International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 04, April 2019, pp. 405-416. Article ID: IJMET_10_04_040
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=4
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication Scopus Indexed
Chanida Phittayanon and Vichai Rungreunganun
Department of Industrial Engineering, Faculty of Engineering
King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand.
ABSTRACT
Promoting export-based industry is a challenging task. Industrial competitiveness is frequently used as the overall indicator. The article offers a novel result of priority weights of Thai silver jewelry industry.
Factors that may influence the industry were collected by literature reviews. Then, industry experts and entrepreneurs choose and classify the factors into performance drivers. There are 32 factors organized into 7 categories or drivers. Next, the analytical hierarchy process (AHP) is utilized to calculate priority weights of factors.
As a result, the sales-and-marketing driver is the most important driver, and laborintensive tasks dominate the competitiveness of the industry. On the other hand, the research community could use the priority weight of factors in their research and develop policies to promote the industry.
Keywords : Silver Jewelry, AHP, Supply Chain, Competitiveness.
Cite this Article : Chanida Phittayanon and Vichai Rungreunganun, Analyzing Factors
Influencing Industrial Competitiveness of Thai Silver Jewelry Industry using Analytic
Hierarchy Process, International Journal of Mechanical Engineering and Technology ,
10(4), 2019, pp. 405-416. http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=4
The jewelry industry is a large part of Thailand economic [1] which employed several thousand high-skill workers and millions of people in related industries [2]. Thailand is one of the top exporters of silver jewelry (HS 711311) in the global market [3]. Competing in the global jewelry market is a key challenge for Thai silver jewelry industrial.
The jewelry industry is very dynamic. The price of raw materials is very fluctuated, especially for precious metal. The jewelry product is a fashion product with a short life cycle.
http://www.iaeme.com/IJMET/index.asp 405 editor@iaeme.com

Chanida Phittayanon and Vichai Rungreunganun
As a result, the manufacturing process is a fine process with low volume production. Therefore, it relies heavily on high-skill labor processes.
To be more competitive, manufacturers, wholesalers, retailers, and traders of Thai jewelry industry have established the Thai Gem and Jewelry Traders Association (TGJTA) [4] to promote the industry. Similarly, the Thai government also establish the Gem and Jewelry
Institute of Thailand (GIT) [5]. These private and public organizations have invested in several projects to overcome challenges and increase competitive advantages among its members and the industry as a whole.
However, there are still debate among experts on the effectiveness of investment to promote the industry. The overall industrial competitiveness is a generic indicator that used in several industries [6]–[10]. However, there is no commonly agreed method to evaluate IC for the silver jewelry industry. This study uses the analytic hierarchy process (AHP) technique to develop a model to evaluate industrial competitiveness.
In this work, we collect factors that influencing the silver jewelry competitiveness from literature and use the AHP method to determine the priority weights of the factors. This article
is organized as followed. The literature review is carried out in Section 0. The AHP
methodology that we used in this work is discussed in Section 0. The resulting AHP evaluation
is shown in Section 0. A discussion of AHP priority weights is presented in Section 0. Then, a conclusion is presented in Section 0.
Companies always need to balance resources between various business objectives, i.e. problem solving for a short-term benefit or investing in resources for long-term growth. This results in a complex relationship between short-term and long-term growth. In addition, companies also need to overcome challenges outside of the company. Industrial competitiveness has become a major business indicator for entrepreneurs, economists, and industrial engineers.
Evaluating industrial competitiveness is a challenging task that relies heavily on expert in the business. The Analytic Hierarchy Process (AHP) has become a popular technique to combine difference opinion from many experts [6]–[10]. The AHP technique also provides a statistical tool to check for consistency in the expert opinion.
An AHP method has been used to evaluate industrial competitiveness for a generic company by considering only the marketing, company resources, and environment factors [6]. Applying
AHP with the Potter diamond model has been proposed to evaluate IC [7]. Similarly, AHP was used to help in a decision-making process of investing in automation robots in the automotive industry [8], [9]. In sustainable manufacturing, the same technique has been used by emphasizing on Green technology [10].
