Enhancing Organizational Effectiveness through Job Evaluation in

Manufacturing: A Scoring Method with Fuzzy ARAS Approach

SAFIYE TURGAY1, RECEP YILMAZ2

1Department of Industrial Engineering

Faculty of Engineering

Sakarya University

54187, Esentepe Campus Serdivan-Sakarya

TURKEY

2Department of Business

Faculty of Business

Sakarya University

54187, Esentepe Campus Serdivan-Sakarya

TURKEY

Abstract: - Job evaluation is a critical process for organizations to enhance organizational effectiveness by

establishing fair and equitable compensation structures and aligning job roles with strategic goals. This study

focuses on the manufacturing industry and aims to explore how job evaluation can be optimized through the

integration of the fuzzy ARAS approach. By combining method, which assigns numerical scores to job factors,

and factor analysis, which identifies underlying dimensions in a set of variables, this research proposes a

comprehensive approach to job evaluation in manufacturing. This study contributes to the existing body of

knowledge by offering a novel approach to job evaluation in the manufacturing industry. Research findings in

practice can support HR professionals and organizational leaders in improving job evaluation practices, which

can ultimately contribute to improved organizational performance and competitiveness in the manufacturing

sector. Future research areas include investigating the extent to which the proposed approach is applicable in

different manufacturing sub-sectors and assessing its long-term impact on organizational effectiveness.

Key Words: Job Evaluation, Fuzzy AHP, Organizational Performance, Human Resources, Fuzzy ARAS,

Scoring, Effectiveness

Received: August 24, 2023. Revised: February 23, 2024. Accepted: March 9, 2024. Published: May 16, 2024.

1 Introduction

In the job evaluation process, the processes of

analyzing the skills and capacities of the employees

and the work environment of the organizations in

the production sector have been successfully

handled and contributed to increasing organizational

effectiveness. The job evaluation system in this

study will enable the development of a fair

remuneration policy in the relative evaluation of

different job positions. In the manufacturing sector,

it is very important to develop an effective job

evaluation model due to the complexity of job roles,

technical skills and specialization.

Model was developed by integrating the fuzzy

ARAS method with the scoring method and the

factor analysis approach. The selection of scoring

method is through a scoring that numerically gives a

importance factor to the skills, responsibilities and

working conditions in job description phase of job

modeling. Through factor analysis, which is

conducting the dimensional delineation of the

factors in the variable set, you are improving the

precision of the job evaluation process comparing to

just an overall job analysis. The purpose of this

study is to build a useful fuzzy ARAS method and

to interweave it with the scoring system of the job

evaluation method using the factor analysis in the

manufacturing sector. This study targets to generate

an accomplishing model that could be useful in

various kinds of human resource management

sectors.

Job evaluation is an issue that is directly related to

employee satisfaction in organizations and

institutions. In this study, the fuzzy ARAS method

is integrated with scoring and factor analysis to

improve job evaluation processes and to define job

roles more comprehensively. This study will assist

HR managers and organizational leaders in the

process of analysing the organizational performance

of the manufacturing sector. This proposed

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approach can be easily applied to a wide range of

sectors within the broad spectrum of the

manufacturing sector. This study also enables the

activation of organizational effectiveness.

The following sections of the study include the

methodology used, the findings and analyses

obtained and the literature review covering business

evaluation processes through the integration of

scoring method and factor analysis, the

methodology in the next section, the case study in

Section 4, and the conclusion and recommendations

in the last section.

2 Literature Survey

Studies on the scoring method and factor analysis

have been examined in order to make job

descriptions and develop job and wage evaluation

policies in the manufacturing sector and other

business lines. According to the findings obtained

from the literature, it is understood that scoring and

factor analysis make significant contributions to

increasing work productivity. This integrated

structure was analyzed by using scoring and factor

analysis methods together with the fuzzy ARAS

method. In this study, a comprehensive application

about the structure of the proposed model is given in

the light of the findings obtained from the literature.

In particular, it is seen that these two methods,

scoring and factor analysis methods, have not been

considered together with MCDM methods and there

is a gap in the literature.

