Essays on big data analytics in industrial research

Abstract

This thesis is a dissertation on empirical research in policy and industry leadership analysis based on big data analytics. The magnitude of available data is expanding because of advancements in information and communication technology. In this big data environment, conversion of data into knowledge is important for devising company strategies and government policies, and big data analytics methodologies can be effectively used in various fields, especially social science. This paper is focused on achieving policy and industry leadership analysis through big data analytics. This thesis is composed of two essays. The first is a study of energy and environment policies using big data methodology. In particular, this research is a study for measuring pro/anti-environmental attitudes utilizing Google search queries for each US state, and then we investigate the effects of the environmental attitudes of local people on green electricity policies. The second essay is a study on industrial leadership analysis through patent wars in the information and communication industry. This research investigates industrial leadership behind patent wars through dyadic relationships analysis, such as competitive and co-operative relationships. A summary of the first essay is as follows: Despite the rising influence of public opinion on government energy policy formulation and implementation, the roles of pro and/or anti-environmental attitudes among residents have not been empirically examined. To quantify time-varying environmental attitudes among local residents, we exploit geo-specific Google search-query data derived from Internet-based “big data” and verify through ordinary least squares regression outcomes regarding environmental behavior. For the purpose of drawing policy implications, we revisit decisions by state governments of the United States to adopt three well-known green electricity policies: renewable energy portfolio, net metering rules, and public benefit funds. As some states have not yet adopted some (or any) of these policies, unlike previous studies, we handle the issue by examining right-censored data and applying a duration-based econometric method called the accelerated failure time model. We found state residents’ environmental attitudes to have statistically significant roles, after controlling for other traditional time-varying policy adoption factors. Interestingly, the extent to which anti-environmental attitudes affect a state’s policy adoption differs across green energy policies, and knowing this can help a local government formulate better-tailored environmental policy. In particular, researchers can use our method of incorporating citizens’ environmental attitudes to discuss relevant issues in the field of energy policy. A summary of the second essay is as follows: As the fundamentals of the global economy shift from manufacturing to knowledge-based industries, intangible assets such as patents become increasingly imperative. Growing importance of patents has induced firms to acquire more of them, which has eventually led to a dramatic increase in patent infringement litigation. Despite the increasing interest in patent infringement litigation, extant research has primarily focused on the issues regarding patents and litigation themselves, not on investigation of the interfirm relationships direct and indirect in the patent war. In order to overcome this research gap, we suggest a new measure called a Structural Relationship Balance Score (SBRS) calculated from the information of interfirm patent infringement litigation. This research examines 143 cases of the patent infringement litigation filed between 2004 and 2014 in the smartphone industry. The results show that our proposed measure can identify the interfirm relationships behind patent war, especially cooperative relationships. Furthermore, we apply Quadratic Assignment Procedure (QAP) analysis on structural balance relationship score to identify the factors of interfirm relationships, which can be applied to all industries where patent litigation is prevalent.