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Grading business journals: A comparative analysis of ABS, ABDC and JCR

quartiles and proposing an algorithm based classification

Tenghao Zhang

School of Business and Law, Edith Cowan University, Joondalup, Australia.

Abstract

There are multiple journal rankings in measuring academic journals in business

research. Among them, ABS (AJG), ABDC and JCR quartiles are extensively used in

business schools across the globe. Which is better in grading business journals based

upon their academic performance? In this study, we used the Principal Component

Analysis (PCA) and Technique for Order of Preference by Similarity to Ideal Solution

(TOPSIS) and evaluated 103 business journals based on six indicators. Then we

proposed a grading simulation approach to simulate the original grades based on the

TOPSIS scores and the results suggest that the JCR quartile is the closest to our

simulation, while ABDC and ABS ranked second and third respectively. Lastly,

drawing on the K-means clustering algorithm, we grouped the journals into four ordinal

classes based on ABS, ABDC, JCR quartile and TOPSIS scores.

Keywords Journal rankings · Business journals · TOPSIS · K-means

clustering· JCR · ABDC · ABS

Introduction

Journal grading has been used for the evaluation of research performance within and

across institutions in many countries (Hirschberg & Lye, 2020). Business journals

comprise a significant portion of social science journals in academia, and there are

multiple business journal rankings proposed by different institutions. In this paper, we

are focusing on three commonly referenced business journal rankings or classifications,

which are: the UK’s Chartered Association of Business Schools’ Journal Guide (ABS),

ABS sometimes is also referred to as “AJG” since its title has changed to “Academic Journal

Guide by Chartered ABS” in 2018. But this paper will stick with the term ABS in order to avoid

confusion.

Contact: Tenghao Zhang ORCID: http://orcid.org/0000-0002-6686-6777

Email: tenghaoz@our.ecu.edu.au

Electronic copy available at: https://ssrn.com/abstract=3826191

Australian Business Deans Council’s List (ABDC) and Journal Citation Reports’ (JCR)

journal quartiles released by Clarivate Analytics. These rankings are not exclusively for

business journals, for example, ABS and ABDC also cover many economic and

psychological journals, while JCR includes journals across almost all disciplines.

Nevertheless, ABS and ABDC are both business-focused and proposed by business

research organizations, while JCR provides a “business” category among its indexed

journals, such that all three assessments can serve as important criteria in gauging the

research output of business journals.

The three rankings, however, taking very distinct approaches in assessing business

journals. ABS employs a combination of expert panels and objective data

measurements from various metrics such as JCR and SCImago Journal Rank (Krueger,

2017). ABDC’s methodology is predominantly subjective which is validated by panels

of experts (Moosa, 2016). The JCR quartiles, on the other hand, is solely based on every

year’s Journal Impact Factors (JIF) published by Clarivate Analytics. Journals in the

same research field are partitioned into four equal groups in which Q1 refers to journals

with the highest 25% of JIFs of the previous year. In a nutshell, the three rankings

represent three conventional techniques in academic journal assessment: subjective

(ABDC), objective (JCR) and mixture (ABS). While each ranking is not without its

critics (Hussain, 2015; Lariviere & Sugimoto, 2019; Moosa, 2016; Zhang, 2021), in

this study, we aim to quantitatively evaluate and compare the three rankings using

uniform standards. Moreover, based on the evaluation results, we endeavour to propose

an algorithm-based business journal classification, which can serve as an alternative

reference for business journal grading.

Data

First, journal selection. To facilitate data analysis, we stipulated the candidate journals

to be included in the latest versions of ABS, ABDC and JCR’s “business” category

concurrently. This has resulted in 126 journals that appeared in ABS (2018), ABDC

(2019) and JCR (2020). Moreover, to exclude some emerging/recently-included

journals that may not have been paid justified attention yet, we required the journals to

have a five-year impact factor in the JCR and the final sample is 103 journals.

Second, indicator selection. To ensure indicators across journals come from a

singular and reliable source, the source must cover all the 103 journals. After

comparisons, we decided to use part of the “key indicators” provided by JCR. There

Electronic copy available at: https://ssrn.com/abstract=3826191

are 13 key indicators listed on Web of Science and they fall under “impact metrics”,

“influence metrics” and “source metrics” classifications (Leydesdorff, 2006). To be

noted that JCR quartiles are based solely on JIFs, and most other key indicators did not

show significant multicollinearity issues with JIF, such that the results are supposed to

be different from the JCR quartiles. Among the 13 indicators, we excluded those not

directly related to journals’ academic performance, such as citable items, percentage of

articles in citable items, cited and citing half-life (Magri & Solari, 1996). We also

excluded indicators that exhibit a high level of multicollinearity with others: total cites,

Eigenfactor score and average JIF percentile. Accordingly, six indicators were retained:

JIF, 5-year impact factor (IF5), impact factor without journal self-cites (IFNS),

immediacy index (IMI), article influence score (AIS) and normalized Eigenfactor

(NEF). Eigenfactor (EF) is a score that reflects the total importance of a journal, it is

based on articles published in the previous five years and cited in the JCR year, but

citations from highly ranked journals are adjusted to a greater weight (Bergstrom et al.,

2008). In addition, to reduce the self-citation bias (Heneberg, 2016; Van Noorden &

Chawla, 2019), we propose an other-cited rate (OCR) by dividing IFNS by JIF, and

thus IFNS was replaced by OCR. Note that although JIF and IF5 exhibited a moderate

but acceptable level of multicollinearity (VIF= 4.09 < 10), we decided to keep them

both as they measure different aspects of journal influences. A description of the six

indicators is presented in Table 1.

Methodology and data analysis

Determine the weights of indicators

In this study, we employ the Principal Component Analysis (PCA) method to determine

indicators’ respective weights. PCA has been applied in various studies to determine

attribute weights (Adler & Golany, 2002; Delchambre, 2015; Liao, 2006; Zhang, 2020).

Although details of methodologies vary, the main steps of applying PCA are to

calculate indicators’ coefficients in the linear combination of principal components

(PC), and then weighted-mean each indicator’s coefficients (normally there will be

more than one PC) and normalise it to obtain the weights. The following shows the

concise steps of weight calculation.

1. Run the PCA test in SPSS (KMO = 0.79, p < 0.001) and obtained two PCs: Z

, which cumulatively explained 80.2% of the total variance.

Electronic copy available at: https://ssrn.com/abstract=3826191

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