In their investigation, Kaur and Kanoge examined

organizational performance research and position

evaluation practices in manufacturing organizations

(Kaur and Kanoge, 2021). They studied the

evaluation of task processes by involving factors

analysis and scoring tools. Nazarian and co-authors

in their study came up with the integrated model (in

the service system sector) [2]. Eventually,

organizational policy regulation and business

operations efficiency rose at the end of study by

explaining this fact. Another example is eco-

gardening as it is the best situation to provide

employees with healthcare facilities while keeping

them mentally healthy [3]. Organizational structure

and effectiveness have been widely studied with

numerous researches on the subject (Factor Analysis

[4], [5], [6], [7] and [8]).

Behera et al. considered a manufacturing firm as

their area of study according to [8]. The phase of

Nadiri and Tanova (2010) [10] used analysis

technique tools (scoring values & factor analysis) to

strengthen the organizational effectiveness in the

manufacturing sector. In this study, they evaluated

the correlation between work force effectiveness,

participation of the employees in the planning and

realization of the company targets and customer

satisfaction with the outcome of the job evaluation

[11], [12]. The researchers use different models and

techniques of scoring and factor analysis [11[, [12],

[13], [14], [15], [16], [17] ] to study the several

applications of these methods in the manufacturing

sector.

Besides determines the efficiency of the

comprehensive methods employed with the

technique both the rating and factor analysis in

enhancement organizational performance in the

manufacturing sector. Job evaluation is not only

concerned with the technical aspects of

organizational performance but also links with the

people management factors, like performance

management, talent management, employee

satisfaction and strategic decision. These studies

always add the subject to the development of its

philosophy and understanding by examining the

losses and incomes that can be identified or accrued

in the sector of manufacturing if the integrated

approaches are practiced.

The reliant outputs provide the insight of the fact

that job evaluation in the manufacturing sector

mapping the employee effectiveness using scoring

method and the factor approach makes a lot of

sense. They critically analyze certain elements of

job evaluation that has great implications on the

firms operation, including performance assessment,

talent development, employee satisfaction and

strategic decision. It is interesting to note that these

studies provide useful information on the

advantages and drawbacks of employing the

integrated system within the framework of job

evaluation in manufacturing industries.

3 The ARAS Approach for Fuzzy

Factor Analysis

The particular area of consideration of fuzzy set

theory is the construction of mathematical

instruments which can provide for the expression,

the modelling and the analysis of the many

uncertainties that might affect the decision-making

process. It could become ambiguous for readers to

know the analysis of the information, so also verbal

clarifiers might be needed. The concept of linguistic

variables allows qualitative or subjective

information to be expressed quantitatively.

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Linguistic variables allow analysis and performance

values to be obtained with the graded evaluation

structure of each criterion in the decision-making

process [18], [19], [20], [221], [22], [23], [24], [25].

Decision makers can express their subjective

judgments in a simpler way by using linguistic

variables. In short, fuzzy set theory and linguistic

variables can effectively evaluate subjective

uncertainty in decision-making processes and

contribute to big data analysis in the same direction.

Fuzzy numbers, uncertainties in number values, can

be expressed by the concept of fuzzy set. Here

, x represents the values on the

real line R (-∞<x<+∞) and is a continuous mapping

from R to the closed interval [0,1].

The membership function assigns a degree of

membership to each value x, indicating its

membership in the fuzzy number A.

Triangular fuzzy numbers are preferred over

trapezoidal fuzzy numbers in many applications, as

they are easier to calculate and possess useful

features for decision-making. A triangular fuzzy

number, denoted as 

󰆻󰇛  󰇜, where l≤m≤u, is

characterized by its triangular membership function.

The membership function of a triangular

fuzzy number is defined as follows:

(1)

The automatic evaluation process uses various

linguistic variables which are then converted into

fuzzy numbers and following, the properly

weighting values are obtained. In other turn, ARAS-

F and the weight values produced by FAHP will

preset to evaluate firmly the criteria and rank them

up. By this means, a good analyzer can correlate

between these two parameters, quantitative and

qualitative, and produce even more accurate

data. To estimate the degree of membership of the

data, trapezoidal fuzzy number is defining, where

the numbers l, m and u are just the triangular fuzzy

numbers, i.e. l≤m≤u. Now for the values different

from [l, u] the membership value is zero, as

well. Fuzzy numbers, that are used to relate

linguistic values with numerical one’s, can also be

used to analyze linguistic values [26], [27], [28].

(2)

The method combines classic Analytical Hierarchy

Process (AHP) with instants of linguistic and fuzzy

values. Through applying linguistic variables as

well as fuzzy numbers the opinions of the assessors

are factored in better than in the classic approach

where even the most subjective evaluations were

made on a purely numerical scale. The methodology

used in the research is based on FAHP approach

which offers decision makers a variety of ways to

say linguistically what matters to them and add a

linguistic variables to the evaluation process.

