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Performance Evaluation System Of Retail Industry

Using Analytical Hierarchy

Selvia Merizqi, Suharjito

Abstract: The company, one of the largest retailers in Indonesia which has 61 malls spread throughout Indonesia. This, of course, creates intense competition between the malls in showing the performance of each mall, thus requiring system management in any mall decision making that requires increased and more attention in improving the performance of their malls. At present, one of the retail company in Indonesia has had a Main Performance Indicator (KPI) in the assessment of managed malls, however, the criteria and sub criteria required by each mall cannot be determined completely according to what is in each. The weighting for each criterion and sub-criteria that can be used for the entire mall. This study aims to resolve the decision-making problem of Shopping Malls performance by considering main criteria such as Financial, Customer Satisfaction, Growth & Develop Talents, and Internal Process. This research using Analytical Hierarchy Process (AHP). The Data is collected through a questionnaire that will be submitted to customers by using a combination of Balanced Scorecard. By referring to the solution provided by the AHP (Analytical Hierarchy Process) method in making decisions, a decision maker can make decisions about choosing a mall that has a higher performance based on established criteria Index Terms: Retailers, Shopping Malls, Analytical Hierarchy Process (AHP), Financial & Non-Financial Criteria, Performance, Criteria, Decision

Support System

————————————————————

1.

INTRODUCTION

The global retail industry has undergone drastic changes recently, including in Indonesia. In 2016, Indonesia was the third largest country in terms of retail sales growth after India and China. In 2016, modern retail sales in Indonesia increased by 10% to 200 trillion (around USD $15 billion). Roy Nicholas Mandey, the Chairman of the Indonesian Retailers Association (Aprindo) informed that Indonesia remains an attractive country for retailers because of the very large population size and the character of Indonesians known as those eager to try and to buy new products [1]. Financial market participants are very fast in responding to the growth of Indonesia’s middle-class, such as having retail property developers, shopping malls and department stores. This is adapted to meet the needs and expectations of the growing middle class with regard to the overall focus of ASEAN: Indonesian Consumer Markets [2]. Shopping malls have a central role in the retail ecosystem by uniting groups of buyers and retailers. The mall's activities are managing physical facilities such as security and maintenance operations, which allow retailers to trade effectively. However, now the retail ecosystem is experiencing major changes. Digital technology becomes very challenging for traditional shopping malls. Moreover, information management on a large scale now allows companies to combine digital and physical elements to offer new products and services [3]. In this regard, more retailers are implementing digital technology to maximize profit efficiency, to expand their portfolio of activities and to increase availability for buyers [4] . The presence of shopping malls is one of the city’s prides. Attractive appearance, attractive shops, and growing and various consumers along with the changes in balance demand in the mall sector force the managers to work hard to survive in this sector [5]. The intense competition among shopping malls also makes mall

managers create their malls different from the nearest competing malls. They begin to do this by understanding carefully what the consumers’ target market values and their shopping behavior. Failure of management to identify factors that ultimately affect mall image is something not good. One strategy in creating profitable shopping malls is to properly manage mall performance attributes. One of the largest retailers in Indonesia which has 61 malls spread throughout Indonesia ranging from low to premium prices. This definitely creates intense competition among the malls in showing their own performance. Thus the management needs a system in any decision making that requires more attention to improving the mall performance.

