ANALISIS CUSTOMER VALUE INDEX DALAM MEMILIH LOW COST GREEN CAR DI INDONESIA 2018

Mobil adalah alat transportasi yang berguna untuk berpindah dari satu titik ke titik lain dengan membawa penumpang dan juga barang. Kebutuhan mobil yang meningkat membuat perusahaan industri mobil berlomba – lomba dalam menciptakan mobil dengan berbagai jenis dan model karena konsumen memiliki prioritas yang berbeda – beda terhadap fasilitas terdapat dimobil. LCGC termasuk jenis mobil yang paling laku kedua disemester pertama 2018. Sebesar 21% di bawah segmen LMPV, namun terdapat penurunan penjualan mobil LCGC yang mengalami penurunan 6,13 persen pada Januari – September 2018. Tujuan dari penelitian ini adalah untuk mengetahui kombinasi atribut pada mobil LCGC yang menghasilkan customer value index tertinggi dan mengetahui atribut yang merupakan value driver mobil LCGC. Atribut pada penelitian ini adalah harga, fitur keselamatan, mesin (cc), perangkat elektronik, efisiensi bahan bakar dan tampilan yang dimiliki. Penelitian ini merupakan penelitian kuantitatif dan menggunakan analisis konjoin, pengambilan sampel menggunakan nonprobability sampling. Jumlah responden pada penelitian ini adalah sebanyak 388 responden yang menggunakan mobil LCGC. Customer value index tertinggi merupakan kombinasi atribut dari fitur keselamatan yang tinggi, kapasitas mesin (cc) tinggi, efisiensi bahan bakar yang tinggi serta memiliki tampilan yang menarik dengan trade off mobil LCGC tersebut memiliki harga yang tinggi dan perangkat elektronik yang rendah, dengan Value driver adalah efisiensi bahan bakar yang tinggi. Saran dari penelitian ini bagi produsen mobil LCGC dapat menjadikan efisiensi bahan bakar sebagai fokus utama dalam pengembangan produk, dimana konsumen menginginkan mobil LCGC yang irit. Kata kunci: Customer Value Index, Konjoin, Mobil LCG

Analisis Customer Value Index dalam Memilih Mobil Hatchback di Indonesia

Mobil merupakan salah satu jenis kendaraan yang memudahkan akomodasi manusia maupun barang untuk berpindah dari tempat satu ke tempat yang lain. Dari masa ke masa, permintaan mobil semakin meningkat yang menyebabkan para produsen mobil berlomba-lomba untuk menciptakan mobil dengan berbagai jenis dan model. Salah satunya adalah mobil hatchback yang pada awal kedatangannya di Indonesia mendapat sambutan baik dan meraih puncak kejayaannya pada tahun 2012 dengan total penjualan mencapai 73.196 unit. Namun masa kejayaan tersebut berlangsung singkat, tercatat di tahun-tahun berikutnya yaitu mulai dari tahun 2013 sampai tahun 2018 penjualan mobil hatchback terus mengalami penurunan. Tujuan dari penelitian ini adalah untuk mengetahui kombinasi atribut dari mobil hatchback yang dapat menghasilkan nilai customer value index tertinggi dan dapat diketahui value driver mobil hatchback. Atribut pada penelitian ini yaitu tenaga mesin, tampilan, fitur keselamatan, efisiensi bahan bakar, perangkat elektronik, dan harga. Penelitian ini merupakan penelitian kuantitatif dan menggunakan analisis konjoin. Pengambilan sampel pada penelitian ini menggunakan teknik non probability sampling dengan jumlah responden sebanyak 402 orang yang pernah memiliki atau sedang memiliki mobil hatchback. Berdasarkan hasil pengolahan data, nilai customer value index tertinggi didapat pada kombinasi atribut tenaga mesin tinggi, tampilan yang menarik, efisiensi bahan bakar tinggi, dan harga yang rendah dengan trade off fitur keselamatan rendah dan perangkat elektronik yang rendah. Value driver pada penelitian ini adalah efisiensi bahan bakar. Saran dari penelitian ini untuk para produsen mobil hatchback agar menjadikan efisiensi bahan bakar menjadi fokus utama dalam mengembangkan atau menciptakan mobil hatchback, karena konsumen menginginkan mobil hatchback yang irit atau memiliki efisiensi bahan bakar yang tinggi. Kata kunci: Customer Value Index, Konjoin, Mobil Hatchback

