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"Indeks Sentimen Konsumen (ISK) banyak digunakan sebagai pengukur keyakinan pembelian di masa mendatang untuk memprediksi perilaku pembelian agregat di masa datang. Studi ini secara empiris membandingkan antara dua model proyeksi : model pengharapan (the expectation model) yang memasukkan ISK sebagai variabel penjelasan dan model tradisional (the traditional model) yang tidak memasukkan ISK. Kedua modal tersebut digunakan untuk mengestimasi permintaan agregat atas mobil baru di Amerika sejak 1976 sampai 1984. Hasil studi mengindikasikan bahwa ISK memiliki hubungan positif dengan penjualan mobil baru. Namun, berdasarkan kemungkinan kriteria dominan (the likelihood dominance criterion), model tradisional lebih baik dibandingkan model pengharapan. Selain itu, kemampuan memprediksi dari model pengharapan sedikit lebih rendah dibandingkan model tradisional.

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The index of consumer sentiment (ICS) has been widely employed as a proxy for future buying confidence to predict future aggregate buying behavior. This study empirically compares two forecast models: the expectation model that includes the ICS as an explanatory variable and the traditional model that does not include the ICS. The models are employed to estimate the aggregate demand for new cars in the U.S. from 1976 to 1984. The results indicate that the ICS has a positive relation with new car sales. On the basis of the likelihood dominance criterion, however, the traditional model is preferred to the expectation model. Furthermore, the forecast ability of the expectation model is slightly inferior to the traditional model."

"Penelitian ini bertujuan untuk mengidentifikasi faktor-faktor yang mempengaruhi perencanaan perjalanan pada generasi milenial dengan menggunakan pendekatan elaboration likelihood. Sampel yang penulis gunakan adalah Milenial Indonesia yang aktif menggunakan media sosial dan pernah menggunakan media sosial untuk merencanakan pengalaman berwisata. Penelitian ini menggunakan Structural Equation Modeling (SEM) untuk mengolah datanya. Hasil penelitian menunjukkan bahwa pada proses awal perencanaan, jalur periferal yang terdiri dari kredibilitas sumber dan reputasi media sosial sangat berperan. Sedangkan pada proses selanjutnya perencanaan rute pusat merupakan jalur utama yang mempengaruhi pengguna ketika mereka mulai menggali lebih banyak informasi dan semakin tertarik dengan informasi yang mereka cari. Ini menyimpulkan bahwa selama proses keputusan pembelian, rute selanjutnya memainkan peran besar, tetapi rute awal memainkan peran penting untuk menarik perhatian pelanggan potensial sebelum mereka memutuskan produk atau layanan mana yang ingin mereka fokuskan.

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This study aims to identify the factors that influence trip planning for the millennial generation using the elaboration likelihood approach. The sample that the author uses is Indonesian Millennials who actively use social media and have used social media to plan travel experiences. This study uses Structural Equation Modeling (SEM) to process the data. The results showed that in the initial planning process, peripheral pathways consisting of source credibility and social media reputation played a major role. Whereas in the next process central route planning is the main route influencing users as they start to dig more information and become more interested in the information they are looking for. It concludes that during the buying decision process, the next route plays a big role, but the initial route plays an important role in grabbing the attention of potential customers before they decide which product or service they want to focus on."

"Peramalan permintaan bantuan logistik merupakan acuan untuk kegiatan distribusi yang optimal pasca gempa bumi. Peramalan permintaan yang akurat dan efisien dapat mencegah habisnya ketersediaan bantuan logistik, mempercepat waktu distribusi, dan menjamin setiap korban gempa bumi memperoleh bantuan logistik yang dibutuhkan, sehingga dapat mengurangi penderitaan dan menyelematkan hidup mereka. Penelitian ini bertujuan untuk merancang model peramalan permintaan bantuan logistik pasca gempa bumi. Pendekatan Case-based Reasoning (CBR) dengan dukungan internet of things (IoT) digunakan pada penelitian ini. Gempa bumi Lombok utara yang terjadi pada tahun 2018 digunakan sebagai kasus target yang akan diramal permintaan bantuan logistiknya. Hasil peramalan diperoleh berdasarkan kasus gempa bumi yang paling similar dengan kasus target. Similaritas kedua kasus ditentukan berdasarkan enam atribut yaitu: magnitudo, kedalaman gempa bumi, jarak episentrum, jumlah populasi terdampak, durasi tanggap darurat (hari), dan Modified Marcelli Intensity (MMI). Penerapan IoT dapat memberikan nilai atribut secara real time sehingga hasil peramalan diperoleh secara cepat. Hasil peramalan permintaan bantuan logistik menunjukkan MAPE di bawah 20%, sehingga dikategorikan sebagai hasil peramalan yang baik dan akurat.

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The demand forecasting of emergency logistic relief is a premise and basis for optimal emergency distribution after earthquake. Accurate and efficient demand forecast will prevent stock-out, save time, and ensure every victims get the critical supplies to reduce their suffering and save their life. This paper aims to design demand forecasting model of emergency logistic relief after earthquake. Case-based Reasoning (CBR) method supported by Internet of Things (IoT) is applied to develop the model. This paper uses eartquake incident which struck North Lombok regency in 2018 as target case.

The demand forecasting result is obtained based on the historical case that are the most similar to the target case. The similarity is determined by six attributes: earthquake magnitude, depth of hypocenter, epicentrum distance, total affected population, duration of response phase (day), and modified marcelli intensity (MMI). IoT supports the model to acquire real-time attributes value when earthquake occurs so the forecasting result will be obtained quickly. From the target case, the results shows overall forecast error lower than 20% and open the door for conducting emergency logistic relief demand forecast with quantitative and qualitative approach."

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