Prerpints.org logo
Preprint
Article

Evaluation and Selection of Products Based on the Quality Dimensions by Word of Mouth (Case Study: Brands of the Mobile Phone)

Altmetrics

Downloads

641

Views

107

Comments

0

This version is not peer-reviewed

Submitted:

02 May 2023

Posted:

03 May 2023

You are already at the latest version

Alerts
Abstract
Today, mobile phones are at the center of many consumers' lives and a part of people's lives. Although this tool was considered a luxury initially, it has gradually become a necessary tool with the reduction of the price and the increase of its functions. Consumers choose specific models of mobile phones according to their needs. Mobile phones have quality features that are expressed in the form of quality dimensions. These dimensions are mostly expressed by word of mouth (WOM). In this research, first; we collected the opinions of 350 students about 4 brands of mobile phones based on Garvin's quality of dimensions, finally; the students’ opinions about mobile phones were evaluated with integrated TOPSIS with interval-valued intuitionistic fuzzy numbers technique, and the priority of 4 phone brands was determined based on students' opinions.
Keywords: 
Subject: Business, Economics and Management  -   Business and Management

1. Introduction

Today, consumers are more aware of the products or services they buy and consume. Before deciding to buy, they check the opinions of previous customers and do not just rely on product advertisements [1].
Product awareness increases with interpersonal communication because consumers often share opinions, news, and information with others and also discuss products, brands, and experiences with each other[2,3]. In marketing, these discussions are known as Word-of-Mouth (WOM) [3]. Word-of-mouth has been considered one of the most powerful forms of communication in the market today [4].
Word of mouth is a way of sharing ideas, beliefs, and experiences [5]. Word of mouth is a non-commercial, informal communication between consumers who discuss the use, performance, features, or ownership of a particular product or service [6,7].
Using word of mouth has increased among consumers before making a purchase decision [8]. A study by Bughin, Doogan, and Vetvik [9] found that 20 to 50 percent of all purchase decisions are due to word of mouth, generating more than twice as many sales as paid advertising. Consumers recognize WOM as an important source of information for making their purchasing decisions [10].
WOM is more important than other external sources of information [11]. Some studies have shown that consumers consider word of mouth as the most reliable source of information. Customers consider 90% of advertising to be non-credible while 90% of WOM is credible [12]. Consumers find the opinions of their peers more valid and reliable than marketers' messages [13]. WOM significantly influences consumers' decision-making processes [12].
The strength of purchaser reviews in influencing consumer choices is so prominent that many companies, such as Kia Motors, are now website hosting opinions on their websites to permit customers to speak about the quality of their products. Purchasers are more and more using WOM to find out about product quality [8]. People talk about quality. Everyone is concerned about the quality at least it seemed of goods and services consumed by them and the environment for life and work given to them [14]. Today consumers have become more inclined toward quality products [15]. Product quality is the product's ability to provide the needs and expectations of customers or even exceed the expectations of customers [16]. One of the more well-known frameworks for determining quality is that of David A. Garvin. Garvin believed that quality is multidimensional. The eight basic dimensions of quality according to Garvin are performance, features, reliability, conformity, durability, serviceability, aesthetics, and perceived quality [17].
Usually, people express their opinions verbally, such as excellent, good, etc. These verbal ideas are vague and do not express themselves exactly. In many phenomena where there is uncertainty, it is difficult to express certainty [18]. Zadeh solved these ambiguities to some extent by introducing fuzzy sets [18,19]. Fuzzy sets use numbers from zero to one to show how much each element belongs to the set which is called the degree of membership [20]. Classical fuzzy set theory has limitations because it doesn't provide enough information [21]. As a result, the theory of intuitionistic fuzzy sets (IFS) was introduced by Atanasov [22]. In this theory, Atanasov defined concepts such as non-membership degree and hesitation degree. Sometimes, it is not possible to specify information such as degree of membership and degree of non-membership with pseudo-exact values [21]. To solve this problem, Atanasov and Gargov further extended the intuitionistic fuzzy set and introduced the concept of interval-valued intuitionistic fuzzy set (IVIFS), and proposed the main algorithm of IVIFS [23]. IVIFS has received increasing attention among researchers and industrialists due to its ability to address the uncertainty and uncertainty of a real-world problem [24]. In IVIFS the membership function and non-membership function values are defined as intervals rather than exact numbers [25]. Therefore, this essential feature of interval-valued intuitionistic fuzzy numbers can provide a more accurate expression of the quality dimensions that are expressed by people.
Today, consumers are facing many challenges. One of these challenges is ‘over choice’. Consumers suffer from choice fatigue [26]. The presence of many products and multiple criteria makes it difficult to decide to choose the most suitable product. In such a situation, the use of decision-making methods and systems is very effective [27]. Multi-criteria decision-making (MCDM) ranks the alternatives or chooses the most appropriate alternatives from many alternatives, by looking at different criteria simultaneously [28]. MCDM is a powerful mathematical tool for modeling multi-criteria problems that are carried out by experts in the field of operations research [29]. With MCDM methods, it is possible to evaluate alternatives and make appropriate decisions according to different criteria [30].
The daily use of mobile phones has made it difficult for buyers or mobile phone users to make decisions [31]. Today, smartphones play an important role in personal and professional life [32] and have become one of the most essential electronic tools [33]. The widespread use of smartphones has caused companies to produce different and new models of mobile phones [34]. Every day people come across a vast quantity of better and more advanced capabilities in smartphones. This makes it difficult for users to choose an ideal smartphone [35].
The purpose of this article is to evaluate the quality dimensions of mobile phones by word of mouth. In this research, a mobile store wants to offer products that are good in terms of quality. By choosing Garvin's quality dimensions as a criterion to evaluate the quality of the product and the existence of different brands, the evaluation, and selection of a quality brand becomes a multiple-decision problem. Since people express their opinions verbally and there is no certainty in these opinions, in this situation, the use of interval-valued intuitive fuzzy numbers for verbal opinions can remove these ambiguities. Therefore, the use of multi-criteria decision-making methods with interval-valued intuitionistic fuzzy numbers can help to examine and choose a brand whose quality is famous among people.

