A Fuzzy ANP Based Grey Relational Approach to Evaluate CRM System in Context of Bangladesh

This study aims to select a suitable CRM (customer relationship management) system among different possible alternatives for organization’s in Bangladesh. Since, evaluating CRM system on the basis of lot of attributes leads us to Multiple-criteria decision analysis (MCDA) problems. In this study, a hybrid MCDA models were used. FuzzyANP (Analytic Network Process) and GRA (Grey Relational Analysis) approaches were adopted to solve the problem. The study explored that the Hubspot CRM was optimal solution in context of Bangladesh. Our research will beneficial to the organizing for better customer support. As far our knowledge goes, this is the first attempt to select CRM softwares in context of Bangladesh.

the conclusion that "the more companies interact in imposing CRM strategies, principally at the initiation and preservation stage, the better they carry out" (Reinartz, Krafft, & Hoyer, 2004).
In Bangladesh, CRM system like Capsule, SalesforceIQ, Hubspot CRM, Zoho CRM, Onpipeline CRM, Saleswith, Smart CRM etc. are availed through multinational companies and telecom operators for making their business curve upward by increased revenue and customer satisfaction. Recognizing the rapid sales growth and revenue earning some local companies like Bashundhara Group,Rahimafrooz, Partex, Square, GenIT, Optimal IT ltd. etc. implemented CRMS step by step (HOSSAIN, 2019). From the above CRM system available in Bangladesh, we have shortlisted five CRM system as alternatives i.e. Zoho CRM, Onpipeline, Hubspot CRM, Saleswith, Smart CRM based on same quality system. We have thoroughly examined their features and took expert opinion for the suitable CRM system in context of Bangladesh.

Conceptual Model for CRM adoption
Information System as like CRM Software can be evaluated based on various frameworks like TOE-Technology Organization environment-framework, TAM -Technology Acceptance Model -theory, DOI -Diffusion of Innovations -theory, etc. Comparing TOE with TAM and DOI, we have seen the TOE framework includes some important factors and it turns out to be better ready to clarify intra-firm advancement adoption. The TOE framework additionally has a strong hypothetical premise, predictable experimental help, and the capability of utilization to IS innovation (Oliveira & Martins, 2011). So, based on a comprehensive literature survey we sort out every critical success factor studied in previous literatures under3 dimensions of TOE framework. 1) Technological Context -The technological setting depicts both the inward and onward advancements important to the organization, together with those existing within the organization.
-IT Integration System is such a process or phase regarding engineering that concerned with different subsystems or components to make them one large system. IT integration is used to conjugate value to a system through new functionality provided through connection of various system functions.
-Technological readiness gives priority to the level of technical assets in a company's settlement. A CRM initiative is considered expensive and a solid innovation that requires expensive information system infrastructures, technical expertise and integrated information services.
-Client information management is the practice of managing customer data in an association. It is a term that identifies with the more extensive classification of information the executives and it starts with sound record and substance the board rehearses.
-Data Quality and Integration data must be consistent and unambiguous and suitable for its intended uses in decision making, planning and operations. It is commonly publicized that faulty data quality can have a serious influence on the overall performance of a company.
2) Organizational Context -The organizational context is associated to the organization's resources, the size of the company, methodology, hierarchy, managerial structure etc. -KM Capabilities is the ability of an association to catch, oversee and convey continuous validated client, items and administrations data to improve client reaction. -Top management support is a very important factor that adopts of a new system in this case CRM. It is supported by senior management of a company (Cruz-Jesus, Pinheiro, & Oliveira, 2019). This factor is crucial for the preadoption and pre-implementation plan of CRM systems. -Management of customer contact channel presently the contact with customers at organizations are face to face visits but now the scenario is changed because of CRM System (Zahedi, Cheshmberah, & Tofighi, 2011). Usually, customers contact the organization through different correspondence stations, for example, telephone contact center, the World Wide Web and so forth.
-Perceived Benefits with regards to the study, it denotes how much benefits a firm can achieve by a CRM system. It is the degree to which a new system is perceived as a good solution (Racherla et al., 2008). -Vendor Support for adopting new technology, organizations show their high concern regarding the vendor support (Alshawi, Missi, & Irani, 2011). A vendor's quality of service can often be demonstrated during the purchasing process when evaluating software packages. -Government support, rules and regulations denotes different types government policies, strategies to help the appropriation of new advances or development (Hasani, Bojei, & Dehghantanha, 2017).