3.1 Employing Fuzzy Analytical Hierarchy

Process (FAHP) to ascertain criteria weights

AHP was first introduced by Saathy in 1980 and

from this basic nature a tool conceived for assessing

the relative weight of criteria in MCDM. In classical

AHP a nine-point scale is used is applied instead of

different scales to the fixed items to compare their

relative novelty but in this process one disregards

the fact that there exists useless uncertainty and

subjectivity which humanizes comparison. To

withstand this uncertainty FAHP was used.

For this example a 9 point scale was used, where

1

, 3

, 5

, 7

, 9

 indicate different strength, priority, or

importance of each pair of items. Fuzzy numbers are

linked to a membership function that expresses the

degree of membership of each value. Fuzzy

numbers are combined with linguistic variables and

express the uncertainty in the pairwise comparison.

With this approach, triangular fuzzy numbers

provide probability values that are not single-point

but interval values, making the subjective judgment

of decision makers more realistic(in Table 1).

Table 1. Definition and membership function of

fuzzy scale

Using the fuzzy comparison matrix, the FAHP

method can derive the weights of the evaluation

criteria, taking into account linguistic variables and

subjective judgments expressed in triangular fuzzy

numbers. These weights provide insight into the

relative importance or significance of each criterion

in the decision-making process.

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The steps of the FAHP method used in this study

are:

1. Comparison of performance scores: The pairwise

comparison values are expressed in linguistic

variables using triangular fuzzy numbers.

2. Fuzzy comparison matrix: The fuzzy evaluation

matrix is expressed in terms of triangular fuzzy

numbers and their relative equivalents are obtained.

(3)

3. Fuzzy eigenvalue solution: The fuzzy eigenvalue



󰆻 is a nonzero nx1 fuzzy vector of the number x in

the fuzzy decision matrix and it contains the largest

value of λ_max. It aims to find the value of x with

the unit matrix of appropriate dimensions I using

equations (4) and (5).



󰆻  

󰆻   (4)

(A  - λ I) x  =0 (5)

Equations (4), (5) and (6), α-cutting techniques are

used in the solution process of the fuzzy decision

matrix, taking into account the fuzzy λ_max

eigenvalue and n×1 fuzzy vector.

det(

󰆻 - 

󰆻I) =0 (6)

The characteristic equation is a polynomial equation

in 

󰆻, and solving it yields the fuzzy eigenvalue 

󰆻.

Substituting the obtained 

󰆻 back into the equation (

󰆻

- 

󰆻I) x  = 0 allows us to determine the corresponding

fuzzy eigenvector x .

(7)

where,

(8)

(9)

for      and all i, j, where        

   .

By adjusting the value of α, the decision maker(s)

can express their level of optimism or pessimism in

their preferences. A larger value of α indicates a

higher degree of optimism, while a smaller value of

α reflects a higher degree of pessimism.

The optimism index, denoted by µ, expresses the

degree of confidence and satisfaction of decision

makers with fuzzy judgments. The optimism index

is calculated by the following formula:

µ = α · µ_max + (1 - α) · µ_min (10) (10)

where α is the weight value assigned to the

maximum value of µ and (1 - α) is the weight value

assigned to the minimum value of µ.

The degree of membership µ provides information

on the level of trust and satisfaction of decision

makers and thus helps in understanding subjective

preferences and decision making.

(11)

After fixing the degree of membership, i.e. the

optimism index μ and the constant value α:

1. The max () and min () values are calculated for

each element in the fuzzy judgment matrix.

2. The optimism index μ is adjusted and the formula

is calculated:

μ = α - max( ) + (1 - α) - min( ) (12) (12).

3. Each value in the fuzzy judgment matrix is

compared with μ and takes the value 1 if the

comparison value is greater than μ and 0 if it is less

than or equal to μ.This process results in a binary

matrix where the elements are either 1 or 0,

depending on whether they meet the threshold of the

index of optimism μ.

This matrix represents the degree of satisfaction or

preference for each element in the fuzzy judgment

matrix, based on the fixed value of α.

Note that the value of α determines the balance

between optimism and pessimism in the decision

maker's preferences. By adjusting α, different levels

of optimism can be incorporated into the analysis.