2. Literature review

Smartphones are the most demanded electronic devices in the world. Every year, lots of new smartphones come out from different companies with different features [36]. For this reason, choosing a suitable model or brand from among the types and brands presented is very complicated [37]. In the literature, there are many studies on choosing a smartphone that has used different methods and criteria.
Goswami and Behera [36] selected the best smartphone with the help of the preference ranking organization method for enrichment of evaluations (PROMETHEE). Also, the fuzzy Analytic hierarchy process (FAHP) based on trapezoidal fuzzy numbers was used to evaluate the criteria weights.
In other research, Goswami and Mitra [33] used two MCDM tools i.e. Complex Proportional Assessment (COPRAS) and Additive Ratio Assessment (ARAS) to choose the fine mobile version among numerous options available on the market.
In research by S. Kumar et al [38], the Integrated Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Objective Optimization based on of Ratio Analysis (MOORA) approach is proposed for prioritizing five smartphone brands.
R. Kumar et al [39], evaluated twelve mobiles using the Simple Additive Weighting (SAW) and Weighted Product Method (WPM) techniques.
Kumar & Parimala [32], evaluated and recommended the best smartphones by combining Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods.
IVIF-ARAS and IVIF-TOPSIS were used by Rani et al [40] to evaluate different smartphone alternatives based on customer benefits.
Aggarwal et al [35], evaluated smartphones using the MCDM approach with the EDAS (Evaluation Based on Distance from Average Solution) technique. In this article, hardware, economic, and physical features are used to choose the most suitable phone.
In research by Srivastava et al [41] Analytic Hierarchy Process (AHP) method was used to compare 4 smartphones of a company.
Büyüközkan and Güleryüz [42], used Intuitionistic Fuzzy TOPSIS with Group decision-making (GDM) to discover the most appropriate smartphone in a Turkish company.
The purpose of Belbag et al [43] was to measure consumers’ evaluation of purchasing smartphones by applying the Fuzzy ELECTRE I method.
To determine the best smartphone, Yildiz and Ergul [34] applied Analytic Network Process (ANP) and Generalized Choquet Integral (GCI).
Trivedi et al [44], evaluated the smartphone using the fuzzy Analytic hierarchy process (FAHP) and fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS).
Akyene [31], used Entropy and TOPSIS to help the customer to buy the best mobile phone. He evaluated 10 mobile phones with 12 criteria.
The aim of Işıklar and Büyüközkan [45] was to propose AHP and TOPSIS to evaluate the mobile phone options according to the users' preferences order. These studies are given in Table 1.
In the literature, it has been found that certain criteria such as appearance criteria and hardware capabilities are effective in choosing a mobile phone. No study focuses extensively on the qualitative dimensions of smartphone evaluation. In this research; the qualitative dimensions mentioned by Garvin [17] were used as criteria for choosing a mobile phone. In the following, Garvin's quality dimensions are presented according to what is stated in the academic literature.

2.1. Quality of dimensions

Quality is a complex and multidimensional concept. In general, product quality is the capacity of a product to meet or exceed customer expectations. Customer perception of product and service excellence is an operational definition of quality. [46].
Various definitions and dimensions have been stated for quality. One of the most reliable definitions of quality was provided by David Garvin of Harvard Business School (1984) [47]. According to Garvin's theory, there are eight critical dimensions of quality: Performance, Features, Reliability, Conformance, Durability, Serviceability, Aesthetics, and Perceived Quality [17].

2.1.1. Performance

Performance refers to the operational characteristics of a product [48]. This dimension includes measurable characteristics, for example, speed, ease, and convenience of use. Since they contain quantifiable properties, brands can be ranked based on their Performance [49].

2.1.2. Features

Another part of the Performance is Features. Features are like “bells and whistles” of products and services. Features are like extra things that come with products and services. These features are placed next to the main benefits and complement them. These features complement the main benefits and improve performance and quality [17,48].

2.1.3. Reliability

Reliability means how likely the product is to work perfectly for a while [50]. When we talk about reliability, we look at a few things: how long it takes for it to break down the first time, how long it lasts before breaking down again, and how often it breaks down in a set amount of time [51].

2.1.4. Conformance

The degree of compliance of the product's performance characteristics with the predetermined standards is called Conformance [51]. This is a kind of "promise" that the product must keep. The products that have this dimension of quality are under the standard [48].

2.1.5. Durability

Durability means how long a product lasts. Durability has two technical and economic dimensions. How well it works technically and how much it costs. From a technical point of view, Durability means how much you can use a product before it stops working. From an economic point of view, when deciding whether to buy a new product, people should consider how much money it will cost to fix old products versus how much it would cost to buy a newer, more reliable one [17,49].

2.1.6. Serviceability

Serviceability means how easy it is to fix something. In addition to how easy it is to fix something, it's also important that the person doing the repairs is fast, kind, and skilled. Things that can be fixed easily are better quality than things that can't be fixed [48].

2.1.7. Aesthetics

Aesthetics means how something looks, feels, sounds, tastes, or smells. It varies from person to person and depends on their preferences. The aesthetics dimension is about how something looks and helps a business be recognized [17].

2.1.8. Perceived Quality

Buyers may not know everything about a product or service. So, they use different methods to compare. When customers are trying to decide if something is good or not, they often rely on the name of the brand, the ads they have seen, and the pictures they have looked at. These things are really important for making people think something is high-quality [17]. The Perceived Quality of a product is mostly based on things like advertisements, the brand name, and past experiences, rather than the product itself [51].

3. Methodology

Biswas and Kumar [52] investigated an integrated TOPSIS method to solve decision-making problems with IVIFS information. In this study, this method has been used to choose a mobile phone.
This section provides an overview of the methodology to be employed in the research for solving the mobile phone selection problem. To begin with, fundamental information around IVIFS and the possibility degree measures for comparing two IVIF numbers (IVIFNs) are displayed, and after that, the computational steps of IVIF TOPSIS are clarified.