Methodology
In this stage, we selected some factors by reviewing selected papers about important factor of CRMS. We have selected those factors which might affect CRM software solution to a company.
We used the ANP, the Analytical Hierarchy Process (AHP)cannot work a huge of feedback, dependencies and interactions between lower and higher level criteria, alternatives and the ANP is the generalization of AHP. In other words, the ANP works more flexible by giving interdependent relationships and measurement among decision factor and alternatives (Saaty, 1980). After that, the fuzzy logic is applied with the ANP which is named fuzzy ANP. In comparison to other methods, fuzzy ANP is more suitable for weighting criteria. Another way, CRM system selection represents an issue in which there is limited of certain information and many factors regarding this analysis. Another way is that, some certain information is known, some information claims uncertainty and unknown which called is a grey area. Because of that is why we adopted another approach called Grey Relational Analysis Approach.

Analytic Network Process
Many decision problems cannot be solved by hierarchically e.g. AHP. Therefore, ANP was implemented. The structure of clusters and nodes make a network which looks more complex than AHP. Though it looks more complex, the pair wise comparison, dependency is just act like AHP. The dependency in hierarchically cannot specified all possible conditions. The significance of criteria and alternatives shows in hierarchy though the significance of alternatives themselves produces the importance of criteria. The feedback from the network enables us to determine that factor what we need to find out future desired. The interdependency among criteria and Journal of Tourism, Hospitality and Sports www.iiste.org ISSN (Paper) 2312-5187 ISSN (Online) 2312-5179 An International Peer-reviewed Journal Vol.57, 2021 20 alternatives is utilizing properly from hierarchical network.

The Fuzzy Logic
The fuzzy set is describable by a function like membership function. The membership function also characterizes the elements within a certain interval, which is originally [0,1]. This interval is most widely used for expressing the degree. It is known as the unit interval. After that, the degree of membership function denotes the extent that an element belongs to set or not. If the value is 1, it is completely agreeing with membership function. That means, the element is in the set. If the value is 0, it is not belonging to set. The remaining value between 0 and 1 is representing the membership in fuzzy sets with a certain degree. A fuzzy set develops different parameter as degree of membership of elements. In our study, we have introduced triangular fuzzy numbers as TFN which 1 to 9 as parameter to capture the judgement. It also helps to capture the lucidity.
The TFN is structuring in a way that which improves the 1 to 9 conventional schema. To take the judgement of human perception in qualitative object, the TFN used the five points scale (Alshawi et al., 2011,9) for membership function. The TFN value is depicted in figure. With the help of TFN, the comparison of matrices constructs to classify the judgement. To illustrate the fuzzy extension of the method, linguistically terms are selected. Now the pairwise comparison matrix of TFNs combined into a matrix which called Fuzzy judgement matrix A (aij) is created as set below: Here fuzzy eigenvalue, λ, is a fuzzy number result to: Ax=λx ……………………………………………………………………………… (3) Where, λmax is the largest eigenvalue of A. to directly estimate the consistency of pair-wise comparisons, consistency ratio (CR) is used. From a table of Random Consistency Index (RI), the CR is calculated by separating the CI by a value obtained: Finally, we can evaluate the result by fitting the value in manipulated matrix and multiplying the interdependency factor. After that we can get the synthesizing result of all calculation as whereAkjdis relative significance of sub criteria regarding criteria. Akjal is the stabilized relative importance between sub-criteria. Pja is relative importance with respect to criteria. Sikja is relative importance between sub criteria and alternatives.

