Application of Fuzzy AHP on the Selection of Online Shopping Platform in Malaysia First Author\[1\]\*, Second Author2 (Double Blind Review: Please do not type or edit anything here until final-camera ready submissions) *1First affiliation, City and Country (Please do not type or edit anything here, until final camera-ready paper submission)* *2Second affiliation, City and Country (Please do not type or edit anything here, until final camera-ready paper submission)*
ARTICLE INFO ABSTRACT

Article history:

Received

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Published 1 March 2024

 There are increasing number of online shopping nowadays. People sometimes may have difficulties in choosing a preferred online shopping platform, since there are platforms with better navigation, but not many choices of products, and so on. Hence, this study aims to determine and rank the optimal factors that influence users in choosing an online shopping platform in Malaysia. The Fuzzy Analytic Hierarchy process is used to achieve the objectives of this study by ranking the four factors which are user-friendly, trusted, price and promotion, and responsive. This study used primary data collected by distributing questionnaires to two experts, an online marketer from a business company and a lecturer with marketing expertise from UiTM Arau, Perlis. The findings show that price and promotion has become the most influence factor. In conclusion, the Fuzzy AHP method can help researchers to rank all the factors with accuracy and assist users to determine a suitable online shopping platform for them to use.

Keywords:

Analytic Hierarchy Process

E-commerce

Online Shopping Platform

DOI:

10.24191/jcrinn.v9i1
# introduction Since many decades ago, people had dealt with business transactions at a physical store. Sales are carried out within business properties like retail stores and are often called as a traditional brick-and-mortar organization. As the technological landscape and the types of services enabled by connectivity continue to evolve, activities that previously required, or were mostly undertaken through, in-person (face-to-face) physically, interactions are increasingly being accomplished online through virtual interactions of various approaches as stated by Dias et al. (2020). Online shopping is an alternative for individuals to buy anything without going out, especially during the COVID-19 pandemic, where people are prohibited from going out to avoid the potential of contracting with the virus. According to Amsari and Sari (2022), people had to change their lifestyle after restrictions were forced upon them, and this includes their shopping behaviour, which they had to start selling and/or buying through online shopping platforms. Thus, there is an increasing number of people visiting online shopping platforms, such as Shopee, Lazada, Amazon, Zalora, Carousell, Mudah.my, eBay, Sephora, Lelong, PrestoMall and so on. Nevertheless, having countless online shopping platforms makes it difficult for users to choose their preferred option as well. They will need to consider a platform that offers them easy and pleasant online shopping experiences. There are platforms that have easy navigation but might occasionally encounter issues when users attempt to use vouchers, like free shipping vouchers. Certain platforms might provide interesting features, but do not have a user-friendly interface. In this study, we are aimed to investigate and rank the factors that influence users in choosing an online shopping platform in Malaysia. According to the literature review, there are four factors that are most dominant and influence for users in choosing the online shopping platform, which are user-friendly (Kiew et al., 2021; Manwaluddin et al., 2018; Tseng et al., 2021), trusted (Huang et al., 2021; Faqih, 2022), price and promotion (Sheehan et al., 2019; Bucko et al., 2018), and responsive (Busalim et al., 2021; Hewei & Youngsook, 2022). In order to fulfil the objective, method of Fuzzy Analytic Hierarchy Process (FAHP) is applied. The remaining part of the paper proceeds as follows. In Section 2, some preliminaries consist of definition and theory on the triangular fuzzy number, FAHP and Saaty’s scale are provided. In Section 3, the methodology that involved in the study is presented. Next, result and some discussion illustrated in Section 4. Finally, the conclusion is drawn in Section 5. # Preliminaries This section provides definition and theory particularly on triangular fuzzy number, FAHP and Saaty’s scale which are used in this study. **Triangular fuzzy number** A fuzzy number of is said to be a triangular fuzzy number (TFN) if its membership function is given by, where and represent the lower and upper bounds of the fuzzy number , respectively, and is the median value (Zadeh, 1965). The standard form of the TFN is demonstrated in Figure 2.1. ![A graph of a function Description automatically generated](65c1d304a684e_media/media/image7.png) Figure 2.1 Representation of a TFN . ## Fuzzy Analytic Hierarchy Process (FAHP) In the 1970s, Saaty introduced Analytic Hierarchy Process (AHP) as the main multi-criteria decision-making approach. A study by Díaz et al. (2022) stated Analytic Hierarchy Process is one of the common methods in solving selection problems and this method causes a hard decision to be decomposed by breaking it into various parts and allocate each problem’s aspect with different weights and rankings. According to a previous study by Putra et al. (2018), AHP that has adapted with fuzzy logic theory, is called as a Fuzzy Analytic Hierarchy Process (FAHP). The only difference in FAHP method is that the AHP scale is placed into the fuzzy triangle scale to access priority. Many problems related to decision-making use the FAHP method, for instance, the passenger aircraft type selection. Dožić et al. (2018) used the FAHP approach to choose an aircraft that could operate a designated set of routes while considering the passenger’s interests and its own interests. Their study decided to use this method since there were uncertainties in making decisions and the aspect of human vagueness when reasoning for a multi-criteria problem. Market conditions and airlines’ requirements are the significance in choosing aircraft type. A study by Li et al. (2017) also used the FAHP method to appraise in-flight service quality since one of the most important parts in the service process of air travel is the in-flight service, but they used a hybrid approach. Besides, another previous study by Putra et al. (2018) used the FAHP method to determine the quality of gemstones. Exceptional ability is required to select and assess the gemstones’ quality so that it can be traded. When individuals possess minimal ability and knowledge and proceed to analyse the quality of gemstones, this can become an obstacle concerning the types of gemstones and consumers variation. According to Calabrese et al. (2019), FAHP was used to choose relevant sustainability issues. Environment and society are at risk of being affected by negative impacts generated by business activities from companies that failed to integrate sustainability into their process, long-term vision, and strategies. ## Saaty’s scale In applying the FAHP method in this study, the used of the Saaty’s scale is very important. The standard Saaty’s scale (Saaty, 1980) that corresponds to the fuzzy triangular number and its linguistic term is given in Table 2.1. Table 2.1 Saaty’s scale with the fuzzy triangular number and its linguistic term. | Classic Saaty’s Scale | Fuzzy Triangular Number | Inverse of Fuzzy Triangular Number | Linguistic Term | | --------------------- | ----------------------- | ---------------------------------- | -------------------------- | | 1 | (1,1,1) | | Equally Important | | 3 | (2,3,4) | | Moderate Important | | 5 | (4,5,6) | | Strong Important | | 7 | (6,7,8) | | Very Strong Important | | 9 | (9,9,9) | | Extremely Strong Important | | 2 | (1,2,3) | | Intermediate Values | | 4 | (3,4,5) | | | | 6 | (5,6,7) | | | | 8 | (7,8,9) | | | # methodology The methodology of this study involves a structured framework comprising ten essential steps. Step 1: A questionnaire has been developed and answered by two chosen experts, which are a professional online marketer and a marketing lecturer from UiTM Perlis. The questionnaire consisted of three sections, which is in Section A involved the demographic issues such as genders, age, based company and working experiences. While in Section B and C involved the evaluation of the factors and sub-factors, respectively. Step 2: The outcomes from the questionnaire are substituted into the pairwise comparison matrices based on the Saaty’s scale as provided in the Table 2.1. The general form of pairwise comparison matrix is given as follows: (1) * *where represents the number of criterion or factors involved in the study. The matrix is positive and symmetric, since and for every . In other words, if the essential preferences is located in the upper triangle of the matrix, then the reciprocal value must be at the lower triangle or vice versa (Bozanic, et al., 2013). Step 3: Next, the consistency ratio (CR) of the experts' fuzzy triangular scale for the pairwise comparison matrix is calculated. The CR should be less than or equal to 10% (0.1), otherwise the pairwise comparison as in step 2, should be re-implemented. The