3.1. Preliminaries

Definition 1. 
An IVIF set  A ~  is defined by K.Atanassov [23] as:
A ~ = x i , μ A ~ L x i , μ A ~ U x i , ν A ~ L x i , ν A ~ U x i x i ∈ X
Where  μ A ~ L x i , μ A ~ U x i  and  ν A ~ L x i , ν A ~ U x i  denote the intervals of membership and nonmember ship degrees of an element  x i ∈ A ~  satisfying:
0 ≤ μ A ~ U x i + ν A ~ U x i ≤ 1 ,   0 ≤ ν A ~ L x i ≤ ν A ~ U x i ≤ 1  and    μ A ~ L x i ≤ μ A ~ U x i .
Definition 2. 
The hesitation degree of an interval-valued intuitionistic fuzzy number  x i  to set  A ~  is defined as:
π A ~ = 1 − μ A ~ U x i − ν A ~ U x i , 1 − μ A ~ L x i − ν A ~ L x i
If  μ A ~ L x i = μ A ~ U x i  and  ν A ~ L x i = ν A ~ U x i  then  A ~  is reduced to an IFS.
Definition 3. 
Assume that  A = x , μ A x , ν A x : x ∈ X  be an IVIFS. Then, the ordered pair  x , μ A x , ν A x , for each  x ∈ X  is called an IVIFN.
For ease of computation, let  a j = a j l , a j u , b j l , b j u  denote an IVIFN. The basic operations for combining the two IVIFNs are provided in this definition.
Definition 4. 
Suppose that  α i = a i l , a i u , b i l , b i u = a i , b i   i = 1,2  be two IVIFNs and λ>0. Then,
α 1 + α 2 = a 1 l + a 2 l − a 1 l a 2 l , a 1 u + a 2 u − a 1 u a 2 u , b 1 l b 2 l , b 1 u b 2 u
λ α 1 = 1 − 1 − a 1 l λ , 1 − 1 − a 1 u λ , b 1 l λ , b 1 u λ
Definition 5. 
For two interval numbers   a = a l , a u  and  b = b l , b u  having interval lengths  L a = a u − a l  and  L b = b u − b l  respectively, the possibility degree of  a ≥ b  is defined as  p a ≥ b = m i n m a x a u − b l L a + L b , 0 , 1
Definition 6. 
Assume that  α i = a i l , a i u , b i l , b i u = a i , b i   i = 1,2  be two IVIFNs and   L a i = a i u − a i l  ,   L b i = b i u − b i l  be their interval lengths, then the degree of possibility   p α 1 ≥ α 2  of  α 1 ≥ α 2  is defined as
p α 1 ≥ α 2 = m i n m a x a 1 u − a 2 l + b 2 u − b 2 l L a 1 + L a 2 + L b 1 + L b 2 , 0 , 1

3.2. TOPSIS method to solve MCDM problems with IVIFS information

TOPSIS technique for solving the MCDM problems in IVIF settings follows several steps and these steps are described below:
Step 1: By Definition 6, the additive reciprocal fuzzy preference relation
P = p τ i ≥ τ j n × n = p i j n × n from IVIF criteria weight vector
τ = τ 1 , τ 2 , ⋯ , τ n = τ j 1 × n is generated.
The crisp weight w j   for the IVIFN weight τ j   j = 1,2 , ⋯ , n may be obtained by the optimal degree. [53] proposed the optimal degree formula to estimate crisp criteria weights or performance values of alternatives using the possibility degree matrix P as
ξ i = 1 m ( m − 1 ) Σ k = 1 n p i k + m 2 − 1 for i = 1,2 , ⋯ , m
Thus, the crisp criteria weight vector w = w 1 , w 2 , ⋯ , w n with w j ∈ 0,1 and Σ j = 1 n w j = 1 .
Step 2 :
For an IVIF decision matrix D = d i j m × n = α i j l , α i j u , β i j l , β i j u , γ i j l , γ i j u m × n the weighted IVIF decision matrix is obtained using Definition 4 as D w = d i j w m × n , where d i j w = w j d i j , i = 1,2 , ⋯ , m ;   j = 1,2 , ⋯ , n .
Let D w = d i j w m × n = μ i j l , μ i j u , υ i j l , υ i j u , π i j l , π i j u m × n where π i j l =1- μ i j u − ν i j u and π i j u =1- μ i j l − ν i j l
Step 3:
The intervals of relative closeness coefficients of m alternatives
A i = i = 1,2 , ⋯ , m is given by C i = C i l , C i u .
C i u = Σ j = 1 r 1 − υ i j l + Σ j = r + 1 n 1 − μ i j l Σ j = 1 r 2 − μ i j u − υ i j l + Σ j = r + 1 n 2 − μ i j l − υ i j u
C i l = Σ j = 1 r 1 − υ i j u + Σ j = r + 1 n 1 − μ i j u Σ j = 1 r 2 − μ i j l − υ i j u + Σ j = r + 1 n 2 − μ i j u − υ i j l
X 1 , X 2 , ⋯ , X r and X r + 1 , X r + 2 , ⋯ , X n represent sets of benefit criteria and cost criteria.
Step 4:
The possibility degree matrix for pairwise comparison of intervals of relative closeness coefficients C i of alternatives A i = i = 1,2 , ⋯ , m is obtained as P = p i k m × m
p i k = p C i ≥ C k = m i n m a x C i u − C k l L C i + L C k , 0 , 1
Where C i = C i l , C i u and C k = C k l , C k u ,   L C i = C i u − C i l and L C k = C k u − C k l .
Step 5:
The performance scores as optimal degrees ξ i for alternatives A i = i = 1,2 , ⋯ , m are evaluated
ξ i = 1 m ( m − 1 ) Σ k = 1 n p i k + m 2 − 1
Step 6:
The alternatives A i = i = 1,2 , ⋯ , m are ranked in decreasing order of the optimal degrees ξ i .