A decision-making system also requires information technology due to the globalization era, which requires a company to move quickly in making decisions and taking actions. In dealing with such multi-criteria decision-making processes, Analytical Hierarchy Process (AHP) developed by [6] can be used for this research. AHP allows experts to evaluate the weight of attributes with greater consistency through pairwise comparison [7]. In AHP, all criteria and parameters are assigned a weighting score that shows the importance of each criterion. The AHP approach has been widely adopted in the field of the environment which is designed as a decision-making tool. Therefore, the application of AHP to this survey is very strong and is not a biased result because the consideration of weighting depends on the decisions of the experts. By referring to the solutions provided by the AHP (Analytical Hierarchy Process) method in assisting decisions, a decision maker can make decisions about choosing mall with higher performance based on the set criteria. AHP builds hierarchy (ranking) from decision devices using a comparison between each pair of devices expressed as a matrix. Pairwise comparisons produce weighting scores that measure how important the devices and the criteria to each other. AHP is developed to optimize decision making when a person is faced with a combination of qualitative, quantitative and sometimes conflict factors considered. AHP has been very effective in making complicated decisions. The purpose of this research is to solve the problem of improving the performance of each mall by using the Analytic Hierarchy Process (AHP) method which is considered appropriate in determining the weight and ranking for the well-performing ————————————————

Selvia Merizqi is graduate from Computer Science Bina Nusantara University Graduate Program-Master in Computer Science

PH-08111126269. E-mail: selvia.merizi@binus.ac.id

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3309 malls. Despite the popularity and simplicity of the AHP

concept, this method is often criticized for its inability to deal with inherent uncertainties and inaccuracies associated with mapping decision makers' perceptions of exact numbers. Because some evaluations of criteria are subjective and qualitative, it is very difficult for decision-makers to express preferences using appropriate numerical values for pairwise comparisons. AHP cannot be directly applied to solve the problem of uncertain decision making. To eliminate this limitation, thus [8] a fuzzy approach was considered, which is able to overcome the uncertainty and inaccuracy of evaluation processes. This study applied the fuzzy concept to reduce uncertainty regarding the data in assessing retail quality attributes

2.

RELATED WORK

Shopping Malls are places where retailers can attract their customers in an attractive and colorful atmosphere. On the other hand, the role of shopping malls can be seen from various perspectives; as a place of business, as a property and as an investment. These different roles bring challenges to shopping mall management. The tenants grouping will be more significant because shopping mall management needs to understand all of these perspectives before developing their own goals and policies in combining the right tenants in their shopping malls. This will serve to strengthen beneficial interactions between tenants and their business managers to get better operational performance [9]. The design of a decision-making system using Analytical Hierarchy Process (AHP) is a method in decision-making. There has been plenty of research on decision making. The following is the literature review of the research. Research conducted by [10] proposed a decision support model for supplier selection based on Analytical Hierarchy Process (AHP) in the case of the automotive industry in a developing country, Pakistan. Then a sensitivity analysis was conducted to examine the robustness of supplier selection decisions. Methodology: The model was started by identifying the main criteria (price, quality, delivery, and service) using a literature review and ranking of the main criteria based on the opinions of experts using AHP. The second stage used in the methodology adopted was identifying sub-criteria and their ranks based on the main criteria. Finally, a sensitivity analysis was implemented to examine the decision robustness using Expert Choice. The research conducted by [11] in 2017 presented a procedure for structuring the problem of transportation decision-making and evaluating the proposed solution from a multi-stakeholder perspective. Analytic Hierarchy Process (AHP) was used as a multi-criteria decision technique, while role-play was used to reproduce participatory processes where students acted as key stakeholders. A comparison between the mathematical aggregation of individual priorities and consensus voting was carried out to verify the differences between the two different methods with their compliance with the stakeholders mentioned in the preferences. AHP-based participatory procedures were proven suitable for overcoming the complexity of transportation decisions.

AHP consists of four steps [10]:

1. Construction of Structural Hierarchy: where objectives are highlighted, and criteria and alternatives are identified. Complicated decisions must be broken down into structural hierarchy from goal to various criteria and sub-criteria to the lowest order. The goal is represented at the

top level of the hierarchy. Then criteria and sub criteria are represented in the middle of the hierarchy. Finally, the alternatives are set at the bottom level of the hierarchy.

