A Scientometric Analysis of Publications in theJournal of Business-to-BusinessMarketing1993–2014

Abstract

Purpose: To conduct a scientometric analysis of the contents of the Journal of Business-to-Business Marketing from 1993 to 2014.Methodology/approach: The authors use the Leximancer computer-aided text analysis program, which reliably and reproducibly identifies the main concepts embedded in the text-their frequency and patterns of co-occurrence-based on the ways words move together in the text. They also identify key concepts that differentiate among the networks of concepts occurring in each of the first four five-year periods of the Journal's history.Findings: There are four common underlying conceptual themes: relationships, market, study, and business. But the focal mix of concepts has changed over time, from a narrower initial focus on distribution and power and conflict, to a greater focus on firm business marketing strategy and pedagogy, to a focus on networks, the Internet and more collaborative relations, to a focus, in the most recent period, on psycho-social network concepts, such as trust and commitment.Research implications: The results complement and provide a baseline for evaluating and comparing researcher-conducted literature reviews of business marketing and JBBM 's contributions. The methodology provides possibilities for further analysis as it can be extended to analyze the business marketing and wider marketing literature more generally and how it has evolved.Practical implications: Leximancer provides a comprehensive, accessible summary of the content of the JBBM and demonstrates its value as a text analysis program.Originality/value/contribution: The authors are among the first to use this novel computer text analysis program to conduct a scientometric analyses of journal content. It provides a template for conducting studies of other journals and text.

Sustainable Supply Chain Based on News Articles and Sustainability Reports: Text Mining with Leximancer and DICTION

“…Therefore, this must be adjusted before comparing the firms based on the categories (region, listed, MNE). To control this, a prominence index is calculated and applied [131,140].…”

“…If there is no correlation between the two, then the expected probability of their co-occurrence is the product of the probability of the concept occurring in the text and the probability of the text occurring in the category. In this case the Prominence Index (PI) is 1" [131,140].…”

“…This is because algorithm-based content analyses (quantitative analyses by computer based on frequency and co-occurrrence algorithms) enable more subtle and in-depth analyses than an average person can achive by analyzing qualitatively [130]. This method is economical (i.e., can analyze large quantity of data), systematic, and reliable [131]. There are two types of content analysis where the computer counts the categories.…”

“…Leximancer generates the most frequently occurring concepts from the news text [131], as well as themes as sets of concepts. Leximancer generated 61 concepts and 8 themes from the 792 news articles.…”

25+ Years of Business Intelligence and Analytics Minitrack at HICSS: A Text Mining Analysis

“…Leximancer is also used to map histories of research publications within a single journal or a conference, as intended by our research. Examples include the 40-year history of themes and concepts published by the Journal of Cross-Cultural Psychology [10], the 10-year history of the Sports Management International Journal [2] or the 22-year history of the Journal of Business-to-Business Marketing [39]. However, to the best of our knowledge, this type of content analysis has not been used to in the field of BI&A and big data, to analyze histories of related conference (mini)tracks or journals.…”