In Thailand, there are a lot of firms in the jewelry manufacturing segment. Most of them entered this industry by the trading of gemstone or other jewelry. A study of Thailand silver jewelry industry [11] has shown that the supply chain structure of Thailand silvery jewelry industry can be divided into 4
stages as shown in Figure 1 .
http://www.iaeme.com/IJMET/index.asp 406 editor@iaeme.com

Analyzing Factors Influencing Industrial Competitiveness of Thai Silver Jewelry Industry using
Analytic Hierarchy Process
Figure 1 Supply Chain of Thai silver jewelry industry
The upstream process involves raw material acquisitions of precious metals and gemstones.
In Thailand, jewelry’s raw materials are mainly imported. This is the lowest value-adding process in the supply chain because raw materials must be imported.
The midstream process includes processing of raw materials. These processes include cutting, polishing, and processing of gemstones and precious metals. These are a medium valueadding process.
The downstream process is the manufacturing of jewelry which can be divided into two groups. The first group is handmade jewelry manufacturers that focus on the high-end market.
This is a high value-adding process. The other group is machine-based manufacturers that mass produce for the high-volume market. This is a low value-adding process.
The distribution process includes domestic retailer with its brand and export to the global market. This process is a high value-added process.
The Thai Ministry of Industry has announced a master development plan [12] . The master plan stated three priority factor-categories to promote the industry. The three categories are ( 1) manage raw material cost, ( 2) create a measure to improve marketing channels, and ( 3) improve supporting structure to promote the industry.
3.1.1. Manufacturing Segment
The cost structure of Thailand jewelry manufacturers has been studied in 2003 [13] . Major parts of raw material cost are for gemstones and precious metals which are about 35% and 30% , respectively. While the total cost of raw material is about 70%.
Most silver jewelry manufacturers in Thailand are small and medium businesses [1] . The composition is as followed. 58% of them are family businesses, 20% of them are small businesses, 5.3% of them are medium businesses, and only 0.8% of them are large businesses.
It is also found that most jewelry businesses are inherited from their parents [2] . And, a lot of jewelry manufacturers are original equipment manufacturers (OEM) for a large global brand
[2] .
A study of problems in Thailand jewelry industry in 2002 [14] has suggested some measures to improve the industry. These measures included improving labor competency and jewelry design capability. http://www.iaeme.com/IJMET/index.asp 407 editor@iaeme.com

Chanida Phittayanon and Vichai Rungreunganun
3.1.2. Surrounding Factors influencing the industry
Lacking domestic raw material is the main challenge in the upstream process. Thai silver jewelry needs to import most of the precious metal and gemstone [2], [14]–[16] . However, labor competency is the main challenge in midstream and downstream processes.
Considering the economy, the market ratio of Thai silver jewelry industry is approximately
80% exported and 20% domestic sales. Therefore, the domestic economic situation would not have a major impact on the industry.
The Thai government has influenced the industry by issuing some measures. For example, the establishment of the jewelry industrial development section within the Ministry of Industry and the Gem and Jewelry Institute of Thailand [5] . There are some tax measures to promote the industry, i.e. conditional VAT exempt, 1%withholding tax exempt, no import tax, and VAT exempt for rough gemstones (exclude diamond and peal).
In 1971 , Saaty proposed the AHP technique [17] which is a multi-factor decision-making tool.
Major advantages of AHP include an ability for solving unstructured problems with a high level of complexity. This technique works by divide the problem into multiple hierarchy levels. The hierarchy structure is set as followed. The top level is designated as the objective, while lower levels are for drivers and factors. The priority weight within each hierarchy level is calculated using predetermined measurement and judgment of experts using pairwise comparisons. Then, the priority ranking and weight values are generated.
To determine relative priorities of factors that affect the industry, this research uses the AHP technique which consists of the following steps.
Step 1: Define the objective
Step 2: Determine lower-level factors
Step 3: Construct a hierarchy structure of factors
Step 4: Survey of expert judgments of pairwise comparisons
Step 5: Determine priority weights of factors
Step 6: Check for consistency of pairwise comparisons
The objective of the AHP model is set to determine the industrial competitiveness (IC) of
Thailand silver jewelry industry. This is to improve the competitiveness and prioritize resource allocation to promote the industrial.
A list of factors that may be influencing the industry was obtained from a literature review.