4. Case study

Nowadays, the smartphone has gotten to be a fundamental portion of cutting-edge life [54]. According to Statista, the use of smartphones in Iran in 2021 was about 57.19 million [55]. According to the statistics of smartphone penetration rates in the world by 2021, Iran ranks 8th with 67.3% [56]. It is predicted that the amount of smartphone usage in Iran will reach 78.58 million in 2025 [57].
Due to the increasing use of smartphones, a mobile store plans to offer phones that are known among people in terms of quality.
This research has been done in two phases. The first phase was a survey of experts to determine the importance of Garvin's quality dimensions in mobile phones. A committee of experts consisting of seven experts is formed with members EX1, EX2 … EX7. These experts work in companies that are responsible for the warranty of mobile phone brands. The warranty of mobile phones in Iran is different from other countries. In most countries of the world, the phone manufacturer directly guarantees its mobile phones, but in Iran, except for limited companies that have official representatives, other phone manufacturers are guaranteed by a secondary agency. These experts describe the importance of the eight criteria by following linguistic terms: Very important (VI), Important (I), Moderate important (MI), Unimportant (UI), and Very unimportant (VUI).
Table 2 shows linguistic variables used for the importance of criteria and their relevant IVIF numbers.
After collecting the opinion of the experts and converting it to interval-valued intuitionistic fuzzy numbers, the average of these opinions was calculated. The opinion of the experts and their averages are shown in Table 3.
So, in the first phase, the importance and weight of the criteria were determined.
The second phase was a survey of customers. The target community is those who can evaluate the quality of phone brands. The statistical population of this research is the students of the Shahrood University of Technology.
The second phase of the research deals with students' evaluation of the quality dimensions of 4 prominent smartphone brands in the country (Brand 1, Brand 2, Brand 3, Brand 4). Based on Cochran's formula [58], the sample size was calculated to be 350.
The questionnaire included the evaluation of the quality dimensions of the smartphone through word-of-mouth advertising. The participants expressed their evaluation of the quality dimensions of 4 smartphone brands in terms of language as follows: Extremely Bad (EB), Very Bad (VB), Bad (B), Moderate Bad (MB), Fair (F), Moderate Good (MG), Good (G), Very Good (VG), Extremely Good (EG). Table 4, shows some of these data.
This study provides a nine-point rating scale and modifies the linguistic descriptions of each term as provided in Table 5.
Using Table 5, the linguistic terms of Table 4 are converted into Interval-valued intuitionistic fuzzy numbers. The averages of all 8 criteria for 4 phone models are obtained by definition 4. Therefore, interval-valued intuitionistic fuzzy matrix 4 × 8 will be as follows:
IVIF decision matrix
Performance Features Reliability Conformance
Brand1 < 0.70,0.75 , 0.13,0.19 > < 0.84,0.89 , 0.04,0.09 > < 0.75,0.80 , 0.09,0.14 > < 0.78,0.84 , 0.07,0.12 >
Brand2 < 0.73,0.78 , 0.11,0.16 > < 0.69,0.74 , 0.13,0.20 > < 0.58,0.63 , 0.26,0.31 > < 0.60,0.65 , 0.22,0.28 >
Brand3 < 0.85,0.90 , 0.03,0.08 > < 0.85,0.91 , 0.04,0.08 > < 0.82,0.88 , 0.05,0.10 > < 0.83,0.89 , 0.05,0.10 >
Brand4 < 0.58,0.63 , 0.26,0.31 > < 0.49,0.54 , 0.36,0.41 > < 0.44,0.50 , 0.38,0.44 > < 0.25,0.30 , 0.59,0.64 >
Durability Serviceability Aesthetics Perceived Quality
Brand1 < 0.82,0.88 , 0.05,0.10 > < 0.71,0.77 , 0.12,0.18 > < 0.85,0.90 , 0.03,0.08 > < 0.76,0.83 , 0.09,0.14 >
Brand2 < 0.76,0.83 , 0.06,0.14 > < 0.74,0.79 , 009,0.15 > < 0.57,0.62 , 0.27,0.32 > < 0.76,0.83 , 0.09,0.14 >
Brand3 < 0.78,0.92 , 0.03,0.07 > < 0.84,0.90 , 0.05,0.09 > < 0.84,0.89 , 0.04,0.09 > < 0.86,0.91 , 0.04,0.08 >
Brand4 < 0.34,0.40 , 0.47,0.53 > < 0.38,0.44 , 0.40,0.48 > < 0.57,0.62 , 0.27,0.32 > < 0.34,0.40 , 0.49,0.54 >
After obtaining the IVIF decision matrix, the steps of the integrated TOPSIS method can be implemented.
Step 1
According to Table 3, the IVIF criteria weight vector is τ = τ 1 , τ 2 , ⋯ , τ 8 = < 0.75,0.86 , 0.06,0.13 > , ⋯ , < 0.52,0.69 , 0.14,0.26 > .
Using Definition 6, the possibility degree matrix obtained by pairwise comparison of criteria is
P = p τ i ≥ τ j 8 × 8 = 0.500 0.708 0.292 0.500 1.000 1.000 1.000 1.000 0.000 0.000 0.000 0.000 0.500 1.000 0.000 0.500 1.000 0.911 1.000 0.706 1.000 1.000 1.000 0.950 0.398 0.000 0.000 0.000 0.639 0.140 0.000 0.000 0.000 0.000 0.089 0.294 0.602 1.000 1.000 1.000 0.000 0.000 0.000 0.050 0.361 1.000 0.860 1.000 0.500 0.016 0.984 0.500 0.735 0.256 1.000 0.743 0.265 0.000 0.744 0.257 0.500 0.003 0.997 0.500
By ξ i = 1 m ( m − 1 ) ∑ k = 1 n p i k + m 2 − 1 for i = 1,2 , ⋯ , m the crisp criteria weight vector becomes w = w 1 , w 2 , ⋯ , w 8 = 0.181,0.169,0.101,0.063,0.109,0.154,0.092,0.132
Step 2
Using Definition 4, the weighted IVIF decision matrix is obtained as
D w = d i j w m × n = w j d i j
Weighted IVIF decision matrix
Performance Features Reliability Conformance
Brand1 < 0.20,0.22 , 0.69,0.74 > < 0.26,0.31 , 0.59,0.66 > < 0.13,0.15 , 0.78,0.82 > < 0.09,0.11 , 0.85,0.88 >
Brand2 < 0.21,0.24 , 0.67,0.72 > < 0.18,0.21 , 0.71,0.76 > < 0.08,0.10 , 0.87,0.89 > < 0.06,0.06 , 0.91,0.92 >
Brand3 < 0.29,0.34 , 0.54,0.63 > < 0.27,0.33 , 0.58,0.66 > < 0.16,0.19 , 0.74,0.79 > < 0.10,0.13 , 0.83,0.87 >
Brand4 < 0.14,0.16 , 0.78,0.81 > < 0.11,0.12 , 0.84,0.86 > < 0.06,0.07 , 0.91,0.92 > < 0.02,0.02 , 0.97,0.97 >
Durability Serviceability Aesthetics Perceived Quality
Brand1 < 0.17,0.21 , 0.73,0.78 > < 0.17,0.20 , 0.72,0.77 > < 0.16,0.19 , 0.73,0.79 > < 0.17,0.21 , 0.73,0.77 >
Brand2 < 0.15,0.17 , 0.77,0.81 > < 0.19,0.22 , 0.69,0.75 > < 0.07,0.09 , 0.89,0.90 > < 0.17,0.21 , 0.73,0.77 >
Brand3 < 0.20,0.24 , 0.68,0.74 > < 0.24,0.30 , 0.63,0.70 > < 0.15,0.19 , 0.75,0.80 > < 0.23,0.28 , 0.64,0.71 >
Brand4 < 0.04,0.05 , 0.92,0.93 > < 0.07,0.08 , 0.87,0.89 > < 0.07,0.09 , 0.89,0.90 > < 0.05,0.06 , 0.91,0.92 >
Step 3
Using Eqs. (1) and (2) The intervals of relative closeness coefficients of alternatives are obtained as
C 1 = 0.212,0.255
C 2 = 0.176,0.208
C 3 = 0.249,0.302
C 4 = 0.096,0.111
Step 4
Using Eq. (3), the possibility degree matrix for the pairwise comparison of alternatives is
P = p i j 4 × 4 = 0.500 1.000 0.000 0.500 0.059 1.000 0.000 1.000 0.941 1.000 0.000 0.000 0.500 1.000 0.000 0.500
Step 5
Using Eq. (4), the performance scores of alternatives A i = i = 1,2 , ⋯ , m as optimal degrees ξ i are estimated as
ξ 1 = 0.297
ξ 2 = 0.208
ξ 3 = 0.370
ξ 4 = 0.125
Step 6
Thus, the ranking of alternatives becomes
B r a n d 3 > B r a n d 1 > B r a n d 2 > B r a n d 4