(13)

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The eigenvector is calculated by fixing the value of

µ and determining the maximum eigenvalue.

(4) Consistency ratio. To ensure the accuracy of

comparative weights, the value of the consistency

ratio CR must be less than 0.10.

    󰇛  󰇜 󰇛  󰇜 (14) (14)

(5) Aggregation of priorities. In the last step, the

local priorities at different levels in the decision

hierarchy are aggregated to derive the criteria

decisions. In the aggregation process, geometric

mean or weighted mean approaches are preferred.

The procedures used to determine the priorities, i.e.

weight values, are as follows:

1. Using FAHP, the local priorities to be included in

the decision matrix are determined.

2. Local priorities are taken into account to obtain

aggregate weights. If local priorities are denoted by

P1, P2 and P3, their weights will be W1, W2 and

W3 respectively, and the combined global priority is

Composite Global Priority = W1 * P1 + W2 * P2 + W3 * P3 (15).

3. Global priorities are normalized so that the sums

are equal to 1.

    󰇛  󰇜 󰇛  󰇜 (16)

With these steps, global priority values are

determined and the weight value obtained is used in

the next analysis process.

3.2 Utilizing the Fuzzy ARAS

The ARAS-F (Contribution Rate Assessment -

Fuzzy) method analyzes and ranks performance by

comparing it with the ideal alternative assessment

method and the following steps are used:

Step 1: Define the ideal alternative: The ideal

alternative is determined based on the maximum

value for benefit criteria (set B) and the minimum

value for cost criteria (set C). The ideal alternative is

represented as:

(17)

Step 2: Normalize the values: Normalize the values

in the decision matrix using the following equations:

(18)

Step 3: Construct the weighted-normalized matrix:

Multiply the normalized values by the significance

coefficients:

(19)

Step 4: Compute the overall utility: Calculate the

overall utility of each alternative by summing the

weighted-normalized values for each alternative:

(20)

Step 5: Defuzzify the overall utility: Apply the

Center of Area (COA) method to defuzzify the

fuzzy number Si and obtain a crisp value:

(21)

Step 6: Calculate the relative utility: Compute the

relative utility (Ki) of each alternative by dividing

its defuzzified overall utility by the overall utility of

the ideal alternative:

(22)

Step 7: The alternatives are ranked relatively and the

best alternative is represented by the Ki value.

With these steps, the criteria in the fuzzy ARAS

method are evaluated according to their

performance and the ranking process is carried out

from the best performance to the worst performance.

4 Application

The integrated use of the scoring method and factor

analysis in the analysis process will contribute to

increasing organizational efficiency. Objective and

consistent job evaluations of organizations,

analyzing job roles with objective rules and

reducing prejudices will also increase internal

reliability. With fair compensation structures,

employees' motivation and trust within the

organization will also increase. With a unified

approach, job roles are also aligned with strategic

goals, resulting in a more efficient structure.

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Table 1 Criterias and Main Performance Scales

Table 1 shows the basic performance scales and

score grades of the criteria. The criteria were

analyzed under 5 sub-headings. These titles are task

characteristics, knowledge, responsibility,

Effort/Environment and Skills. In this application,

the factors and sub-factors used are as follows (in

Table 1):

 Task Characteristics: 1.1 Task Complexity

1.2 Task Relevance

 Knowledge: 2.1 Professional Knowledge

2.2Knowledge Updates 2.3 Physical Skills

2.4 Experiences

 Responsibility 3.1 Responsibility for

Financial Resources 3.2 Responsibilities for

Human Resources

 Effort/Environment 4.1 Mental Effort 4.2

Physical Effort 4.3 Working Conditions

Skills 5.1 Planning and Organizational Skills

5.2 Communication and Relationship Skills

5.3 Analytical and Judgemental Skills 5.4

Innovation Skills

This ensures that job roles designed and evaluated in

a manner that supports the organization's objectives

and maximizes productivity and efficiency.

These factors and sub-factors used to evaluate and

assess various aspects related to workforce

management. Each factor represents a specific area

or attribute that considered important in the

decision-making process. For example, the Task

Group includes factors such as task complexity, task

relevance, skill, and initiative and improvement,

which are essential for assessing an individual's

competency and expertise. Similarly, the

Responsibility Group focuses on factors related to

different types of responsibilities, such as financial

resources responsibility, and human resources

responsibility.

A general overview of the calculation procedure

includes the determining the weights of evaluation

criteria in fuzzy AHP.