Then, the list is confirmed by a focus-group discussion. As a result, a total of 32 factors influencing the industrial were identified. Next, factors were divided into seven factorcategories which can be considered as competitive drivers. Seven drivers are business partnerships (BP), internal management (IM), manufacturing (MF), product design (PD), standard and certification (SC), sales and marketing (SM), and surrounding factor (SF).
Drivers and factors that were identified in the previous step were organized in a hierarchy structure which comprises of the objective, drivers, and factors. Therefore, a three-level
hierarchy structure was developed as presented in Figure 2 .
The top level of the model is the http://www.iaeme.com/IJMET/index.asp 408 editor@iaeme.com

Analyzing Factors Influencing Industrial Competitiveness of Thai Silver Jewelry Industry using
Analytic Hierarchy Process overall objective which is the industrial competitiveness. The second level represents seven categories of competitiveness drivers. Finally, the third level contains factors influencing the industry of each category. This model presents the operational relationship between the overall objective, drivers, and factors of the competitiveness that relevant to silver jewelry industrial in
Thailand.
Figure 2 The AHP model for industrial competitiveness of Thai silver jewelry industry.
This step performs a comprehensive analysis of industrial competitiveness. By collecting of expert judgments and evaluation. Experts were carefully chosen from the industry. In this study, a group of 43 experts and entrepreneurs from Thailand jewelry firms were invited. First, they were briefed about the research and the AHP method. Then, they were interviewed to assess pairwise comparisons among seven drivers based on their potential to improve industrial competitiveness. Then, each factor in the third level is compared with other factors within the same category. As proposed by Saaty [17], the pairwise comparison uses a nine-point scale of
relative preference which is described in Table 1 .
Table 1 Relative preference for a pairwise comparison [17]
Scale
9
7
5
3
Level of Relative Importance
Vastly more importance
Largely more importance
Much more importance
More importance
1 Same importance
8, 6, 4, 2 Intermediate level
By utilizing the scale shown in Table 1, eight pairwise comparison matrices for the overall
objective and seven drivers were constructed. Results of the comparison are put into a matrix
form. An example of the matrix of sales and marketing driver is shown in Table 2 in which the
cell data 𝑎 𝑖,𝑗
represents the relative importance of the 𝑖 factor with respected to the 𝑗 factor and can be computed using geometric mean of all responses as: http://www.iaeme.com/IJMET/index.asp 409 editor@iaeme.com

Chanida Phittayanon and Vichai Rungreunganun 𝑛 𝑟 𝑎 𝑖,𝑗
= (∏ 𝑎 𝑖,𝑗,𝑟
) 𝑟=1
1 𝑛𝑟
=
1 𝑎 𝑗,𝑖
, where index 𝑟 = 1 , 2 , 3 , … , 𝑛 𝑟
refers to each response from an expert, and 𝑛 𝑟
is the number of responses. Therefore, the pairwise comparison matrix 𝑃 can be constructed as:
𝑃 =
1 𝑎1,2
1 𝑎1,3
1
⋮
[ 𝑎1,𝑛
1 𝑎
1,2
1
1 𝑎2,3
⋮
1 𝑎2,𝑛 𝑎 𝑎
1,3
2,3
… 𝑎
1,𝑛
… 𝑎
2,𝑛
1 … 𝑎
3,𝑛
1
⋮ 𝑎3,𝑛
⋱ ⋮
… 1 ]
, where 𝑛 is the number of drivers or factors in the same category.
For instance, if an expert evaluates that the “place strategy” factor is more important than
“after-sales service” the factor, the value of 3 was chosen according to the scale of relative
preferences in Table 1 .
Therefore, reciprocally the “after-sales service” is 1/3 times less important than the “place strategy” factor, i.e. 𝑎 𝑖,𝑗
= 1 / 𝑎 𝑗,𝑖
. Thus, a pairwise comparison matrix can be created in a similar manner. The example of a pairwise comparison matrix for a sales-
and-marketing driver is shown in Table 2 .