Consumer Stated Preference for Acer Laptop from Online Reviews

Consumer preference is a hot topic in the domain of marking management and e-commerce. Many previous studies have been conducted in this field. Whereas, there are rarely studies building on the particular commodity such as laptop. Therefore, this study explores comprehensive features that affect consumer preference for laptops by mining the online reviews. Firstly, we collect 6531 online reviews for Acer laptop from Amazon.cn and code these reviews with Nvivo10. Secondly, we develop a feature-based consumer preference model named MCPL based on the review text analysis. Considering the data imbalance of the collected 6531 product reviews, we adopt a random cluster sampling method to extract 50 groups with 100 samples per group. Then the correspondent regression analyses are conducted for the 50 groups of reviews. Finally, the meta-analysis is creatively conducted to integrate the multiple liner regression results of different groups. According to the result of meta-analysis, we demonstrate dominant features on behalf of the consumer preference of laptop and draw practical implications for enterprise competition strategies to facilitate product design or improvement

Interaction of consumer preferences and climate policies in the global transition to low-carbon vehicles

Burgeoning demands for mobility and private vehicle ownership undermine global efforts to reduce energy-related greenhouse gas emissions. Advanced vehicles powered by low-carbon sources of electricity or hydrogen offer an alternative to conventional fossil-fuelled technologies. Yet, despite ambitious pledges and investments by governments and automakers, it is by no means clear that these vehicles will ultimately reach mass-market consumers. Here, we develop state-of-the-art representations of consumer preferences in multiple, global energy- economy models, specifically focusing on the non-financial preferences of individuals. We employ these enhanced model formulations to analyse the potential for a low-carbon vehicle revolution up to mid-century. Our analysis shows that a diverse set of measures targeting vehicle buyers is necessary for driving widespread adoption of clean technologies. Carbon pricing alone is insufficient for bringing low-carbon vehicles to mass market, though it can certainly play a supporting role in ensuring a decarbonised energy supply

ATRIBUTOS DETERMINANTES EN EL DISEÑO DE UN PROGRAMA DE CUARTO NIVEL: APLICACIÓN DEL ANÁLISIS DE CONJUNTO

En este trabajo se aborda la definición de atributos y el establecimiento de su importancia en el criterio de decisión de compra de los clientes, constituyen uno de los pasos fundamentales para el diseño de cualquier producto. Lo anterior constituye una problemática a la que se enfrenta un grupo amplio de profesionales de diversas áreas de conocimiento en Santo Domingo de los Tsáchilas. En  este  artículo  se  muestra  la utilidad y forma del análisis de conjunto como herramienta estadística para definir los atributos y la importancia  que  los  clientes  les  conceden  en  el  momento  de  tomar  una decisión. El resultado de la aplicación consistió en definir los atributos considerados por los clientes potenciales en la selección de un programa de formación de cuarto nivel en correspondencia con los cuales se realizará su diseño. PALABRAS CLAVE: Análisis de conjunto; Atributos; Diseño de producto. ABSTRACT This work is related with the definition of attributes and the establishment of its importance criteria in the customer decisions are one of the key steps in the design of any product. This is a problem faces by a large group of professionals from various fields of knowledge in Santo Domingo. In this paper shows the utility of Cluster Analysis as statistical tool to define the attributes and the importance that customers give them the time to make a decision. In this case, the application used to define the attributes considered in designing a training program for fourth level. KEYWORDS: Cluster Analysis; Attribute; Product design

Assessment of Social Preference in Automotive Market using Generalized Multinomial Logistic Regression