5. Conclusion

Lots of people use mobile phones every day and new technology is appearing quickly. This is making lots of companies create different kinds of mobile phones to sell. Choosing the right mobile phone can be hard because there are many criteria to consider.
The goal of this work was to include Garvin’s quality dimensions to identify the most suitable smartphone from the users’ perspective. Since people are influenced by word-of-mouth advertising and it is effective in their decisions, this source of information was used to evaluate the quality dimensions of the smartphone and its selection.
To choose the best smartphone option, we need to use MCDM techniques because it is difficult to evaluate all options with different criteria alone.
In this research, an integrated TOPSIS method was used to solve MCDM problems with IVIF information.
According to Table 3, experts who work in smartphone warranty agencies, believe that Performance, Features, and Serviceability are very important among Garvin's quality dimensions and they assigned a high weight to them.
The students influenced by word-of-mouth advertising, have evaluated the quality dimensions of 4 brands of smartphones. According to the IVIF matrix which is the mean of students’ ideas, Brand 3 is higher in all dimensions and only in Aesthetics and Durability Brand 1, has been evaluated better than Brand 3. Brand 2 is evaluated better than Brand 1 in Performance. Also, Brand 2 is equal to Brand 1 in Perceived Quality. Brand 4 is lower than other brands in all dimensions, but it is equal to Brand 2 in Aesthetics.
Considering the opinion of experts and students shown in the weighted IVIF matrix, it can be concluded that Brand 3 was superior in all aspects of quality, but the Aesthetics of Brand 1 was ranked higher than Brand 3. Brand 1 has been evaluated as higher than Brand 2 and Brand 4 in many dimensions, but in Perceived Quality Brand 2 is equal to Brand 1, and also Brand 1 in Performance and Serviceability has been evaluated as lower than Brand 2.
Brand 4 is lower than other brands in all dimensions and is equal to Brand 2 only in Aesthetics.
Finally, according to the opinion of experts and students, the mobile store can recommend brand 3 to customers because it has higher quality in many aspects. Of course, if only Aesthetics is concerned, Brand 1 is a good suggestion. After Brand 1, Brand 3 can be a good option, but in terms of Performance and Serviceability, Brand 2 has been able to attract the opinion of customers. Brand 4 was lower than other brands in all dimensions.
In general, it can be concluded that among the four smartphone brands, Brand 3 has been able to emerge as the best alternative among other smartphones in terms of quality dimensions and word-of-mouth advertising. These results can vary based on weight and customer opinion.