Figure 1 Construction of Structural Hierarchy

2. Constructive of Comparative Judgments (Pair-wise Comparison Matrix) for all criteria and alternatives [12]. After the hierarchy is built, the next step is to determine the priority of variables at each level by building a comparison matrix of all variables in relation to each other. The Pairwise comparison illustrates how many variable 'A' is better or more important than variable 'B'. Logical preferences are measured using pairwise comparison evaluations using scale points 1-9 as shown in the table below. The matrix is represented mathematically as follows.

(1)

(2) 3. Determination of weights through normalization

procedure. To determine the criteria weights and local weights as alternatives from the pairwise comparison matrix, each value in the 'j' column is divided by the total value in the 'j' column. The total value in the matrix must be 1, therefore, the normalization of the pairwise comparison matrix is represented in the equation below. 4. Synthesis of weight and consistency test. First, get

(3)

(3)

Second, control the consistency of the weight values (). To do this, vector consistency will be calculated (A x C Matrix). Afterward, multiply A and C (A x C) to reach the nearest top two to the eigenvector. This is shown by the following equation.

(4)

Third, estimatem ax . An estimate of m ax will be calculated using the following formula:

m ax

 = ni = 1 1

1

C X

(5)

Where m ax is the eigenvalue of the pairwise comparison matrix, then the estimate to the consistency index (CI) will be calculated. Finally, the assessment of consistency for the exact value of n by CR must be examined to ensure the consistency of the pairwise comparison matrix, as shown in the following representation.

CI = 1 m ax

 

n n

dan CR=

RI CI

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RI range is the sequence length (0.00, 0.00, 0.58, 0.09, 1.12, 1.24, 1.32, 1.41, 1.45, and 1.49); where RI represents a random consistency index and RI value for different number of n from 1 to 10. If CR ≤ 0.10 (10%) then the level of consistency is satisfying; but if CR> 0.10, then it indicates serious inconsistency. The inconsistency index is then converted into inconsistency ratio because it measures the inconsistency of a matrix. Inconsistency index is then converted into inconsistency ratio by dividing it with Random Index. From 500 random matrix samples with a scale ratio of 1-9, several Saaty’s matrices of order obtain a RI mean value as displayed in the following table.

Table 1: Random Index Value

n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49 1.51 1.48 1.56 1.57 1.59 Information:

n = Matrix of Order (number of criteria evaluated) RI = Random Index

By Comparing the values of CI and RI, it will result in a standard that declares whether a matrix is consistent or not, so-called Consistency Ratio (CR) which is indicated by the following formula:

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If CR value is smaller than 0.10, this means the assessment of decision maker is relatively consistent and the AHP method can be used for decision making. If not, then the assessment needs to be repeated.

3. RESEARCH METHODOLOGY

To start the research, the author determined the methodology as follows: Stage I (Research Problem and Literature Study) The research preparation stage was done by formulating the background, research problem, objectives, benefits and scope. Furthermore, the literature study was also conducted on related previous research. The research began with describing the background, then determining the purpose and scope of this research. The literature study became necessary to deepen understanding of the model to be analyzed for formulating into the research problem.

Stage II (Data Collection)

Required to obtain information needed in research. Data collected consists of primary data and secondary data: a) Primary Data

Primary data in the form of information obtained by direct research to determine the current assessment related to the Mall Performance to be studied, such as AHP questionnaire data and related processes, consolidation readiness and existing problems. This data is obtained from the results of interviews, filling in questionnaires, system reports, and observations.

b) Secondary Data

Secondary data is data that already exists and can be seen by the public, such as data on the website and other data related to this research

Stage III (Data Processing)

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3311 3.1 AHP Method

The analytical method used to answer the existing problem was the AHP method which functioned to decide the main criteria in determining the good mall performance properly. In this part, decomposition of the problem was done to be arranged later in a decision hierarchy. Decomposition was completed by identifying and decomposing the following components.

1) Purpose

The purpose of this AHP method analysis was to determine and to rank the mall performance.