1. Construct the pairwise comparison matrix:

Experts or decision-makers compare the importance

of each criterion with respect to the others using

linguistic terms. The comparisons recorded in a

pairwise comparison matrix, where each element

3. α-cut fuzzy comparison matrix: The fuzzy

comparison matrix is obtained by applying α-cuts to

the fuzzy numbers in the binary comparison matrix.

The α-cut also expresses the degree of confidence. It

is an auxiliary factor used in the analysis of

uncertainty.

4. Fuzzy eigenvalue analysis: It is used to determine

the weights of the evaluation criteria. In the

equation Ãx = λx, where Ã is the α-cut fuzzy

comparison matrix, x is the eigenvector, i.e. the

relative weight of each criterion, and λ is the

eigenvalue.

5. Consistency ratio: Consistency ratio (CR)

analyzes how pairwise comparisons compare with

the random consistency index (RI).A lower CR

value indicates better consistency. If the CR exceeds

a predefined threshold (typically 0.10), the

judgments may need to be revised.

6. Aggregate the weights: The local weights

obtained for each criterion at different levels of the

decision hierarchy aggregated to determine the

composite global weights. This aggregation process

combines the relative importance of criteria from

different levels to obtain a comprehensive weight

for each criterion. The criteria were evaluated by 5

experts and the averages of the evaluation results are

shown in Table 2.

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Table 2 Pairwise Comparison Matrix values of

criteria with linguistic expressions

The weightage of the sub-criteria can be established

by pursuing the steps delow. The particular numeric

values and strategies may change according to the

chosen fuzzy directive and the informationstated in

Table 3. The average rating values obtained by

analogous evaluation of all criteria were set fuzzily

as well. While the variety of the assessment results

on one hand occupy a part of the Table 3, on the

other hand, the pairwise comparison matrix appear

at this point. Even though the answer sheet is the

hidden word as Table 4, then the score of the criteria

is uncovered in Table 5.

Table 3 Pairwise comparison matrix

According to the results of this ranking, the criteria

with the highest importance are C1- “Task

complexity”, C2-“Task relevance”, C7-

“Responsibilities for financial resources”, C9-

“Mental Effort. The lowest value is C13-

“Communication and judgmental skills”, C10-

“Physical Effort”, C15-“Innovation Skills”. In Table

5, the weights of criteria within the different levels

of the evaluation matrices, as well as the importance

ranking for the parameters are presented (in Table

6).

Table 4 Weight values

Table 5 Ranking weight values

These results provide insight into the relative

importance of different criteria in the evaluation of

parameters. The weights obtained through FAHP

help prioritize these factors and guide the decision-

making process in selecting and ranking the

parameters according to their performance.

Table 6 Normalized values.

In the evaluation and ranking of the human

resources evaluation alternatives using the ARAS-F

method, the following steps were followed:

Step 1: Data collection and linguistic conversion:

The experts' opinions and evaluations were collected

and converted into triangular fuzzy numbers to

represent the linguistic values. This conversion

allows for handling subjective uncertainty in the

decision-making process.

Step 2: Construction of the fuzzy decision-making

matrix: Using the global weights of all sub-criteria

obtained from the FAHP, a fuzzy decision-making

matrix was constructed. This matrix incorporates the

performance evaluations of the parameters

alternatives based on the collected data.

Step 3: Calculation of performance indices: The

ARAS-F method applied to calculate the

performance indices of the parameters alternatives

using the fuzzy decision-making matrix. This step

involved the normalization and weighting of the

performance values according to the ARAS-F

procedure described earlier.

Step 4: Evaluation and ranking of alternatives: As a

result of the evaluation using the Fuzzy ARAS

method, workforce planning was the most important

criterion, followed by job evaluation as the second

most important criterion. Recruitment and selection

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ranked 3rd, workforce planning ranked 4th and this

criterion was followed by compensation and

benefits, Performance Appraisal and lastly health &

safety.

Table 7. Final performance indices of human resources with

respect to fuzzy MCDM methods

6 Conclusion

In this study, fuzzy ARAS (Additive Ratio

Assessment) technique is developed and proposed

together with an integrated scoring structure to

increase organizational efficiency and effective job

evaluation in the manufacturing sector. Relatively

focusing on the process of defining and scoring

processes, business processes were analysed. This

study is expected to contribute to human resources

applications, especially in job evaluation,

compensation management and performance

analysis. The limitations and deficiencies in

traditional job evaluation methods have been

addressed in this study and a new perspective has

been brought to this subject. With the scoring

system integrated with the fuzzy ARAS technique,

the evaluation criteria and the relationships between

them were also analysed. The study also examined

the ambiguities between job evaluation and job

appraisal and the effects of these ambiguities on the

system. The subjective judgments of the experts and

their evaluations using linguistic variables were

included.