References

  1. Sa’ait, N.; Kanyan, A.; Nazrin, M.F. The effect of e-WOM on customer purchase intention. International Academic Research Journal of Social Science 2016, 2, 73–80. [Google Scholar]
  2. Berger, J. Word of mouth and interpersonal communication: A review and directions for future research. Journal of consumer psychology 2014, 24, 586–607. [Google Scholar] [CrossRef]
  3. Herold, K. Impact of word-of-mouth on consumer decision-making: an information processing perspective in the context of a high-involvement service. 2015.
  4. Gildin, S.Z. Understanding the power of word-of-mouth. RAM. Revista de Administração Mackenzie 2022, 4, 92–106. [Google Scholar] [CrossRef]
  5. Ahmad, N.; Vveinhardt, J.; Ahmed, R. Impact of word of mouth on consumer buying decision. European journal of business and management 2014, 6. [Google Scholar]
  6. Anggraeni, A. Effects of brand love, personality and image on word of mouth; the case of local fashion brands among young consumers. Procedia-Social and Behavioral Sciences 2015, 211, 442–447. [Google Scholar] [CrossRef]
  7. Yasin, M.; Shamim, A. Brand love: Mediating role in purchase intentions and word-of-mouth. Journal of Business and Management 2013, 7, 101–109. [Google Scholar] [CrossRef]
  8. Filieri, R. >What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of business research 2015, 68, 1261–1270. [Google Scholar] [CrossRef]
  9. Bughin, J.; Doogan, J.; Vetvik, O.J. A new way to measure word-of-mouth marketing. McKinsey Quarterly 2010, 2, 113–116. [Google Scholar]
  10. HUANG, J.H.; HSIAO, T. T.; CHEN, Y.F. The Effects of Electronic Word of Mouth on Product Judgment and Choice: The Moderating Role of the Sense of Virtual Community 1. Journal of Applied Social Psychology 2012, 42, 2326–2347. [Google Scholar] [CrossRef]
  11. Smith, R.; Ennew, C. Service quality and its impact on word-of-mouth communication in higher education. 2001: University of Nottingham in Malaysia, Division of Business and Management.
  12. Jan, M.T.; Abdullah, K.; Shafiq, A. The impact of customer satisfaction on word-of-mouth: conventional banks of Malaysia investigated. International Journal of Information Technology & Computer Science 2013, 10, 14–23. [Google Scholar]
  13. Shen, Y.; Li, S.; DeMoss, M. The effect of quantitative electronic word of mouth on consumer perceived product quality. International Journal of Management and Marketing Research 2012, 5, 19–29. [Google Scholar]
  14. Mukherjee, S.P. Quality: Domains and dimensions. 2018: Springer.
  15. Djamal, N.; Widyarto, W.O. Quality Services Measurement Using Fuzzy Service Quality (Fuzzy SERVQUAL) Method. Int. J. Eng. Res. Technol 2017, 6, 614–618. [Google Scholar]
  16. Jangkung, M.R.P.; Sudrajat, A. The Influence of Product Quality on The Purchase Decision of Wardah Cosmetics in Karawang City. Primanomics: Jurnal Ekonomi & Bisnis 2022, 20, 53–71. [Google Scholar]
  17. Garvin, D.A. Product quality: An important strategic weapon. Business horizons 1984, 27, 40–43. [Google Scholar] [CrossRef]
  18. Xu, P.; et al. Certain Concepts of Interval-Valued Intuitionistic Fuzzy Graphs with an Application. Advances in Mathematical Physics 2022, 2022. [Google Scholar] [CrossRef]
  19. Zadeh, L.A. Fuzzy sets. Information and control 1965, 8, 338–353. [Google Scholar] [CrossRef]
  20. Chen, S.-M.; Fan, K.-Y. Multiattribute decision making based on probability density functions and the variances and standard deviations of largest ranges of evaluating interval-valued intuitionistic fuzzy values. Information Sciences 2019, 490, 329–343. [Google Scholar] [CrossRef]
  21. Fu, S.; Xiao, Y.-z.; Zhou, H.-j. Interval-valued Intuitionistic Fuzzy Multi-attribute Group Decision-making Method Considering Risk Preference of Decision-makers and Its Application. 2022.
  22. Atanasov, K. Intuitionistic fuzzy sets Fuzzy sets and systems. 1986.
  23. K.Atanassov, G.G. Interval valued intuitionistic fuzzy sets. Fuzzy Sets and Systems 1989, 31, 343–349. [Google Scholar] [CrossRef]
  24. Bharati, S.K. Transportation problem with interval-valued intuitionistic fuzzy sets: impact of a new ranking. Progress in Artificial Intelligence 2021, 10, 129–145. [Google Scholar] [CrossRef]
  25. Hashemi, H.; et al. An extended compromise ratio model with an application to reservoir flood control operation under an interval-valued intuitionistic fuzzy environment. Applied Mathematical Modelling 2014, 38, 3495–3511. [Google Scholar] [CrossRef]
  26. Ballantyne, R.; Warren, A.; Nobbs, K. The evolution of brand choice. Journal of Brand Management 2006, 13, 339–352. [Google Scholar] [CrossRef]
  27. Bączkiewicz, A. MCDM based e-commerce consumer decision support tool. Procedia Computer Science 2021, 192, 4991–5002. [Google Scholar] [CrossRef]
  28. Kokoç, M.; Ersöz, S. New ranking functions for interval-valued intuitionistic fuzzy sets and their application to multi-criteria decision-making problem. Cybernetics and Information Technologies 2021, 21, 3–18. [Google Scholar] [CrossRef]
  29. Can, G.F.; Kılıç Delice, E. A task-based fuzzy integrated MCDM approach for shopping mall selection considering universal design criteria. Soft Computing 2018, 22, 7377–7397. [Google Scholar] [CrossRef]
  30. Siksnelyte-Butkiene, I.; Zavadskas, E.K.; Streimikiene, D. Multi-criteria decision-making (MCDM) for the assessment of renewable energy technologies in a household: A review. Energies 2020, 13, 1164. [Google Scholar] [CrossRef]
  31. Akyene, T. Cell phone evaluation base on entropy and TOPSIS. Interdisciplinary Journal of Research in Business 2012, 1, 9–15. [Google Scholar]