2) Criteria and Sub Criteria

The preparation of criteria and sub-criteria was done by first conducting a literature study and review of

previous related research results. The results of the literature study and review of previous studies showed several criteria and sub-criteria candidates which were included into the decision hierarchy criteria and sub-criteria. Further, these were discussed with the HR Development Manager and HR Assistant Manager in a discussion for finalizing while validating the suitability and application of criteria and sub criteria that were used in the questionnaire with conditions in the related companies. The results of the discussion obtained the criteria and sub-criteria used in this research which are shown in the following table:

Table 2: Criteria & Sub Criteria Reference

Criteria Sub Criteria

Financial (K1)

a. Rental (K11) b. Casual Leasing (K12) c. Service Charge (K13) d. Promotion (K14) e. Utilities (K15) f. Miscellaneous (K16) g. Parking (K17)

Customer Satisfaction (K2) a. Shopper Satisfaction (K21) b. Number of accident (K22)

Internal Process (K3) a. EPRO Implementation (K31)

b. CCTV Implementation and connect to HO (K32)

Growth & Develop Talents (K4)

a. Identifying and mapping talents competencies and define carrier path (K41)

b. Gross revenue per headcount per month (vs. Last year) (K42)

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in the process of mall performance

decision

Figure 2: AHP Hierarchy Process

4.

RESULTS AND DISCUSSIONS

4.1 AHP Processing

In this study, AHP questionnaire data processing was carried out to the resource persons or respondents who were considered to have competence, understanding, and visions related to the problems being faced and the expected goals to be achieved. Because the mall performance decision was in relation to IT service support provision within the retail company, the respondents from each mall and from the head offices were invited to give their opinions. AHP data calculation was completed using a combination of 2 tools. The first was using MS. Excel, where the matrix was created manually by the researchers. The second was using the AHP Expert Choice, which was more popular for the application of the AHP method. The use of this combination was to validate or to calculate the output of the tools to give slightly different results, the difference in the number of zeros behind the dot symbol caused by rounding or calculating iterations, but in principle, the calculation resulted in the correct outputs.

Table 3: List of Respondents

Responden Jabatan Posisi

Responden 1 Mall Director Mall Responden 2 Head of HR Performance Head Office Responden 3 Business Intelligence Manager Head Office The questionnaire responses from each respondent were then

inputted in the pairwise comparison matrix to calculate the consistency values of the opinions given. Iteration for revision or refilling the given value was done by confirming to the informants if the consistency values exceeded the specified limit, greater than 0.1.

4.2 Determining The Weights of Criteria

After the values obtained from the correspondents in the matrix pairwise comparison provided consistent results, a combined calculation of all respondents as in table 2 was done, by inputting the values in the pairwise comparison matrix. Then the priority values or relative weights between factors in the matrix were calculated by using Expert Choice to determine the weight of each criterion. The calculation results for each criterion shown in Table 4.

Table 4: The Weights Result of Criteria

Kriteria Weight

Financial (K1) 0.581

Customer Satisfaction (K2) 0.148

Internal Process (K3) 0.160

Growth & Develop Talents (K4) 0.111

For the weight of priority of the sub-criteria (local priority), it needs to be weighted from the parent criteria above to get the overall priority. Criteria or sub-criteria directly connected with alternatives, as illustrated in the decision hierarchy. The results of multiplication between each alternative weights with criteria weights or sub-criteria are then summed to get the final ranking value as shown in table 4.55. In Table 3, the sequence of alternative rankings of the Mall Performance Criteria is Financial (0.581), then the second is Internal Process (0.160), Customer Satisfaction (0.148), then the last order is Growth & Develop Talents (0.111).

4.2 Determining The Weights of Sub-Criteria

After getting the weight between criteria, then in Table 4 shows the pairwise comparison combined results of the calculation of priority weight or vector for the entire sub-criteria.

