A keyword-based organizing framework for ERP intellectual contributions

Abstract

Purpose – The purpose of this paper is to develop a keyword-based organizing framework for enterprise resource planning (ERP) intellectual contributions, and use the proposed framework to derive major areas of emphasis in ERP research. Design/methodology/approach – Using keyword persistence, a time-based measure, and keyword dominance, a frequency-based measure, the authors constructed an empirical framework to classify ERP intellectual contributions. Findings – Four major ERP themes were identified using the proposed framework; namely, emerging/phantom, intermittent, trendy, and core. Further examination of the keywords in each of these themes revealed that whereas the business aspects of ERP systems are discussed in an intermittent manner, its technical aspects are discussed in a trendy fashion. Research limitations/implications – The main limitation of this research is that it only used keywords from papers published in scholarly journals, and did not include conference proceedings, books, and other outlets. In this paper, the authors developed an objective keyword-based organizing framework. The proposed framework is simple, flexible, and is based on dimensions that are research domain independent, and thus can be used as an organizing framework for any research domain. Practical implications – The proposed framework may help managers to better focus consulting engagements on ERP implementations through identifying areas that are not well covered by researchers to ensure that such areas they are well covered by consulting services. Originality/value – The main contribution of this paper is the development of an objective keyword-based framework that can be used as an organizing tool for intellectual contributions. The framework can be used to derive insights in the macro characterizations of the research in a given domain, and is thus valuable to researchers – both academic and practitioner. To the best of the authors’ knowledge, this is the first such keyword-based research organizing framework.

Augmented reality technology in the manufacturing industry: A review of the last decade

“…The top-left quarter includes the keywords which first appeared more than 5.5 years ago, but have not been used frequently since then. These keywords identify the "intermittent concepts," i.e., concepts that are discussed in the literature in an on-off manner (Fadlalla and Amani, 2015). These are research topics that either the researchers have not agreed on yet, or are relatively frequently tackling changing topics.…”

“…Review papers and conceptual papers, which are fewer in number, are examined almost individually, by providing an overview of the main topics treated. As application papers and technical papers are more numerous, the framework for their analysis is grounded on the classification of the study keywords as way to capture the essential topics covered, according to Fadlalla and Amani (2015). These authors suggested evaluating two main parameters, namely the frequency of use of the keywords and their persistence.…”

Available Pathways for Operationalizing Circular Economy into the Olive Oil Supply Chain: Mapping Evidence from a Scoping Literature Review

“…In doing so the most considered and explored pathways among scholars are ordered, which makes evidence of their degree of establishment within the emerging theoretical framework. Borrowing the two dimensions applied by [31] for keyword-based analysis, the dominance and persistence of the recognized pathways can be outlined. The former is measured in terms of frequency (namely, the number of articles that explored a given pathway), and the latter in terms of continuity over time (that is, the number of years covered by afferent publications).…”

The Role of ICT in Supporting Spent Coffee Grounds Collection and Valorization: A Quantitative Assessment

“…A total of 386 keywords was identified, 79% of which occurs just once. By excluding, for obvious reasons, the nouns "spent coffee ground", "coffee ground", and "coffee", which could distort results as they were used to run the original queries, we identified the frequency for each keyword, i.e., the total number of times it occurs, and the persistency, i.e., the number of years of presence in literature, computed by considering the first and the last appearance, in order to evaluate trends, according to suggestions of Fadlalla and Amani [27]. The resulting graph (shown in Figure 3) is included, to make the graph more effective.…”