2.1. Hybrid MCDM Methods
In the literature, there are numerous combinations of the weighting and evaluation methods that form various hybrid methods to deal with uncertainty and vagueness in decision-making [
19]. The fuzzy set, rough set, grey set as well as probabilistic models, such as evidential reasoning, are used as the main approaches to account for uncertainties. Some of these hybrid methods combine the pairwise comparison methods and the classical weighted sum model (WSM) [
20].
Structuring a decision problem is an effective approach for tackling it. Dincer et al. [
21] analysed the performance results of the European policies in energy investment using the quality function deployment (QFD) [
22] and the balanced scorecard. Then, they assessed the customer and technical requirement using a hesitant fuzzy decision set combined with the decision-making trial and evaluation laboratory (DEMATEL) and the analytical hierarchical process (AHP) [
23]. Also, the hesitant fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) [
24] was incorporated in weighting the technical requirements of a new service/product development process. In the selection of air traffic protection aircraft, Petrović and Kankaraš [
25] applied DEMATEL to determine the evaluation criteria and AHP for weighting. Hu et al. [
26] applied the DEMATEL with the analytical network process (ANP) [
23] for weighting and ranking experts in evaluating the prediction power of experts using the GRA [
7]. Roy et al. [
27] developed a rough strength relational DEMATEL model to analyze the individual priorities for the key success factors of hospital service quality.
Pairwise comparison gives all criteria an equal opportunity for assessment, and there is some integration of the MCDM methods with the geographic information system (GIS). Shariat et al. [
28] applied the fuzzy extension of the AHP, WSM, and TOPSIS in analyzing the risk in an urban stormwater infrastructure system. The GIS was integrated to solve the spatial MCDM problem. Ali et al. [
29] applied the AHP to assess the suitability of a wind and solar farm based on GIS in Thailand. The AHP weight was directly used with the WSM. Arabameri [
30] combined the AHP and TOPSIS method as a hybrid MCDM method. This was further compared with bivariate and multivariate statistical analysis as well as data mining approaches in assessing water mapping based on the GIS. Sahu et al. [
31] developed the Taguchi SAW method to rank the cutting speed, feed, and depth of cut in a lathe machine. Then, the Taguchi TOPSIS method was employed to validate the rankings in their research.
Another application area of the hybrid MCDM methods is in efficient sustainable development. Zhang et al. [
32] selected the best renewable energy project by employing a linear programming model to determine the weights as an extended interacting and MCDM method. Aghdaie et al. [
33] applied the step-wise weight analysis ratio assessment (SWARA) [
34] and the complex proportional assessment (COPRAS) [
35] with GRA in market segmentation, where GRA integrated incomplete information in the model. Much later, this research was extended as fuzzy group decision-making [
36]. Wang et al. [
37] matched demand in reverse logistics in manufacturing as an MCDM problem. They applied a hybrid of the AHP and entropy weights combined with the grey multi attributive border approximation area comparison (MABAC) method [
38] to evaluate the collection mode. Kamari [
39] developed a soft systems methodologies (SSM) for assessing the barrier to sustainable renovation and employed the AHP and TOPSIS as a mixed method based on the SSM. Nie et al. [
40] presented the Pythagorean fuzzy partitioned normalized weighted Bonferroni mean (PFPNWBM) operator, which considers a Shapley fuzzy measure. They illustrated the use of this operator in the selection of green raw material.
The problem of an exponential increase of comparison in the AHP and ANP is addressed using the best and worst MCDM (BWM) methods [
41]. Badi and Ballem [
42] combined a rough set with BWM for weighting in the multi-attribute ideal-real comparative analysis of evaluating medical and pharmaceutical equipment. Similarly, to mitigate this problem, Pamučar et al. [
43] developed a full consistency method (FUCOM) for approximating the weight of criteria in the MCDM model. The FUCOM is a subjective weighting method that determines the deviation from full consistency in the comparison. The FUCOM algorithm pairwise compares the criteria and only needs
n-1 comparisons, which is less than the number of comparisons needed by the AHP [
23] and BWM method. Also, Pamucar et al. [
44] extended the FUCOM method to multi attributive ideal-real comparative analysis and was applied in evaluating cross-leveling in railway infrastructure. Then, Nunić [
45] applied the FUCOM method in estimating the weights of criteria with MABAC for the evaluation of PVC carpentry manufacturers.