Table 2
Pairwise comparison matrix (P) of the sales-and-marketing driver
Sales and marketing driver
Active marketing campaign (F1)
Price strategy (F2)
Place strategy (F3)
Product Brand (F4)
After-sales service/warranty (F5)
Total
F1
1
F2
0.8970 1
1.3127 0.8523
F3
1.1149 0.7618 0.9755 1.0224
1.1732
1
F4
0.8160
1.0809
1.0251 1.2256 0.9252 1
0.9781 0.9232 0.7785 0.8940
F5
1.0832
1.2845
1.1186
1
5.2128 5.1160 4.6387 4.7663 5.5087
This step is to calculate the priority weight of all drivers and factors. First, the pairwise comparison matrix 𝑃 is normalized by dividing each cell in column 𝑖 by a summation of all cells in column 𝑖 . This generates a normalized pairwise comparison matrix 𝑃
Table 3, in which a summation of cells
𝑃 norm 𝑖,𝑗
in each column is 1. The 𝑃 norm norm
as shown in
is given by: 𝑛
𝐴 𝑗
= ∑ 𝑎 𝑖,𝑗
, 𝑖=1
𝑃 norm
=
1
𝐴
1
1 𝑎1,2𝐴1
1 𝑎1,3𝐴1
1
⋮
[ 𝑎1,𝑛𝐴1
𝑃 norm 𝑖,𝑗
=
𝑃 𝑖,𝑗
𝐴 𝑖
, 𝑎1,2
𝐴2
1
𝐴2
1 𝑎2,3𝐴2
⋮
1 𝑎2,𝑛𝐴2 𝑎1,3
𝐴3 𝑎2,3
𝐴3
1
𝐴3
1
⋮ 𝑎3,𝑛𝐴3
… 𝑎1,𝑛
𝐴𝑛
…
… 𝑎2,𝑛
𝐴𝑛 𝑎3,𝑛
𝐴𝑛
⋱ ⋮
…
1
𝐴𝑛
,
] http://www.iaeme.com/IJMET/index.asp 410 editor@iaeme.com

Analyzing Factors Influencing Industrial Competitiveness of Thai Silver Jewelry Industry using
Analytic Hierarchy Process where 𝑛 is the number of drivers or factors in the same category, 𝐴 𝑖 in a column of pairwise comparison matrix 𝑃 .
is a summation of cells
Finally, a priority weights vector 𝑊
were produced (shown in the last column in Table 3 )
by averaging of cells in row 𝑖 of 𝑃 norm
as:
𝑊 𝑖
= 𝑛
1 𝑛
∑ 𝑃 𝑗=1 norm 𝑖,𝑗 where 𝑛 is the number of drivers or factors in the same category.
Table 3 Normalized pairwise comparison matrix ( 𝑃 norm
) of the sales-and-marketing driver, and the corresponding weight vector 𝑊
Sales and marketing driver
Active marketing campaign (F1)
Price strategy (F2)
Place strategy (F3)
Product Brand (F4)
After-sales service/warranty (F5)
F1 F2 F3 F4 F5 Weight
0.1918 0.2179 0.1642 0.2047 0.1856 0.1929
0.1721 0.1955 0.2529 0.1712 0.1966 0.1977
0.2518 0.1666 0.2156 0.2268 0.2332 0.2188
0.1966 0.2396 0.1995 0.2098 0.2031 0.2097
0.1876 0.1805 0.1678 0.1876 0.1815 0.1810
Priority weight is the relative importance of influence on the objective of a factor in relation
to other factors. From Table 3, priority weights are ranked with the highest priority given to
“place strategy” factor with weight value of 0.2188
, followed by “product brand” factor with the weight value of 0.2097
, then “price strategy” factor with the weight value of 0.1977
, then
“active marketing campaign” factor with the weight value of 0.1929
, and then “after-sales service/warranty” factor with the weight value of 0.1810.
This step is to validate whether the pairs of factors are evaluated consistently or not. This is important because it is possible that some evaluators may provide inconsistence judgments. The
AHP technique uses the consistency ratio ( 𝐶𝑅 ) to check whether a factor can be used for the decision-making process. The 𝐶𝑅 is defined as the ratio of the consistency of the results being tested (called Consistency Index ( 𝐶𝐼 )) over the consistency of random numbers (called Random
Index ( 𝑅𝐼 )). Following the AHP method [17], an appropriate value of 𝑅𝐼 can be selected from
Table 4 .