  32. Kumar, G.; Parimala, N. An integration of sentiment analysis and MCDM approach for smartphone recommendation. International Journal of Information Technology & Decision Making 2020, 19, 1037–1063. [Google Scholar]
  33. Goswami, S.; Mitra, S. Selecting the best mobile model by applying AHP-COPRAS and AHP-ARAS decision making methodology. International Journal of Data and Network Science 2020, 4, 27–42. [Google Scholar] [CrossRef]
  34. Yildiz, A.; Ergul, E.U. A two-phased multi-criteria decision-making approach for selecting the best smartphone. South African Journal of Industrial Engineering 2015, 26, 194–215. [Google Scholar] [CrossRef]
  35. Aggarwal, A.; Choudhary, C.; Mehrotra, D. Evaluation of smartphones in Indian market using EDAS. Procedia computer science 2018, 132, 236–243. [Google Scholar] [CrossRef]
  36. Goswami, S.S.; Behera, D.K. Evaluation of the best smartphone model in the market by integrating fuzzy-AHP and PROMETHEE decision-making approach. Decision 2021, 48, 71–96. [Google Scholar] [CrossRef]
  37. Popović, G.; Karabašević, D.; Pucar, Đ. Smartphone Selection based on the PIPRECIA and CoCoSo Methods. PaKSoM 2021, 467. [Google Scholar]
  38. Kumar, S.; Kumar, T.A.; Agrawal, S. Research methodology: Prioritization of new smartphones using topsis and moora. in International Conference of Advance Research & Innovation (ICARI). 2020.
  39. Kumar, R.; Channi, H.; Singh, H. Selection of mobile phone with multi criteria decision making approach: a case study. Future of Business Through Innovations. 2020, National Press Associates.
  40. Rani, P.; Mishra, A.R.; Ansari, M.D. Analysis of smartphone selection problem under interval-valued intuitionistic fuzzy ARAS and TOPSIS methods. in 2019 Fifth International Conference on Image Information Processing (ICIIP). 2019, IEEE.
  41. Srivastava, A.; et al. Comparison between Smart phones on the basis of their reliability factors. in 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions)(ICTUS). 2017, IEEE.
  42. Büyüközkan, G.; Güleryüz, S. Multi criteria group decision making approach for smart phone selection using intuitionistic fuzzy TOPSIS. International Journal of Computational Intelligence Systems 2016, 9, 709–725. [Google Scholar] [CrossRef]
  43. Belbag, S.; et al. The evaluation of smartphone brand choice: an application with the fuzzy ELECTRE I method. International Journal of Business and Management Invention 2016, 5, 55–63. [Google Scholar]
  44. Trivedi, A.; Chauhan, A.; Trivedi, V. Using fuzzy extension of hybrid MCDM techniques to evaluate smartphones on qualitative dimensions. in International Marketing Conference (MARCON 2014), IIM Kolkata. 2014.
  45. Işıklar, G.; Büyüközkan, G. Using a multi-criteria decision making approach to evaluate mobile phone alternatives. Computer Standards & Interfaces 2007, 29, 265–274. [Google Scholar]
  46. Hoe, L.C.; Mansori, S. The effects of product quality on customer satisfaction and loyalty: Evidence from Malaysian engineering industry. International Journal of Industrial Marketing 2018, 3, 20. [Google Scholar] [CrossRef]
  47. Mohd, R.S.; et al. The relationship between product quality and purchase intention: The case of Malaysia’ s national motorcycle/scooter manufacturer. African Journal of Business Management 2011, 5, 8163–8176. [Google Scholar] [CrossRef]
  48. Andri, P.; Jasfar, F.; Kristaung, R. Effect Of Product, Distribution And Service Quality on Customer Loyalty Through Customer Satisfaction At Indonesian Marketplace. Devotion: Journal of Research and Community Service 2022, 3, 321–330. [Google Scholar] [CrossRef]
  49. Chowdhury, S. Measuring the relationship between product quality dimensions & repurchase intention of smart phone: A case study on chittagong city. International Journal of Scientific & Engineering Research 2017, 8, 1031–1040. [Google Scholar]
  50. Owlia, M.S.; Aspinwall, E.M. A framework for the dimensions of quality in higher education. Quality assurance in education, 1996.
  51. Sinclair, S.A.; Hansen, B.G.; Fern, E.F. Industrial forest product quality: an empirical test of Garvin's eight quality dimensions. Wood and fiber science 1993, 66–76. [Google Scholar]
  52. Biswas, A.; Kumar, S. An integrated TOPSIS approach to MADM with interval-valued intuitionistic fuzzy settings, in Advanced Computational and Communication Paradigms. 2018, Springer. p. 533-543.
  53. Shui, X.; Li, D. A possibility based method for priorities of interval judgment matrix. Chin J Manag Sci 2003, 11, 63–65. [Google Scholar]
  54. Birenboim, A.; Shoval, N. Mobility research in the age of the smartphone. Annals of the American Association of Geographers 2016, 106, 283–291. [Google Scholar] [CrossRef]
  55. Laricchia, F. Number of smartphone users by leading countries in 2021 (in millions). 2022; Available from: https://www.statista.com/statistics/748053/worldwide-top-countries-smartphone-users/.
  56. Laricchia, F. Penetration rate of smartphones in selected countries 2021, 2022; Available from: https://www.statista.com/statistics/539395/smartphone-penetration-worldwide-by-country/.
  57. Degenhard, J. Forecast of the number of smartphone users in Iran from 2010 to 2025(in millions). 2021; Available from: https://www.statista.com/forecasts/1143925/smartphone-users-in-iran.
  58. Cochran, W.G. Sampling techniques. 1977: John Wiley & Sons.
Table 1. (MCDM) techniques and criteria used in mobile phone selection research
Table 1. (MCDM) techniques and criteria used in mobile phone selection research
Author MCDM Method criteria
1 [36] integrating fuzzy-AHP and PROMETHEE Price/ Storage capacity /RAM /Battery Screen size/ Camera/ Clock speed
