2026-09-18T19:05:07Zhttps://iris.unica.it/oai/request

oai:iris.unica.it:11584/2668212022-10-20T09:32:30Zcom_11584_207615com_11584_111066col_11584_265854

Technological cycles, Meta-Ranking and Open Access Performance ENNAS, GIANFRANCO analisi impatto riviste scientifiche cicli tecnologici dominant paradigm indici aggregati journal impact analysis journal quality meta-ranking paradigma dominante qualità riviste scientifiche technological cycles Settore SECS-P/10 - Organizzazione Aziendale This thesis consists of three essays, linked by innovation, classification and change. In the first paper, I analyze a theoretical problem regarding the reemergence and affirmation of a technological paradigm over the others; in the second article, I propose a framework to aggregate journal rankings and classify academic journals; in the third essay I analyze the performance of Open Access journals, considered an innovative form of publishing, with the aim of identifying the main features of top-rated ones. More specifically, the first essay deals on the technological life cycle which explains how the battles between competing technologies sooner or later end with the dominance of one over the others, or, under certain conditions, with their coexistence. However, the practice points out that, sometimes, beaten technologies can re-emerge in the market. Firms dealing with technology investment decisions need to completely understand the competing technologies dynamics, because the emergence of an alternative and potentially superior technology does not necessarily mean the failure of the incumbent, and different scenario would be traced. Starting from the analysis of the microprocessor market and considering the relationships with complementary companies, I show how the battle for dominance between two rival technologies can be reopened with a new era of ferment. While factors of dominance have been explored by a great amount of literature, little has been said on this question. In particular, I find a non-conventional S-curve trend and I seek to explicate its managerial implication.The second chapter deals with ranking academic journals, an issue that during the years received several contribute from literature of Business and Management [DuBois and Reeb, 2000, Franke et al, 1990, Serenko and Bontis, 2004, Tüselmann et al, 2015, Werner, 2002]. Ranking journals is a longstanding problem and can be addressed quantitatively, qualitatively or using a combination of both approaches. In the last decades, the Impact Factor (i.e., the most known quantitative approach) has been widely questioned, and other indices have thus been developed and become popular. Previous studies have reported strengths and weaknesses of each index, and devised meta-indices to rank journals in a certain field of study. However, the proposed meta-indices exhibit some intrinsic limitations: (i) the indices to be combined are not always chosen according to well-grounded principles; (ii) combination methods are usually unweighted; and (iii) some of the proposed meta-indices are parametric, which requires assuming a specific underlying data distribution. I propose a data-driven methodology that linearly combines an arbitrary number of indices to produce an aggregated ranking, using different learning techniques to estimate the combining weights. I am also able to measure correlations and distances between indices and meta-indices in a vector space, to quantitatively evaluate their differences. The goal of the third essay, is to identify the features of top-rated gold open access (OA) journals by testing seven main variables: languages, countries, years of activity and years in the DOAJ repository, publication fee, the field of study, whether the journal has been launched as OA or converted, and the type of publisher. A sample of 1,910 gold OA journals has been obtained by combining SCImago Journal & Country Rank (SJR) 2012, the DOAJ, and data provided by previous studies [Solomon, 2013]. I have divided the SJR index into quartiles for all journals' subject areas. First, I show descriptive statistics by combining quartiles based on their features. Then, after having converted the quartiles into a dummy variable, I test it as a dependent variable and in a binary logistic regression. This work contributes empirically to better understanding the gold OA efficacy, which may be helpful in improving journals' rankings in the areas where this is still a struggle. Significant results have been found for all variables, except for the types of publishers, and for born or converted journals. 2015-05-28 info:eu-repo/semantics/doctoralThesis http://hdl.handle.net/11584/266821 eng numberofpages:100 info:eu-repo/semantics/openAccess Università degli Studi di Cagliari