Table 5: The Weight Result of Sub Criteria

Criteria Sub-Criteria Weight

Financial

Rental (K11) 0.352

Casual Leasing (K12) 0.088

Service Charges (K13) 0.268

Promotions (K14) 0.066

Utilities (K15) 0.099

Miscellaneous (K16) 0.058

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3313 Customer

Satisfaction

Shopper Satisfaction (K21) 0.781

Number of Satisfaction (K22) 0.219

Internal Process

EPRO Implementation (K31) 0.781

CCTV Implementation (K32) 0.219

Growth & Develop

Talents

Identifying & Mapping Talents Competencies and define carrier

path (K41) 0.25

Gross Revenue per Headcount per Month (K42) 0.75 4.3 Analysis of AHP Data Processing Results

The results of the overall calculation of weights for the criteria and sub-criteria are then poured into the Mall performance application which is intended for users of each mall consisting of 10 Mall Strata in Indonesia in Table 6 below:

Table 6: List of Malls No Nama Mall

1 WTC Matahari

2 Metropolis Town Square 3 GTC Makassar 4 Malang Town Square 5 Depok Town Square 6 Grand Palladium Mall 7 Bellanova Country Mall 8 City of Tomorrow 9 Grand Mall Bekasi 10 Tamini Square

Furthermore, Table 6 shows the results of weight calculations for the overall criteria, along with the results of the Consistency Ratio (CR) calculation, showing that the entire matrix had values below 0. In conclusion, it could be declared as consistent, and the weight calculation values of sub-criteria could be used. The calculation results for each criterion are shown in Table 7.

Table 7: Synthesis of Final Results Calculation

Rental Promotio

ns Utilities Parking Shopper Satisfact ion

Number of satisfact

ion

(K11) (K14) (K15) (K17) (K21)

(K22)

Local Priority 0.352 0.088 0.268 0.066 0.099 0.058 0.069 0.781 0.219 0.781 0.219 0.25 0.75 Global Priority

(Kriteria x Sub Kriteria)

0.205 0.051 0.156 0.038 0.058 0.034 0.04 0.116 0.032 0.125 0.035 0.028 0.083

WTC Matahari 2 2 4 2 2 3 2 3 3 3 4 2 3

Metropolis Town

Square 3 1 1 2 3 2 2 2 4 2 4 4 4

GTC Makassar 4 3 2 2 4 3 2 2 3 3 3 4 4

Malang Town Square 4 3 2 5 3 4 2 2 3 5 4 4 3

Depok Town Square 3 4 3 4 1 3 3 3 3 5 4 3 4

Grand Palladium

Medan 2 3 4 4 2 3 4 1 1 5 4 3 4

Bellanova Country

Mall 3 4 3 2 2 3 2 1 3 5 4 2 3

City of Tomorrow 3 1 1 3 3 4 2 4 5 5 2 4 3

Grand Mall Bekasi 2 1 1 5 2 3 2 4 4 5 2 4 5

Tamini Square 4 1 2 4 4 4 5 5 4 3 4 3 4

Identifying & Mapping

Talents Competen cies and define carrier path (K41)

Gross Revenue

per Headcount per month (K42) Sub Kriteria

Casual Leasing (K12)

Service Charges (K13)

Miscellan eous (K16)

EPRO Implement

ation (K31)

CCTV Implement

ation (K32) Kriteria

Financial (K1)

Customer

Satisfaction (K2) Internal Process (K3)

Growth & Develop Talents (K4)

0.581 0.148 0.16 0.111

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Rental Promo

tions Utilities Parking Shopper Satisfac tion

Number of satisfac

tion

(K11) (K14) (K15) (K17) (K21) (K22)

