Estudo sobre os componentes do modelo de negócios: uma pesquisa bibliométrica de 2009 a 2014

This article aims to identify the main aspects discussed by scholars regarding business model components. The methodology used was a bibliometric study, based on the Law of Zipf, whose keywords form “Business model” and “components”. Data were collected from the Capes scientific journals database from 2009 to 2014 and from the Web of Science database. The extracted articles underwent descriptive analysis and the data extracted from the Web of Science were processed through Bibexcel Software, in which it was possible to generate a citation matrix that was managed by the Ucinet64 software, generating a list of authors by importance according to the number of citations and their respective relations of co-citations. The results allowed to identify the main research streams about business model components addressed by the scholars, highlighting value proposition, clients, infrastructure, resources, revenue model and costs.

The Power-weakness Ratios (PWR) as a Journal Indicator: Testing the “Tournaments” Metaphor in Citation Impact Studies

PurposeRamanujacharyulu developed the Power-weakness Ratio (PWR) for scoring tournaments. The PWR algorithm has been advocated (and used) for measuring the impact of journals. We show how such a newly proposed indicator can empirically be tested.Design/methodology/approachPWR values can be found by recursively multiplying the citation matrix by itself until convergence is reached in both the cited and citing dimensions; the quotient of these two values is defined as PWR. We study the effectiveness of PWR using journal ecosystems drawn from the Library and Information Science (LIS) set of the Web of Science (83 journals) as an example. Pajek is used to compute PWRs for the full set, and Excel for the computation in the case of the two smaller sub-graphs: (1) JASIST+ the seven journals that cite JASIST more than 100 times in 2012; and (2) MIS Quart+ the nine journals citing this journal to the same extent.FindingsA test using the set of 83 journals converged, but did not provide interpretable results. Further decomposition of this set into homogeneous sub-graphs shows that—like most other journal indicators—PWR can perhaps be used within homogeneous sets, but not across citation communities. We conclude that PWR does not work as a journal impact indicator; journal impact, for example, is not a tournament.Research limitationsJournals that are not represented on the “citing” dimension of the matrix-for example, because they no longer appear, but are still registered as “cited” (e.g. ARIST)-distort the PWR ranking because of zeros or very low values in the denominator.Practical implicationsThe association of “cited” with “power” and “citing” with “weakness” can be considered as a metaphor. In our opinion, referencing is an actor category and can be studied in terms of behavior, whereas “citedness” is a property of a document with an expected dynamics very different from that of “citing.” From this perspective, the PWR model is not valid as a journal indicator.Originality/valueArguments for using PWR are: (1) its symmetrical handling of the rows and columns in the asymmetrical citation matrix, (2) its recursive algorithm, and (3) its mathematical elegance. In this study, PWR is discussed and critically assessed.

Diagonal Values in ACA

Diagonal values in the cocitation frequency counts matrix are a fundamental issue in ACA study. Diagonal values are the co-citation frequency counts between the author himself/herself excluding self-citation. Retrieving exact values of diagonal values in the co-citation matrix requires a manual and time consuming procedure. For that reasons, ACA researchers suggested many different approaches to create, not retrieving the real values, the diagonal cells in the cocitation matrix. They include the mean cocitation count, missing values, zeroes, highest off-diagonal counts, adjusted off-diagonal values, and the number of times cocited with himself/herself. The majority of ACA researchers seem to prefer to use either the adjusted value approach by adding three highest off-diagonal values and divided by two or the missing value approach. This chapter empirically examines the impact of these different approaches on the ACA outcomes. Based on the results of this study, if the pure cocitation counts are not used, the next best alternatives are as follows. They are the missing value approaches, mean cocitation value approach, and the highest off-diagonal value approaches in the order of the highest total variance explained.