PMID- 37153010
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230509
IS  - 2376-5992 (Electronic)
IS  - 2376-5992 (Linking)
VI  - 9
DP  - 2023
TI  - Proposal for an objective binary benchmarking framework that validates each other 
      for comparing MCDM methods through data analytics.
PG  - e1350
LID - 10.7717/peerj-cs.1350 [doi]
LID - e1350
AB  - When it comes to choosing the best option among multiple alternatives with 
      criteria of different importance, it makes sense to use multi criteria decision 
      making (MCDM) methods with more than 200 variations. However, because the 
      algorithms of MCDM methods are different, they do not always produce the same 
      best option or the same hierarchical ranking. At this point, it is important how 
      and according to which MCDM methods will be compared, and the lack of an 
      objective evaluation framework still continues. The mathematical robustness of 
      the computational procedures, which are the inputs of MCDM methods, is of course 
      important. But their output dimensions, such as their capacity to generate 
      well-established real-life relationships and rank reversal (RR) performance, must 
      also be taken into account. In this study, we propose for the first time two 
      criteria that confirm each other. For this purpose, the financial performance 
      (FP) of 140 listed manufacturing companies was calculated using nine different 
      MCDM methods integrated with step-wise weight assessment ratio analysis (SWARA). 
      In the next stage, the statistical relationship between the MCDM-based FP final 
      results and the simultaneous stock returns of the same companies in the stock 
      market was compared. Finally, for the first time, the RR performance of MCDM 
      methods was revealed with a statistical procedure proposed in this study. 
      According to the findings obtained entirely through data analytics, Faire Un 
      Choix Adequat (FUCA) and (which is a fairly new method) the compromise ranking of 
      alternatives from distance to ideal solution (CRADIS) were determined as the most 
      appropriate methods by the joint agreement of both criteria.
CI  - (c) 2023 Baydas et al.
FAU - Baydas, Mahmut
AU  - Baydas M
AD  - Faculty of Applied Sciences, Necmettin Erbakan University, Konya, Turkey.
FAU - Eren, Tevfik
AU  - Eren T
AD  - Faculty of Applied Sciences, Necmettin Erbakan University, Konya, Turkey.
FAU - Stevic, Zeljko
AU  - Stevic Z
AUID- ORCID: 0000-0003-4452-5768
AD  - Faculty of Transport and Traffic Engineering, University of East Sarajevo, Doboj, 
      Bosnia and Herzegovina.
FAU - Starcevic, Vitomir
AU  - Starcevic V
AD  - Faculty of Business Economics, University of East Sarajevo, Bijeljina, Bosnia and 
      Herzegovina.
FAU - Parlakkaya, Raif
AU  - Parlakkaya R
AD  - Faculty of Political Sciences, Necmettin Erbakan University, Konya, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20230425
PL  - United States
TA  - PeerJ Comput Sci
JT  - PeerJ. Computer science
JID - 101660598
PMC - PMC10159627
OTO - NOTNLM
OT  - Data analytics
OT  - Financial performance
OT  - MCDM benchmarking and evaluation methodology
OT  - Rank reversal
COIS- Zeljko Stevic is an Academic Editor for Peerj. All other authors declare that 
      they have no competing interests.
EDAT- 2023/05/08 06:41
MHDA- 2023/05/08 06:42
PMCR- 2023/04/25
CRDT- 2023/05/08 04:10
PHST- 2023/01/26 00:00 [received]
PHST- 2023/03/28 00:00 [accepted]
PHST- 2023/05/08 06:42 [medline]
PHST- 2023/05/08 06:41 [pubmed]
PHST- 2023/05/08 04:10 [entrez]
PHST- 2023/04/25 00:00 [pmc-release]
AID - cs-1350 [pii]
AID - 10.7717/peerj-cs.1350 [doi]
PST - epublish
SO  - PeerJ Comput Sci. 2023 Apr 25;9:e1350. doi: 10.7717/peerj-cs.1350. eCollection 
      2023.