Individual auto market share is always one of the major concerns of any auto manufacturing company. It indicates a lot of things about the company such as profitability, competitiveness, short term and long term development and so on. The focus of this paper is to construct a quantitative model that can precisely formulate the social welfare function of the auto market by relating the auto market share with the utilities of the significant vehicle-purchasing criteria (e.g. reliability, safety, etc.) that concern vehicle buyers. Social welfare function is defined as the additive form of the utility of each criterion considered, it’s a good estimation of the customer preferences. The assessment methods used in this research include random utility theory and B-spline fitted logistic regression model. G-test is applied to select the criteria that is significant to the vehicle market social welfare, pseudo R-squareds are used as the model goodness-of-fit measures and Kendall rank correlation coefficient and Matthews correlation coefficient are applied to validate the assessment model. A case study using the U.S. auto market and vehicles related data collected in years of 2013 and 2014 are conducted to illustrate the assessment process of the social welfare function, and the data from 2015 are used to validate the assessment model.Master of Science in EngineeringIndustrial and Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136069/1/Assessment of Social Preference in Automotive Market using Generalized Multinomial Logistic Regression.pdfDescription of Assessment of Social Preference in Automotive Market using Generalized Multinomial Logistic Regression.pdf : Master of Science in Engineering Thesi

- Case of next-generation transportation market -

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 2020. 8. 이종수.The present dissertation aims to provide insights into the application of different artificial neural network models in the analysis of consumer choice regarding next-generation transportation services (NGT). It categorizes consumers decisions regarding the adoption of new services according to Deweys buyer decision process and then analyzes these decisions using a variety of different methods. In particular, various artificial neural network (ANN) models are applied to predict consumers intentions. Also, the dissertation proposes an attention-based ANN model that identifies the key features that affect consumers choices. Consumers preferences for different types of NGT services are analyzed using a hierarchical Bayesian model. The analyzed consumer preferences are utilized to forecast demand for NGT services, evaluate government policies within the transportation market, and provide evidence regarding the social conflicts among traditional and new transportation services. The dissertation uses the Multiple Discrete-Continuous Extreme Value (MDCEV) model to analyze consumers decisions regarding the use of different transportation modes. It also utilizes this MDCEV model analysis to estimate the effect of NGT services on consumers travel mode selection behavior and the environmental effects of the transportation sector. Finally, the findings of the dissertations analyses are combined to generate marketing and policy insights that will promote NGT services in Korea.본 연구는 기계학습 기반의 인공지능망과 기존의 통계적 마케팅 선택모형을 통합적으로 활용하여 제품 및 서비스 수용 이론으로 정의된 소비자들의 제품 수용 행위를 분석하였다. 