The findings of the study show the applicability of

this study in this field. The use of a fuzzy ARAS

approach with more than one evaluation criterion

allows the job evaluation process to be evaluated

with a more robust and reliable analysis.

References:

[1] Kaur, N., Kang, L.S.(2021) Person-organisation

fit, person-job fit and organisational citizenship

behaviour: An examination of the mediating role

of job satisfaction, IIMB Management Review,

Volume 33, Issue 4, December, Pages 347-359

[2] Nazarian, A., Atkinson, P., Foroudi, P.,

Edirisinghe, D., Factors Affecting Organizational

Effectiveness In Independent Hotels – The Case

Of Iran, Journal of Hospitality and Tourism

Management, Vol.46, March 2021, pp.293-303.

[3] Naveed, T., Alhaidan, H., Halbusi, H.A., Al-

Swidi, A.K., Do Organizations Really Evolve?

The Critical Link Between Organizational Culture

and Organizational Innovation Toward

Organizational Effectiveness: Pivotal Role Of

Organizational Resistance, Journal of Innovation

& Knowledge, Volume 7, Issue 2, April–June

2022, 100178

[4] Nguyen, T.M., Malik, A., Budhwar, P.,

Knowledge hiding in organizational crisis: The

moderating role of leadership, Journal of Business

Research, Vol. 139, Feb. 2022, pp. 161-172.

[5] Anand, A., Dalmasso, A., Vessal, S.B.,

Parameswar, N., Rajasekar, J., Dhal, M. (2023)

The Effect of Job Security, Insecurity, and

Burnout of Employee Organizational

Commitment, Journal of Business Research,

Volume 162, July, 113843

[6] Lingmont, D.N.J., Alexiou, A., The Contingent

Effect Of Job Automating Technology Awareness

On Perceived Job Insecurity: Exploring The

Moderating Role Of Organizational Culture,

Technological Forecasting and Social Science,

Vol.161, December 2020, 120302

[7] Charoensukmongkol, P., Supervisor-

Subordinate Guanxi And Emotional Exhaustion:

The Moderating Effect Of Supervisor Job

Autonomy And Workload Levels In

Organizations, Asia Pasific Management Review,

Vol.27, Is.1, March 2022, pp.40-49

[8] Song, Z., Chon, K., Ding, G., Gu, C., Impact of

organizational socialization tactics on newcomer

job satisfaction and engagement: Core self-

evaluations as moderators, International Journal of

Hospitality Management, Vol. 46, April 2015,

pp.180-189.

[9] Behera, R.K., Bala, P.K., Rana, N.P., Kizgin, H.,

Cognitive Computing Based Ethical Principles For

Improving Organisational Reputation: A B2b

Digital Marketing Perspective, Journal of Business

Research, Vol. 141, March 2022, pp. 685-701.

[10] Nadiri, H., Tanova, C., An Investigation Of

The Role Of Justice In Turnover Intentions, Job

Satisfaction, And Organizational Citizenship

Behavior In Hospitality Industry, International

Journal of Hospitality Management, Vol. 29, Is.1,

March 2010, pp.33-41

Engineering World

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Volume 6, 2024

[11] Clercq, D.D., Haq, I.U., Azeem, M.U., The

Roles Of Informational Unfairness And Political

Climate In The Relationship Between

Dispositional Envy And Job Performance In

Pakistani Organizations, Journal Of Business

Research, Vol.82, January 2018, Pp. 117-126.

[12] Katsikea, E., Theodosiou, M., Perdikis, N.,

Kehagias, J., The Effects Of Organizational

Structure And Job Characteristics On Export Sales

Managers’ Job Satisfaction And Organizational

Commitment, Journal of World Business, Vol. 46,

Is. 2, Apr. 2011, pp. 221-233.

[13] Eliyana, A., Maarif, S.M., Job Satisfaction

And Organizational Commitment Effect In The

Transformational Leadership Towards Employee

Performance, European Research on Management

and Business Economics, Vol.25, Is.3, Sept-Dec.,

2019, pp. 144-150

[14] Zou, X., Ingram, P., Bonds And

Boundaries: Network Structure, Organizational

Boundaries, And Job Performance, Organizational

Behavior And Human Decision Processes, Vol.