The formula to calculate 𝐶𝑅 and 𝐶𝐼 are given by:
𝐶𝑅 =
𝐶𝐼
𝑅𝐼
,
𝐶𝐼 = 𝜆 max
− 𝑛 𝑛 − 1
, where 𝑛 is the number of drivers or factors in the same category and 𝜆 max
is the maximum value of the Eigenvector which can be calculated by: 𝜆 max
= max(𝜆 𝑖
) , 𝜆 𝑖
= 𝛿 𝑖
𝑊 𝑖
, 𝛿 = 𝑃𝑊 where 𝛿 is a matrix multiplication result between the pairwise comparison matrix 𝑃 and the priority weight vector 𝑊 , 𝜆 is an Eigenvector, and 𝑖 = 1 , 2 , 3 , … , 𝑛 . http://www.iaeme.com/IJMET/index.asp 411 editor@iaeme.com

Chanida Phittayanon and Vichai Rungreunganun
Table 4 Consistency ratio of random numbers [17]
Size of matrix ( 𝒏 ) Random index ( 𝑹𝑰 )
3
4
5
0.58
0.90
1.12
6
7
1.24
1.32
Table 5 shows an example calculation of the sales-and-marketing driver. Values of
𝛿 ,
Eigenvector 𝜆 are shown in the column corresponding to their factors. The 𝜆 max
is the largest value of the Eigenvector 𝜆 are used to calculate 𝐶𝐼 . The 𝑅𝐼 of 1.12
was selected from Table 4 .
Finally, the 𝐶𝑅 value of 0.007017
was obtained.
If expert judgments are consistent enough to provide a meaningful estimation of priority weights, the 𝐶𝑅 value will be small. In the AHP technique, a typical threshold value is 0.1.
Hence, if the 𝐶𝑅 value is less than 0.1
, the degree of consistency is satisfactory. Otherwise, there might be serious inconsistencies, and priority weights might not provide meaningful results, and the evaluation should be reviewed. For example, in the sales-and-marketing driver, the 𝐶𝑅 value is 0.007017. Therefore, the degree of consistency can be considered acceptable
( 𝐶𝑅 ≪ 0.1).
Table 5 Example of calculation of consistency ratio
Sales and marketing driver 𝜹 𝝀
Active marketing campaign (F1) 0.9695 5.0273
Price strategy (F2) 0.9945 5.0314
Place strategy (F3)
Product Brand (F4)
1.0996 5.0257
1.0545 5.0286
After-sales service/warranty (F5) 0.9099 5.0270 𝜆 max
= 5.031437
Notes: 𝐶𝐼 =0.007859, 𝑅𝐼 =1.12 for 𝑛 =5, 𝐶𝑅 =0.007017
Similarly, pairwise comparisons for the objective and other drivers were performed. Their priority weights are calculated. And, their degrees of consistency are checked. Priority weight
results of pairwise comparisons of the objective and seven drivers are shown in Section 0 .
From expert judgments, we check the consistency ratio to validate if evaluators make
consistent assessments. The consistency results of indicators and drivers are shown in Table 6 .
As shown in the table, 𝐶𝑅 values of all indicators and drivers are less than 0.1
, which mean that the survey is consistent.
Table 6 Consistency validation of the objective and drivers
Objective/Driver
Competitiveness
Business Partnership
Internal Management
Manufacturing
Product Design
Standard and Certificate
Sales and Marketing
Surrounding Factors 𝒏 𝑪𝑰 𝑹𝑰 𝑪𝑹
7 0.02283 1.32 0.01729
3 0.02999 0.58 0.05172
5 0.05588 1.12 0.04989
5 0.04677 1.12 0.04176
5 0.04111 1.12 0.03670
4 0.01777 0.90 0.01974
5 0.00786 1.12 0.00702
5 0.00447 1.12 0.00399 http://www.iaeme.com/IJMET/index.asp 412 editor@iaeme.com

Analyzing Factors Influencing Industrial Competitiveness of Thai Silver Jewelry Industry using
Analytic Hierarchy Process
The AHP hierarchy structure is shown in Figure 2 .
Priority weights of the competitive indicator
of silvery jewelry industry are shown in Table 7 .
Similarly, the weights of factors on each driver
are shown in Table 8
to Table 14 .
As shown in Table 7, the sales-and-marketing is the most important driver at
24.5% , followed by, the product-design is at 17.2%. Then, the manufacturing driver is at 15.1%.