기존의 제품 수용 이론들은 소비자들의 선택에 끼치는 영향을 단계별로 정의하였지만, 대부분의 이론은 제품 특성이 소비자 선택에 미치는 영향을 분석하기 보다는 소비자들의 의향, 제품의 대한 의견, 지각 수준과 소비자 선택의 관계 분석에 집중하였다. 따라서 본 연구는 소비자의 제품 수용 의향, 대안 평가 그리고 제품 및 사용량 선택을 포함하여 더욱 포괄적인 측면에서 소비자 제품 수용 행위를 분석하였다. 본 연구에서는 소비자의 제품 수용 관련 선택을 총 세 단계로 분류하였다. 첫 번째는 소비자의 제품 사용 의향을 결정하는 단계, 두 번째는 제품들의 대안을 평가하는 단계, 세 번째는 제품의 사용량을 선택하는 단계로, 각 단계를 분석하기 위해서 본 연구는 인공지능망과 통계적 마케팅 선택모형을 활용하였다. 인공지능망은 예측과 분류하는 작업에서 월등한 성능을 가진 모형으로 소비자들의 제품 수용 의향을 예측하고, 의향 선택에 영향을 주는 주요 변수들을 식별하는 데 활용되었다. 본 연구에서 제안한 주요 변수 식별을 위한 인공지능망은 기존의 변수 선택 기법 보다 모형 추정 적합도 측면에서 높은 성능을 보였다. 본 모형은 향후 빅데이터와 같이 많은 양의 소비자 관련 데이터를 처리하는데 활용될 가능성이 클 뿐만 아니라, 기존의 설문 설계 기법을 개선하는데 용이한 방법론으로 판단된다. 소비자 선호를 기반으로 한 대안 평가 및 사용량을 분석하기 위해서 통계적 선택 모형 중 계층적 베이지안 모형과 혼합 MDCEV 모형을 활용하였다. 계층적 베이지안 모형은개별적인 소비자 선호를 추정할 수 있는 장점이 있고, 혼합 MDCEV 모형의 경우 소비자들의 선호를 기반하여 선택된 대안들로 다양한 포트폴리오를 구성할 수 있고, 각 대안에 대한 사용량을 분석할 수 있다. 제안된 모형들의 실증 연구를 위해 차세대 자동차 수송 서비스에 대한 소비자들의 사용 의향, 서비스 대안에 대한 선호, 수송 서비스별 사용량을 분석하였다. 실증 연구에서는 차세대 자동차 수송 서비스를 수용하기까지 소비자들이 경험하는 단계별 선택 상황을 반영하였으며, 각 단계에서 도출된 결과를 통해 향후 차세대 자동차 수송 서비스의 성장 가능성과 소비자들의 이동 행위 변화에 대해 예측하였다. 본 연구를 통해 인공지능망이 소비자 관련 연구에서 유용하게 활용될 수 있음을 보였으며, 인공지능망과 통계적 마케팅 선택모형이 결합될 경우 소비자들의 제품 선택 행위뿐만 아니라, 제품 선택 의사결정 과정 전반에 걸쳐 소비자 선호를 포괄적으로 분석할 수 있음을 확인하였다.Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Research Objective 7 1.3 Research Outline 12 Chapter 2. Literature Review 14 2.1 Product and Technology Diffusion Theory 14 2.1.1. Extension of Adoption Models 19 2.2 Artificial Neural Network 22 2.2.1 General Component of the Artificial Neural Network 22 2.2.2 Activation Functions of Artificial Neural Network 26 2.3 Modeling Consumer Choice: Discrete Choice Model 32 2.3.1 Multinomial Logit Model 32 2.3.2 Mixed Logit Model 34 2.3.3 Latent Class Model 37 2.4 Modeling Consumer Heuristics in Discrete Choice Model 39 2.4.1 Consumer Decision Rule in Discrete Choice Model: Compensatory and Non-Compensatory Models 39 2.4.2 Choice Set Formation Behaviors: Semi-Compensatory Models 42 2.4.3 Modeling Consumer Usage: MDCEV Model 50 2.5 Difference between Artificial Neural Network and Choice Modeling 53 2.6 Limitations of Previous Studies and Research Motivation 58 Chapter 3. Methodology 63 3.1 Artificial Neural Network Models for Prediction 63 3.1.1 Multiple Perceptron Model 63 3.1.2 Convolutional Neural Network 69 3.1.3 Bayesian Neural Network 72 3.2 Feature Identification Model through Attention 77 3.3 Hierarchical Bayesian Model 83 3.4 Multiple Discrete-Continuous Extreme Value Model 86 Chapter 4. Empirical Analysis: Consumer Preference and Selection of Transportation Mode 98 4.1 Empirical Analysis Framework 98 4.2 Data 101 4.2.1 Overview of the Survey 101 4.3 Empirical Study I: Consumer Intention to New Type of Transportation 110 4.3.1 Research Motivation and Goal 110 4.3.2 Data and Model Setup 114 4.3.3 Result and Discussion 123 4.4 Empirical Study II: Consumer Choice and Preference for New Types of Transportation 142 4.4.1 Research Motivation and Goal 142 4.4.2 Data and Model Setup 144 4.4.3 Result and Discussion 149 4.5 Empirical Study III: Impact of New Transportation Mode on Consumers Travel Behavior 163 4.5.1 Research Motivation and Goal 163 4.5.2 Data and Model Setup 164 4.5.3 Result and Discussion 166 Chapter 5. Discussion 182 Bibliography 187 Appendix: Survey used in the analysis 209 Abstract (Korean) 241Docto