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Automobile industry and smart products: an Hedonic Price Model analysis

Luciano Vitolo

Automobile industry and smart products: an Hedonic Price Model analysis.

Rel. Paolo Neirotti. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management), 2022

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The automotive industry, in recent years, is facing a phenomenon of digital revolution. Since the advent of IT, digitalization has influenced various industries, modifying processes, products and business models related to them. The aim of this study is to investigate whether there is a positive correlation between the characteristics that make today’s car a digital and smart product and the value perceived by the consumer. The work is structured in five chapters. In the first one, a clear definition of digitization and analogous terms is given. After this, the unconventional changes that this form of transformation can deliver are observed with the aid of some examples. The focus shifts then to the automotive industry that, even though historically characterized by a physical product, is also facing a digital transformation driven mainly by market needs and new regulations. Then, the primary capabilities that make the automobile a smart and related product are listed with a brief description of the technologies that allow its implementation. Lastly, a number of the actions taken by leading groups in the sector and possible entrants are reported. Chapter 2 presents an assessment of the scientific literature associated with the Hedonic Pricing Model, specifically with reference to applications in the automotive industry. Fundamental hypotheses and main mathematical models used in literature are highlighted. After specifying the model, in the next chapter, the hypothesis object of this work and the relative models are presented. Also, information about the data gathered are provided, concerning both the collection methodology and about the variable encoding. Chapter 4 reviews the effects of the analyses accomplished to verify the hypotheses set out in the previous chapter. Descriptive statistics about the variables concerned are provided. Consequently, outlier identification process is described. Finally the regression analysis is performed and its results are shown, together with a robustness check and remarks about the regression coefficients obtained. Last chapter reports the final considerations about the study carried out. Also, the limits of the work and suggestion for future research are furnished.

Relators: Paolo Neirotti
Academic year: 2022/23
Publication type: Electronic
Number of Pages: 116
Corso di laurea: Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management)
Classe di laurea: New organization > Master science > LM-31 - MANAGEMENT ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/25846
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