Table 7 Weights of the Silver Jewelry Competitiveness
Driver Weight
Business Partnership (BP) 0.068925
Internal Management (IM) 0.146062
Manufacturing (MF) 0.151112
Product Design (PD) 0.172213
Standard and Certificate (SC) 0.147543
Sales and Marketing (SM) 0.245540
Table 8 Weights of Business Partnership Factors
BP Driver
Direct business partnership between raw material supplier and manufacturer
Weight
0.309486
Marketing strategies in managing manufacturer and foreign partner
0.354798
Branding development with foreign partner 0.335715
Table 9 Weights of Internal Management Factors
IM Driver
Ability in internal management
Weight
0.209013
Skill and ability development of employee 0.301368
Inventory Management 0.138480
Distribution and Transportation
Liquidity of Equity
0.109858
0.241281
Table 10 Weights of Manufacturing Factors
MF Driver Weight
Improvement of production technology 0.200045
Mold and die manufacturing technology 0.112691
Labor proficiency in production
Variety of raw material
Cost of raw material
0.260173
0.146506
0.280585
Table 11 Weights of Product Design Factors
PD Driver Weight
R&D in jewelry design 0.236098
Packaging design 0.116260
Design technology 0.153457
Design ability
Product variety
0.268515
0.225669 http://www.iaeme.com/IJMET/index.asp 413 editor@iaeme.com

Chanida Phittayanon and Vichai Rungreunganun
Table 12 Weights of Standard and Certificate Factors
SC Driver Weight
Improvement to standard level 0.152091
Obtained certification/standard 0.146603
High-quality product
Quality of raw material
0.357133
0.344173
Table 13
Weights of Sales and Marketing Factors
SM Driver Weight
Active marketing campaign 0.192850
Price strategy
Place strategy
0.197654
0.215791
Product brand 0.209703
After-sales service/warranty 0.181001
Table 14
Weights of Surrounding Factors
SF Driver
Government policies
Weight
0.127892
Domestic economic situation 0.123164
Foreign economic situation 0.250157
Consumer purchasing power 0.318467
Currency exchange risk 0.180320
As shown in Table 7, it is found that the top three factor-categories (drivers) are (
1) sales and marketing, ( 2) product design, and ( 3) manufacturing. These top-three drivers contribute over
56% of the competitive indicator. These top drivers are all labor-intensive processes in the Thai silver jewelry industry. Therefore, it is very important to promote labor competency. Although this is a piece of common knowledge in the industry, this insight is confirmed by the result of this work.
By multiplying priority weights of 32 factors with the corresponding weight of its driver,
impacts of each factor on the industrial competitiveness can be derived. Table 15 shows the top
10 factors with the highest impact values.
Table 15 Impact of top 10 factors
Factor
Place strategy
High-quality product
Product Brand
Impact
5.37%
5.27%
5.15%
Quality of raw material
Price strategy
Active marketing campaign
Design ability
5.08%
4.85%
4.74%
4.62%
After-sales service/warranty 4.44%
Skill and ability development of employee 4.40% http://www.iaeme.com/IJMET/index.asp 414 editor@iaeme.com

Analyzing Factors Influencing Industrial Competitiveness of Thai Silver Jewelry Industry using
Analytic Hierarchy Process
Evaluating industry competitiveness is a complex task. In this work, an AHP model has been proposed to evaluate silver jewelry industrial competitiveness. Factors influencing the industry were collected from literature and focus group interview. Then, an expert judgment survey has been conducted using the AHP technique to determine the quantitative priority of factors. In this AHP analysis, all of drivers and factors pass the consistency validation. In addition, industry experts have reviewed and agreed with priority weights.
Individual firms could use the proposed method to evaluate their competitiveness and validate the proposed model by comparing the competitive indicator with revenue or profit over a period of time. In future works, this AHP model could be used to develop a system dynamic
(SD) model and use it to simulate impacts of policy and measure to the industrial.
Authors would like to acknowledge the Thai Gem and Jewelry Traders Association (TGJTA) for suggesting experts in the industry. Similarly, authors greatly appreciate the help of 43 experts and entrepreneurs of Thai silver jewelry industry for their time and effort completing a lengthy questionnaire for pairwise comparisons. Authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Authors received no financial support for the research, authorship, and/or publication of this article.
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