The uncertainty in fuzzy membership functions have led to other hybrid methods. Similarly, Aghdaie and Tafreshi [
46] integrated the fuzzy set theory with the SWARA in the weight of the key performance index in the recency-frequency-monetary model. Also, Stanujkić and Karabašević [
47] extended the WASPAS method [
48] with an intuitionistic fuzzy number in evaluating a website and showed that it is an improvement over the other extension with a hamming distance. Singh and Garg [
49] developed the symmetric triangular interval type-2 intuitionistic fuzzy set operations and the use of the Hamy mean to aggregate the operators. The company selection problem and the risk for an event was used to illustrate the use of the operator. Dursun and Arslan [
50] combined the quality function development (QFD), linguistic hierarchies, and 2-tuple fuzzy linguistic, and COPRAS value for selecting the most appropriate material in the manufacturing of detergents. The development criteria for a new product was obtained using the QFD and the criteria weight from experts, then an assessment of the alternatives was conducted using the COPRAS method. Ye et al. [
51] proposed the grey correlation accurate weighted determining method that combined interval-valued intuitionistic and fuzzy decision-theoretic rough sets in deciding the region for e-commerce development in Sichuan province of China. Some researchers use the fuzzy set as the main domain for dealing with uncertainty. Nonetheless, GST can also be applied in dealing with uncertainty.
2.2. Grey System Theory
The grey system theory (GST) was developed by Deng [
52] to handle uncertainty. A simple interpretation of a grey system is an incomplete information system. In real life, every system can be categorized as an incomplete information system. In other words, if the information of the system is complete, then there is little or no room for further research. The emphasis in the GST is to what extent is the information incomplete, i.e., what is the shade of the grey system? Now, a white system is one with complete information, and a black system is a system with unknown information. A system with partial information is the grey system. In a timescale, the past is a white system, and the future is a black system, but a grey system is the present, in which we are in between the past and the future, the known and the unknown.
In GST, the grey number is used to represent incomplete systems. There are different types of grey numbers, the unbounded, bounded, and interval grey numbers. In this research, the interval grey numbers will be used. While an interval number is all the possible numbers within the lower and upper interval, an interval grey number is a single number within the lower and upper bound of the grey number. The GST is different from the classical statistical and probability methods that require a large data sample and should conform to a typical distribution [
53]. On the other hand, GST stands to maximize the information presented for computation, and provide an interpretation of the data, regardless of how little the information is presented for analysis [
8].
Although the GRA has been applied to solve lots of MCDM problems, GRA is used in logistics and industrial applications. Liu and Tong [
54] applied GRA in selecting the optimal design strategy in an engineering automatic form by providing the correlation between the design and analytical factor. Yazdani et al. [
55] combined QFD and GRA to evaluate performance in logistics management in a supply chain. The main criteria evaluated are the environmental indicators, economic downturn, and the social and cultural complaints. Although the fuzzy linguistic value was used to estimate the weights of the criteria, GRA was used in ranking the criteria for the evaluation of the supply chain. Sefidian and Daneshpour [
56] developed the missing values imputation method that replaces the Euclidean distance with the GRA in the fuzzy clustering algorithm. Ramesh et al. [
57] applied GRA in combination with the Taguchi based orthogonal array in ranking the control factors with regards to the output of the design of experiments (DoE) approach, thereby obtaining the setting for a better cutting force. Lai et al. [
58] applied GRA in determining the most influential factor, imbibition recovery, on the spontaneous imbibition of a tight gas reservoir in the Ordos basin, Chain.