2 [33] AHP-COPRAS and AHP-ARAS Price/ Internal Storage/ RAM/ Brand
3 [38] TOPSIS and MOORA Cost/ Performance/
Camera/ Screen Size/ Battery
4 [39] SAW and WPM Cost/ Weight/ RAM/ Screen pixels/ Screen size/ Storage capacity
5 [32] hybrid MCDM/AHP and TOPSIS Battery/ Speed/ Display/ Camera
6 [40] IVIF-ARAS and IVIF-TOPSIS Price/ Battery Life/ Operating System/ Storage/ Network
7 [35] EDAS Camera Quality/ Internal storage/ Battery backup/ Ram/ Processor/ Screen Performance/
Non-favorable/ Weight/ cost
8 [41] AHP Operating System/ Memory/
Display/ Touchscreen/ Data Speed/ Battery Life/ Camera/ Processor/ Speaker
9 [42] Intuitionistic Fuzzy TOPSIS Durability/ Battery life/ Changeable parts/Dimensions/ Memory capacity/ Processor speed/ Internet connection
speed (4G/5G)/ Camera specifications/ Operation system easiness/ Variety of applications/ Brand choice/ Prestige/ Fashionable style, Aesthetics/ Personal information and media security/ Price/ Warranty /Service Availability
10 [43] Fuzzy ELECTRE I Method Price/ Screen size/ Weight/ Ease of use/ Resolution/ Design/ Durability
11 [34] two-phased MCDM/ANP/GCI Durability/ Screen size/ Thickness/ Weight/ Battery power/ Built-in memory/ Camera/ Operating system/ Pixel density/ Processor type/ RAM/ Standby time/ Talk time/ Aesthetic/ Brand/ Cost/ Ease of use
12 [44] hybrid MCDM/ AHP/ TOPSIS Brand value/ Camera quality/ Battery life/ Price/ User friendliness of operating system/ Sensitivity of touch panel
13 [31] Entropy and TOPSIS Dimensions/ Weight/ Screen Size/ Memory/ RAM/ Speed/ Blue Tooth/ Camera/ OS operating system/ CPU/ Battery/ Price
14 [45] AHP and TOPSIS Basic requirements/ Physical characteristics/ Technical features /Functionality/ Brand choice/ Customer excitement
Table 2. Linguistic terms for the relative importance of criteria.
Table 2. Linguistic terms for the relative importance of criteria.
Linguistic terms Interval-valued intuitionisticfuzzy numbers
Very important (VI) < 0.80,0.90 , 0.05,0.10 >
Important (I) < 0.60,0.75 , 0.10,0.20 >
Moderate Importance (MI) < 0.30,0.50 , 0.25,0.45 >
Unimportant (UI) < 0.20,0.35 , 0.45,0.60 >
Very unimportant (VUI) < 0.00,0.10 , 0.70,0.90 >
Table 3. experts’ opinion of the importance of criteria and their average.
Table 3. experts’ opinion of the importance of criteria and their average.
Ex1 Ex2 … Ex7 mean
Performance VI VI I < 0.75,0.86 , 0.06,0.13 >
Features VI I VI < 0.68,0.82 , 0.08,0.16 >
Reliability I MI I < 0.39,0.57 , 0.22,0.38 >
Conformance MI UI VUI < 0.18,0.34 , 0.43,0.62 >
Durability I MI MI < 0.42,0.60 , 0.18,0.34 >
Serviceability VI I VI < 0.62,0.77 , 0.11,0.21 >
Aesthetics VUI UI VUI < 0.35,0.52 , 0.26,0.43 >
Perceived Quality I MI I < 0.52,0.69 , 0.14,0.26 >
Table 4. Students' evaluation of the quality dimensions of 4 mobile phone brands.
Table 4. Students' evaluation of the quality dimensions of 4 mobile phone brands.
Performance Features Reliability Conformance
B1 B2 B3 B4 B1 B2 B3 B4 B1 B2 B3 B4 B1 B2 B3 B4
Student 1 VG VG EG G EG VG EG MB VG G EG MG VG MG EG MB
Student 2 G G VG MG VG G EG F VG MG VG MB VG MB EG EB
Student 3 F G VG F VG MG VG MB G F G B G B G VB
Student 4 MG MG VG B G MB MG F MG B G EB MG VG F EB
Student 5 VG VG F EB G EB MG VG VG MG G G VG MG VG VB
Student 6 EG MG VG VG G MB MG MG VG MG VG MG VG G EG B
Student 7 G F MG VB VG MG VG MB MG VG F EB EG VG EG MG
Student 8 VG MB G MB F VG F G G VG F VG G G G MG
Student 9 VG EG MB VG G G VG B VG VG EG MB VG B MG G
Student 10 VG MG VG G EG MG VG VG VG G EG VB VG EG MG VG
Student 11 EG MG EG MG VG F VG F VG MG EG G VG VG EG VG
Student 12 F VG MG VG VB MG F MG G B G B VG MG VG MB
Student 13 G G VG B MG MG G VB MG F MG VB G VG F G
⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞
Student349 VG MG EG MB G VG EG MB MG F MG VB EG VG EG MB
Student350 VG VG EG G EG VG EG MG VG G EG MG EG G EG MB
Durability Serviceability Aesthetics Perceived Quality
B1 B2 B3 B4 B1 B2 B3 B4 B1 B2 B3 B4 B1 B2 B3 B4
Student 1 EG VG EG MG VG VG EG EB EG MG EG MG VG VG EG MB
Student 2 VG G EG VB G VG EG MB VG MG VG MG G G EG MG
Student 3 G G VG B MG G G EB VG F VG F G G VG B
Student 4 G MG VG VB MG F MG VB VG MB G MB MG MG G VB
Student 5 G B G B VG B G MB G MB MG F G F G B
Student 6 MG B G EB VG MG VG G F G VG F VG MG VG MB
Student 7 VG VB MG F MG G VG F G EG MG VG G EG VG EG
Student 8 EG MG EG MG F VG MG VG G MG EG MG EG MG VG VG
Student 9 VG MG VG F G MB MG F MG MG G VB MG G EG VG
Student 10 EG G EG MB VG MG VG MB VG MG EG MB MG F MG VB
Student 11 EG G EG MB VG G EG B VG B MG G G VG EG MB
Student 12 VG MG VG VB G G G MG G G VG B VG F VG F
Student 13 VG F VG F VG MG VG G EG MG EG MG G G VG B
⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞ ⁞
Student349 VG G VG MG VG MB VG F VG MG EG G MG F G EB
Student350 EG EG EG F VG VG EG VG EG VG EG VG F F EG F
Table 5. Linguistic terms for the rating of alternatives.
Table 5. Linguistic terms for the rating of alternatives.
Linguistic terms Interval-valued intuitionistic fuzzy numbers
Extremely Bad (EB) < 0.02,0.05 , 0.90,0.95 >
Very Bad (VB) < 0.10,0.15 , 0.70,0.75 >
Bad (B) < 0.25,0.30 , 0.55,0.60 >
Moderately Bad (MB) < 0.40,0.45 , 0.45,0.50 >
Fair(F) < 0.50,0.55 , 0.35,0.40 >
Moderately Good (MG) < 0.60,0.65 , 0.25,0.30 >
Good (G) < 0.70,0.75 , 0.15,0.20 >
Very Good (VG) < 0.80,0.85 , 0.05,0.10 >
Extremely Good (EG) < 0.90,0.95 , 0.02,0.05 >
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

facebook logotwitter logolinkedin logo
MDPI Initiatives
Important Links
Subscribe

Disclaimer

Terms of Use

Privacy Policy

© 2024 MDPI (Basel, Switzerland) unless otherwise stated