Tamini Square 0.818 0.051 0.311 0.153 0.23 0.135 0.2 0.578 0.13 0.375 0.14 0.083 0.333 3.538 Depok Town Square 0.614 0.205 0.467 0.153 0.058 0.101 0.12 0.347 0.097 0.625 0.14 0.083 0.333 3.343 Malang Town Square 0.818 0.153 0.311 0.192 0.173 0.135 0.08 0.231 0.097 0.625 0.14 0.111 0.25 3.316 Grand Palladium Medan 0.409 0.153 0.623 0.153 0.115 0.101 0.16 0.116 0.032 0.625 0.14 0.083 0.333 3.044 GTC Makassar 0.818 0.153 0.311 0.077 0.23 0.101 0.08 0.231 0.097 0.375 0.105 0.111 0.333 3.023 City of Tomorrow 0.614 0.051 0.156 0.115 0.173 0.135 0.08 0.462 0.162 0.625 0.07 0.111 0.25 3.003 Bellanova Country Mall 0.614 0.205 0.467 0.077 0.115 0.101 0.08 0.116 0.097 0.625 0.14 0.056 0.25 2.941 Grand Mall Bekasi 0.409 0.051 0.156 0.192 0.115 0.101 0.08 0.462 0.13 0.625 0.07 0.111 0.416 2.918 WTC Matahari 0.409 0.102 0.623 0.077 0.115 0.101 0.08 0.347 0.097 0.375 0.14 0.056 0.25 2.771 Metropolis Town

Square 0.614 0.051 0.156 0.077 0.173 0.067 0.08 0.231 0.13 0.25 0.14 0.111 0.333 2.412 Identifying

& Mapping

Talents Competen cies and define carrier path (K41)

Gross Revenue

per Headcou

nt per month (K42)

Ranking Alternatif x Global

Kriteria

Casual Leasing (K12)

Service Charges (K13)

Miscell aneous (K16)

EPRO Implem entation

(K31) CCTV Impleme ntation (K32)

The ranking of alternatives is done by multiplying each weight (priority) alternative with the weight of the criteria or sub-criteria that are directly connected with the alternatives. The results of multiplication between each alternative weight with the criteria weight or sub-criteria are then summed to get the final ranking value as shown in table 4, with the order of the first best performing mall is Tamini Square (3,538), Depok Town Square (3,343), Malang Town Square (3,316), Grand Palladium Medan (3,044), Makassar GTC (3,023), City of Tomorrow (3,003), Bellanova Country Mall (2,941), Grand Mall Bekasi (2,918), WTC sun (2,771) and the last order Metropolis Town Square (2,412).

4.4 Analysis of AHP Data Processing Results based on Criteria

The following is the result of data processing based on Criteria for all Malls, In Figure 3 below shows the mall Performance Results based on Financial Criteria:

Figure 3: Mall Performance based on financial criteria

Based on the graph from Figure 3 above, it can be seen that Tamini Square gets the highest order based on Financial (1,898) and Grand mall Bekasi gets the lowest financial order (1,104). This means that Grand Mall Bekasi needs to get attention from the management and Mall Director because Financial is an important role in the performance of a mall, can be seen from Table 3 Financial get the highest weight that is 0.581. The following is the result of data processing based on Criteria for all Malls, In Figure 3 below shows the mall Performance Results based on Customer Satisfaction Criteria:

Figure 4: Mall Performance Based on Customer Satisfaction Criteria

Based on the graph from Figure 4 above, it can be seen that Tamini Square gets the highest order based on Customer Satisfaction (0.708) and the Grand Palladium Mall gets the lowest Customer Satisfaction sequence (0.148). This means that Grand Palladium Medan is a mall with poor satisfaction, so Mall Management needs to make efforts so that service to customers can be further improved.

The next image in Figure 5 is the Mall Performance Results based on the Internal Process Criteria:

Figure 5: Mall Performance Based on Internal Process Criteria

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3315 Figure 6: Mall Performance Based on Growth Criteria &

Develop Talents

4.5 Analysis of AHP Data Processing Results based on All Malls

Regarding the AHP results analysis, related to the evaluation of the performance of the Cell Mall based on the criteria and sub-criteria that have been determined, it can be seen that the financial criteria are the most important criteria in conducting mall assessments. The picture below is the result of evaluating the performance of the entire mall:

Figure 7: Mall Performance All Malls

It can be seen from the picture above that Tamini Square gets the best mall performance value compared to other malls, influencing the high level of Financial from the mall as described in Figure 7.