120, Is.1 Jan. 2013, pp. 98-109.

[15] Lin, S.L., Chen, Z.X.,Ashford, S.J., Lee, C.,

Qian, J., A Self-Consistency Motivation Analysis

Of Employee Reactions To Job Insecurity: The

Roles Of Organization-Based Self-Esteem And

Proactive Personality, Journal of Business

Research, Vol. 92, Nov. 2018, pp.168-178.

[16] Meng, J., Berger, B.K., The impact of

organizational culture and leadership performance

on PR professionals’ job satisfaction: Testing the

joint mediating effects of engagement and trust,

Public Relations Review, Vol.45, Is.1, March

2019, pp. 64-75

[17] Ho-Taek, Y., Cho, Y., Amenuvor, E.,

Internal Marketing And Salespeople's Out-Of-Role

Behaviour: The Mediating Role Of Job

Satisfaction, European Research on Management

and Business Economics, Vol.29 ,Is.2, May-

August 2023, 100216.

[18] Tavana, M., Shaabani, A., Di Caprio, D.,

Amiri. M. (2021) An integrated and

comprehensive fuzzy multicriteria model for

supplier selection in digital supply chains,

Sustainable Operations and Computers, Volume 2,

Pages 149-169.

[19] Büyüközkan, G., Göçer, F. (2018) An

extension of ARAS methodology under Interval

Valued Intuitionistic Fuzzy environment for

Digital Supply Chain, Applied Soft Computing,

Volume 69, August, Pages 634-654.

[20] Chen , T., Wang, Y.C., Jiang, P.H. (2023)

A selectively calibrated derivation technique and

generalized fuzzy TOPSIS for semiconductor

supply chain localization assessment, Decision

Analytics Journal, Volume 8, September, 100275.

[21] Deveci, M., Gökaşar, İ., Brito-Parada, P.R.

(2022). A comprehensive model for socially

responsible rehabilitation of mining sites using Q-

rung orthopair fuzzy sets and combinative

distance-based assessment, Expert Systems with

Applications, Volume 200, 15 August, 117155.

[22] Ghahremani-Nahr , J., Ghaderi, A., Kian, R.

( 2023) A food bank network design examining

food nutritional value and freshness: A multi

objective robust fuzzy model Javid a , Abdolsalam

a,∗ , Ramez b, Expert Systems with Applications,

Volume 215, 1 April, 119272.

[23] Turgay, S., Erdoğan, S., Security Impact of

Federated and Transfer Learning on Network

Management Systems with fuzzy DEMATEL

Approach, Journal of Artificial Intelligence

Practice, Journal of Artificial Intelligence Practice

(2023), Vol. 6 Num. 4, DOI:

10.23977/jaip.2023.060404 ISSN 2371-8412.

[24] Turgay, S. ,Ayma, S.B., Determined by

Tolerances with Rough Set Based MCDM,

Industrial Engineering and Innovation

Management (2021) 4: 34-47 Clausius Scientific

Press, Canada, DOI: 10.23977/ieim.2021.040105

ISSN 2522-6924.

[25] Taşkın, H., Kubat, C., Topal, B., Turgay, S.,

Comparison Between OR/Opt Techniques and Int.

Methods in Manufacturing Systems Modelling

with Fuzzy Logic International Journal of

Intelligent Manufacturing, 15, 517-526 (2004).

[26] Saaty, T.L. (1980) The Analytic Hierarchy

Process. McGraw-Hill, New York.

[27] García-Rodrí, F.J., Dorta-Afonso, D.,

González-de-la-Rosa, M., Hospitality Diversity

Management and Job Satisfaction: The Mediating

Role of Organizational Commitment Across

Individual Differences, International Journal of

Hospitality Management, Vol.91, October 2020,

102698.

[28] Y.Zou, C. Chun-An, “A combinatorial

optimization approach for multi-label associative

classification”, Knowledge-Based Systems 240 ,

108088, 2022.

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DOI:10.37394/232025.2024.6.9

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

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S.Turgay, R.Yılmaz – investigation,

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S.Turgay writing & editing.

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DOI:10.37394/232025.2024.6.9

Safiye Turgay, Recep Yilmaz

E-ISSN: 2692-5079

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