CR is computed using the equation below: (2) where (3) andis the largest eigenvalue of the comparison matrix and *n* is the number of samples. While, the random consistency index (*RI*) is based on the number of sample (Saaty, 1980), as given in Table 3.1. Table 3.1 Random consistency index. | Number of samples, *n* | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | | ---------------------- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | | Ratio Index, *RI* | 0.00 | 0.00 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 | Step 4: Calculate the average of the expert preferences using the given formula: (4) where represents the number of experts. From the average value, the pairwise comparison is updated. Step 5: Determine the fuzzy geometric mean, for each factor based on the following equation: > (5) where is multiplied by each fuzzy value from the pair-wise comparison matrix. Subsequently, calculate the vector summation, of the geometric mean for each factor using: > (6) Step 6: Calculate the inverse of vector summation, . (7) Then, the is arranged according to its order. Step 7: Based on the new arrangement of , fuzzy weight of each factor is determined using: (8) where is the fuzzy geometric mean for each factor and is the new arrangement of . Step 8: The fuzzy weight is converted to non-fuzzy value, or known as defuzzification process, which implemented using: (9) Step 9: For the non-fuzzy weight, the weight must be normalized using the following formula: > (10) where is the final weight after normalization. Step 10: Ranking and selection of decisions. This is based on normalized weight. The factors are ranked from the highest value to the lowest value. Hence, the highest value is the best factor. # result and discussion Based on methodology presented in the previous section, the outcomes from the two experts are extracted to be in the form of pairwise comparison matrix as presented in Table 4.1. The consistency ratio (CR) also has been calculated. Table 4.1 Pairwise comparison matrix for both experts and their consistency ratios. | Expert 1 | Expert 2 | | ------------------------------------------ | ------------------------------------------ | | ![](65c1d304a684e_media/media/image47.wmf) | ![](65c1d304a684e_media/media/image48.wmf) | | Thus, | Thus, | Since both CR are less than 0.1, thus the comparisons made by the experts are considered acceptable and consistent. Next, the average pairwise comparison matrix for all experts is calculated by setting up the triangular fuzzy number of comparison matrix from each expert (as in Table 4.2 and 4.3). Then, calculate the average using the Equation (4). Table 4.2 Pairwise comparison matrix of Expert 1 | Factor | User-Friendly | Trusted | Price & Promotion | Responsive | | ----------------- | ------------- | ------- | ----------------- | ---------- | | User-Friendly | | | | | | Trusted | | | | | | Price & Promotion | | | | | | Responsive | | | | | Table 4.3 Pairwise comparison matrix of Expert 2 | Factor | User-Friendly | Trusted | Price & Promotion | Responsive | | ----------------- | ------------- | ------- | ----------------- | ---------- | | User-Friendly | | | | | | Trusted | | | | | | Price & Promotion | | | | | | Responsive | | | | | Table 4.4 Average pairwise comparison matrix for all experts | Factor | User-Friendly | Trusted | Price & Promotion | Responsive | | ----------------- | ------------- | ------- | ----------------- | ---------- | | User-Friendly | | | | | | Trusted | | | | | | Price & Promotion | | | | | | Responsive | | | | | Then, calculation of the fuzzy geometric mean of each factor was performed using the Equation (5). The following shows an example of geometric mean calculation for user-friendly factor. (11) Table 4.5 shows the fuzzy geometric mean for all factors. Table 4.5 Fuzzy geometric mean for all factors. | | Fuzzy geometric mean | | | | ------------------------ | -------------------- | ------ | ------ | | User-Friendly | 1.1067 | 1.3512 | 1.6549 | | Trusted | 0.4811 | 0.6180 | 0.8409 | | Price & Promotion | 2.1147 | 2.6918 | 3.2237 | | Responsive | 0.5117 | 0.5590 | 0.6493 | | Vector Summation | 4.214 | 5.220 | 6.369 | | Inverse Vector Summation | 0.1570 | 0.1916 | 0.2373 | While, based on the geometric mean calculated in Equation (11), vector summation and inverse vector summation have been calculated using Equations (6) and (7), respectively. Subsequently, Table 4.6 shows the fuzzy weight, non-fuzzy weight, and normalized weight that have been calculated using Equations (8-10). The example calculation for the user-friendly factor is shown below. Table 4.6 Fuzzy weight, non-fuzzy weight, and normalized weight of all factors | Factor | Fuzzy Weight, *W* | Non-Fuzzy Weight, *C* | Normalized Weight, *Z* | Rank | | ----------------- | ------------------------ | --------------------- | ---------------------- | ---- | | User-Friendly | (0.1738, 0.2589, 0.3927) | 0.2751 | 0.2601 | 2 | | Trusted | (0.0755, 0.1184, 0.1995) | 0.1312 | 0.1240 | 3 | | Price & Promotion | (0.3320, 0.5157, 0.7650) | 0.5376 | 0.5083 | 1 | | Responsive | (0.0803, 0.1071, 0.1541) | 0.1138 | 0.1076 | 4 | | Sum | 1.0577 | 1 | | | Each factor is ranked based on the normalized weight from the highest to the lowest value. # conclusion Various online shopping platform with different features, a variety of products, and services create confusion to the users when they tried to choose a suitable and the best platform. Hence, the aims of this study, are to determine and rank the factors that influence users in choosing an online shopping platform in Malaysia. In order to evaluate the factors, a questionnaire had been distributed to an online marketer expert and a lecturer at UiTM Arau, Perlis, who is an expert in marketing. The collected data was calculated using Fuzzy AHP method, where it is the combination of Fuzzy theory and Analytic Hierarchy Process. This method helped in achieving the objectives of this study, since it solved selection problems by decomposing the criterion and allocated fuzzy weights and rankings. Thus, according to the results, this study concluded the optimal factor that influences the users when choosing an online shopping platform is price and promotion. For the future study, it is recommended to use other method, such as the Fuzzy TOPSIS because it is one of the best methods to get ideal solution among similar options, or other Multi-Criteria Decision-Making (MCDM) methods. Such an approach can be made so that the results of the research could be verified by having almost similar outcomes. # acknowledgement The authors would like to acknowledge the support of Universiti Teknologi Mara (UiTM), Cawangan Perlis and Institute of Engineering Mathematics, UniMAP for providing the facilities support on this research. The authors would also like to express their gratitude to the anonymous referee for the constructive comments to improve this study. # # Conflict of interest statement The authors agree that this research was conducted in the absence of any self-benefits, commercial or financial conflicts and declare the absence of conflicting interests with the funders. # References Amsari, S., & Sari, D. P. (2022). Consumer Factors in Deciding to Purchase Online at Shopee E-Commerce During the Covid-19 Pandemic. *Proceeding International Seminar of Islamic Studies*, *3*(1), 174–182. Bozanic, D., Pamucar. D, (2013). 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(2020). *Success Factors of E-Commerce – Drivers of the Conversion Rate and Basket Value*. *April*, 43–50. https://doi.org/10.33965/es2020\_202005l006 1. \* Corresponding author. *E-mail address*: (Please do not type or edit anything here, our editors will do the work for you)