5. CONCLUSIONS

Based on the results of data processing and analysis it can be concluded as follows:

(1) the main factors or criteria that are most considered in determining the best performing Mall (Mall Performance), based on the largest sequence are Financial (0.581), Internal Process (0.160), customer satisfaction (0.148) and Growth & Develop Talents (0.111).

(2) The Mall with the best performance in May period is Tamini Square with the acquisition of 1,898 and Grand Mall Bekasi getting the lowest score of 1,104.

(3) In determining the best Mall performance, it is necessary to pay attention to the alternative weights to the main criteria under consideration. The alternative that is the first choice but has a low ranking weight on the main criteria will require a higher effort to accommodate the fulfillment of the main criteria. In this case Growth & Develop Talents have low weight related to other main criteria such as Internal Process and Customer Satisfaction.

REFRENCES:

[1] APRINDO, ―Indonesia Posts 3rd-Largest Modern Retail Sales Growth in Asia,‖ INDONESIA INVESTMENTS, 2017. [Online]. Available:

https://www.indonesia- investments.com/id/news/todays-headlines/indonesia-

posts-3rd-largest-modern-retail-sales-growth-in-asia/item7625?

[2] H. T. D. Council, ―ASEAN in Focus: The Indonesian Consumer Market,‖ HKTDC Research, 2017. [Online]. Available:

https://hkmb.hktdc.com/en/1X0A91HG/hktdc- research/ASEAN-in-Focus-The-Indonesian-Consumer-Market.

[3] J. Frishammar, J. Cenamor, H. Cavalli-Björkman, E. Hernell, and J. Carlsson, ―Digital strategies for two-sided markets: A case study of shopping mallshammar, Johan Cenamors,‖ Decis. Support Syst., vol. 108, pp. 34–44, 2018.

[4] G. A. and H. M., ―Beyond the hype: Big data concepts, methods, and analytics,‖ Int. J. Inf. Manage., vol. 35, pp. 137–144, 2014.

[5] G. F. Can and E. Kılıç Delice, ―A task-based fuzzy integrated MCDM approach for shopping mall selection considering universal design criteria,‖ Soft Comput., vol. 22, no. 22, pp. 7377–7397, 2018. [6] T. L. Saaty, ―Decision making with the analytic

hierarchy process,‖ Int. J. Serv. Sci., vol. 1, no. 1, pp. 83–98, 2008.

[7] D3 and B. Khwanruthai, ―How to do AHP analysis in Excel,‖ pp. 1–21, 2012.

[8] S. B. Gopalan,Rema; Satpathy, ―Article information : Evaluation of Retail Service Quality- A Fuzzy AHP approach,‖ 2015.

[9] S. Burnaz and Y. I. Topcu, ―A decision support on planning retail tenant mix in shopping malls,‖ Procedia - Soc. Behav. Sci., vol. 24, pp. 317–324, 2011.

[10] F. Dweiri, S. Kumar, S. A. Khan, and V. Jain, ―Designing an integrated AHP based decision support system for supplier selection in automotive industry,‖ Expert Syst. Appl., vol. 62, pp. 273–283, 2016.

[11] N. Giuffrida, ―Combining Analytic Hierarchy Process ( AHP ) with role-playing games for stakeholder engagement in complex transport decisions,‖ Transp. Res. Procedia, vol. 27, pp. 500–507, 2017.

Figure

Figure 1 Construction  of Structural Hierarchy
Table 1: Random Index Value
Figure 2: AHP Hierarchy Process
Table 6:  List of Malls
+2

References

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