GST has been used in solving group decision-making problem. Commonly, the DMs’ preferences are measured as linguistic values then aggregated or whitenized and used in conjunction with other MCDM evaluation methods. In aggregating the DMs’ preferences, Mehrjerdi [
59] and Wang et al. [
60] used the grey arithmetic mean in aggregating the linguistic variables of the DMs for solving an MCDM system selection problem. However, Kang et al. [
61] applied the geometric mean to aggregate the DMs’ preferences in the selection of restaurants. Zhang [
62] applied the correlative operator for combining the grey linguistic value of the DMs, and Ma et al. [
63] equally applied the correlative operator for weighting in the evaluation of investment options. Jin et al. [
64] proposed multiple harmony operators for aggregating group linguistic variables. Notwithstanding the operator used in aggregating the DMs’ preferences, a lower-level problem of the DMs’ preferences measurement approach remains, and there is a need to consider the orientations from which the DMs view the evaluation criteria, and to present an improved measurement instrument.
2.3. Regulatory Focus Theory
Regulatory focus theory, according to Crowe and Higgins [
65], allows one to understand the fundamental ways we approach a task or a goal as it pertains to our motivation. Various factors can motivate people during target tracking, and we regulate our methods and processes while aiming and pursuing it. RFT suggests that motivational power is increased when the way in which people work towards a goal supports their regulatory focus. Individuals can pursue different goals with different regulatory orientations and explore opportunities. There are two different types of regulatory orientations that people use to achieve their goals: Promotion focus orientation and prevention focus orientation. According to Higgins [
66], individuals use internal processes to achieve personal goals. Regulatory focus theory assists in predicting persuasion by manipulating communications that may depend on an individual’s personal goals and characteristics [
67].
Interestingly, Forster et al. [
68] proposed the goal looms larger effect, which confirmed that people’s motivation increases as they move closer to their goals based on their regulatory focus. In other words, motivation and performance on tasks towards a goal increase when the regulatory focus of the incentive and mean matches [
69]. For example, Zhao and Pechmann [
70] showed that effective communication is based on the synergy of the receiver’s regulatory focus, the regulatory focus of the message, and the message frame. Besides investigating the motivation of people, Dijk and Kluger [
71] hypothesized that the task type moderates task performance and their motivation. They confirmed that different tasks could be affected by one regulatory focus, as well as regulatory focus and task type explaining the variability of the feedback effect on task motivation and performance. On the other hand, Bullens et al. [
72] showed the reversibility of a decision affects motivation, implying that a reversible decision strengthens prevention focus more than promotion focus. This implies that reversible DMs are more prevention focus than promotion focus ones, and this has some effect on the choice satisfaction of the DMs.
Some applications and investigations for RFT in decision-making have been presented, which support the upper echelons theory. Kuhn [
73] presented how DMs make a judgment in hiring staff based on the promotion focus, i.e., selecting the good; and prevention focus, i.e., rejecting the bad. Ahmadi et al. [
74] attempted to answer the question of whether managers are motivated to explore in the face of new technological change. The answers and explanation were dependent on the regulatory focus and fit of the DMs. Lai et al. [
75] empirically investigated information system development team performance and confirmed that transformational leadership leads to promotional focus, while transactional leadership leads to prevention focus. Liao and Long [
76] discovered that a company environmental innovation process is positively influenced by the top leader’s promotion focus, but negatively influenced by their prevention focus. Song et al. [
77] showed that institution-based trust impacts community commitment, which is moderated by RFT orientations.
Most importantly, Higgins and Cornwell [
78] extensively discussed the frontier of RFT and presented the effects of both prevention and promotion focus on decision-making, but the aspect of the MCDM weighting method was not mentioned. Lastly, Lo Gerfo et al. [
79] conducted neurophysiological testing of the RFT and showed DMs indeed could be either more promotion focused or more prevention focused. This research fills the gap in the literature that has not applied the RFT in MCDM in estimating the weights of the DMs in an uncertain decision-making environment by measuring the DMs’ weight from both promotion and prevention focus orientations.