentukan model terbaik dari seluruh model yang dibentuk. Hasil dari regresi linear menghasilkan 4 model yang dapat dinyatakan “fit” dengan variabel berpengaruh yaitu, jumlah karyawan dan luas toko. Model tersebut tervalidasi dengan membanding data aktual dengan prediksi yang berarti nilai RMSE mendekati angka nol. Temuan statistik dari model ini menunjukkan bahwa bangkitan perjalanan angkutan barang lebih sensitif terhadap variabel jumlah karyawan. Model ini dibuat agar perencana dan pembuat kebijakan dapat memperoleh manfaat dari penelitian untuk membuat keputusan terkait angkutan barang.

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Logistics activities have an important role in human life to meet daily needs. As populations and economies continue to expand, so do logistics activities because demand for goods depends on the growth of both. The more demand for goods, the more transportation that transports and will cause congestion and pollution. Congestion will delay the movement of goods and people which will result in slow economic growth. This study analyses travel patterns and freight trip generation in the Tanah Abang Trade Centre. In this case, the author analyses the characteristics of Tanah Abang freight transportation trips and models the rise of freight transportation trips produced and withdrawn by each store in Tanah Abang. To conduct the analysis, the authors conducted a survey of interviews directly to the field to obtain data. The data is obtained from respondents who are employees or store owners. Modelling is carried out using linear regression analysis by determining the best model from all models formed. The results of linear regression produce 4 models that can be declared "fit" with influential variables, namely, the number of employees and store area. The model is validated by comparing actual data with predictions which means the RMSE value is close to zero. The statistical findings of this model show that the rise of freight travel is more sensitive to the variable number of employees. This model was created so that planners and policymakers can benefit from research to make decisions regarding freight transportation."