Analysis of the global mobile phone industry from 2012 to 2018 (range J-R): the relationship between competitive intensity and product heterogeneity in the brands portfolio.

Abstract

This dissertation focuses on the mobile phone industry, from 2012 to 2018. The aim is to investigate how different levels of competitive intensity impact on firms’ decisions on the heterogeneity versus homogeneity of their product portfolio. The mobile phone industry is a dynamic reality characterized by continuous innovations. Since the introduction of smartphones, mobile phone vendors are introducing technologies from other product categories - i.e. camera - to distinguish themselves and gain a competitive advantage over the others. Therefore, starting from a database listing the main technological and aesthetic features for 1640 handsets from a sample of 30 different vendors from J to R, an annual database has been developed selecting seven characteristics related to either the aesthetic or the technical performance of the handsets. Such features are volume and weight – aesthetic features - battery talk-time and standby, number of GSM frequencies, camera pixel and degree of technological convergence - identifies the number of technologies in a handset from other product categories. The standard deviation of the considered variables and the competitive intensity - reported both as cumulative market share of the four major global vendors and as the number of operating companies - have been calculated per year. Subsequently, statistical analysis has been run out. Correlation highlights the existence of one strong positive linear relationship between volume and weight standard deviation, meaning that the values of the former variable increase as the values of the latter increase. The linear regression model – whose aim is to observe how competitive intensity (independent variable) impacts on the variation of the dependent variables (standard deviation of the elements in the dataset) - shows that for each variable the angular coefficient is negative, thus as competitive intensity increases, heterogeneity of the analyzed variables decreases. Furthermore, the quadratic regression models - to investigate whether exists a curvilinear relationship among the variables – demonstrate that the standard deviation of volume, weight standby battery and GSM frequencies follow a curvilinear distribution resulting in a U-shaped relationship between the mentioned variables and competitive intensity, meaning that as competitive intensity increases, the heterogeneity of such variables decreases, but for high degrees of competitive intensity, this relationship is reversed, highlighting product differentiation. Both in linear and quadratic regression, the competitive intensity variable is considered with one-year lag, because decisions on new products introduction in high-tech industries necessitate of approximately one year. To sum up, in such dynamic environment, this thesis aims to understand how firms react to increasing levels of competitive intensity, trying to investigate whether they simplify their product line, focusing their resources on standard models, rather than keeping products heterogeneous in order to attract and serve different consumer segments. The statistical analysis underlines that the two groups of variables have two distinguished behavior in relationship with the degree of competitive intensity. On the one hand, variables describing aesthetic characteristics tend to increase in heterogeneity as competitiveness increases, but for high levels of the independent variable the relationship is reversed. On the other hand, there is a negative relationship between competitive intensity and differentiation in variables related to technological performance.