Ranking Alternatives: GRA with Fuzzy ANP
In GRA,there are 3 main phases. The first phase is data pre-processing; it is usually essential when the range in one data sequence is dissimilar from others or the sequence scatter range is too large. It transfers the original data sequence to a comparable sequence. / * 1 =

Data collection
The data was collected from 5 midlevel managers from Bangladesh. A close ended questionnaire was developed including criteria, sub-criteria and alternatives. The alternatives those we selected after pre-screening, we represented those as S1, S2, S3, S4 & S5.

Case Study and Discussion
We have evaluated all pairwise comparison matrices and determined the eigenvalue for all comparisons. With a decent consistency ration, we have calculated the dependency among the criteria and goal. It shows the weight for every sub-criterion to criteria, criteria to goals and sub-criteria to goals.
Journal of Tourism, Hospitality and Sports www.iiste.org ISSN (Paper) 2312-5187 ISSN (Online) 2312-5179 An International Peer-reviewed Journal Vol.57, 2021 21 Table1: The weighted value from all matrix Now, Table1 shows the main picture of ANP method. The pairwise comparison of all which described before and also the convergence result is displayed in Table1.Table2 shows the multiplication of all weighted value and the sum of all weighted value according to our 5 alternatives. The GRG is representing the rank for the interdependency of all sub-criteria. After this we are going to multiply the effecting factors and GRG result to find out the best alternatives.

Discussion
In our research, to evaluate CRM system alternatives, the criteria and sub-criteria with quantitative value has been taken for our research approach. We applied the fuzzy ANP instead of primary ANP which was introduced by Saaty. The conventional ANP uses nine scale point. Because of uncertainty and vagueness of decision-makers' preferences, the scale of pairwise comparison which used on primary ANP may not be precise and sufficient enough to measure the accurate judgments. That is why we used fuzzy ANP.
As well as, the GRA method is applied to deal with the grey information or unknown during the decisionmaking approach. The result of fuzzy ANP with GRA approach can use to find the distances more effectively from maxima and minima to referenced vector and high relational grade with right decision. It is noticeable that the inputs are examined in both GRA and Fuzzy ANP process. It also ensures that the preferences for desired outcome are in consistent level and robustness of the result.
The approach is a well-structured methodology that consists of GRA with fuzzy ANP advantage, and attains an efficient solution for CRM system selection problems. The more calculation and more time consuming is the disadvantage of this system. The software that calculates automatically the approach can easily solve these problems with more alternatives and criteria.
On the other way, the cooperation of the organization management from middle to upper is mandatory for this approach and the approach can be work more effectively with a cross validation team. When defining the criteria and alternatives the team must need support from external and internal resources. The cooperation of upper management is motivating the team and its member to confirm that the CRM system is implemented successfully.

Conclusion and Recommendations
The conceptual model in our research is all about the Criteria and sub-criteria. We have found out those criteria which makes our research in context of Bangladesh for a CRM system. We found out the factors which effect the CRM system like IT system Integration, Data Quality and Data integration, Technological Readiness and Customer Information Management Under Technological Context. According to Environmental context, these factors are KM capabilities, Top management Support, Management of Customer Contact Channel, Perceived Benefits and the Environmental context consists of Vendor Support, Government Dimensions, Competitive Pressure. After the findings of Criteria, we have ranked our alternatives in context of Bangladesh. The order that we have find out is S2>S5>S1>S3>S4 which shows the S2 is the optimal solution In our research, we have faced with some problems which is our limitation. The CRM system is not so available in our country why the data is not so available. The result of this approach is dependent on the decision makers input. So, it is more obvious to bias the judgment for any particular system. That is why, it cannot be ruled out. Another problem is that Inconsistency may occur in the comparison which can provide wrong results. There are so many opportunities for expanding the approach. Its structural phase, uncertainty, and usefulness in decision making environment raises practical challenges in real world.The applicability of this approach quite complex. The another is, number of criteria and related sub-criteria can also affect the result of fuzzy ANP to determine the interdependency between them. The number of criteria and sub criteria will also increase the complexity. For future work, it will be more effective to divide more sub-factors into more factors. Knowledge based or an expert system may be developed to calculate such complex measurement more efficiently. There are so many decision making problems in the world. This approach can also work with them properly.