ORCID Profile
0000-0001-6218-0172
Current Organisation
Caritas Institute of Higher Education
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Publisher: Emerald
Date: 05-02-2016
DOI: 10.1108/BPMJ-04-2015-0039
Abstract: – As a response to increasing global market competition, companies in various industries tend to identify and manage customer relationship to increase profit performance. Companies commit more resources to identify their VIP customers and retain them by all means. The purpose of this paper is to develop a customer relationship management (CRM) business process management (BPM) model to identify airline customers with different degree of relationship and profit potential, and select the highly profitable customers for developing retention strategy and processes, and convert the less profitable into profitable corporate accounts. – This study innovatively apply the well-known techniques including CRM and relationship marketing models, fuzzy analytic hierarchy process (FAHP), and technique for order preference by similarity to ideal solution (TOPSIS) in the BPM research. This novel approach analyzes longer term customer profit and value potential, and prioritizes corporate accounts as the basis for setting appropriate customer service levels and improving the CRM process. This hybrid model is able to capitalize on the benefits of these methods and offset their deficiencies. Most importantly, it can be customized to various industries without complex modification. – This study uses data of an airline company to validate feasibility of the proposed CRM BPM model. The results indicate that this model is able to classify the customers based on various criteria and sub-criteria, thus allowing companies to introduce appropriate service levels to deal with different categories of customers, and improve CRM process so as to maximize customer profit and value potential. – This CRM BPM model and analysis provide managers extensive customer knowledge, more analytical and fact-based decision-making support, and a stronger focus on return on investment in sales and marketing. Knowing the profit and value potential generated by in idual corporate customer makes it easier to establish the link between the CRM and the profit outcome. This model also benefits the organization and its stakeholders by allocating more resources to the targeted customer relationships that are profitable or valuable, and makes marketing more accountable in its marketing programs. – This study makes the first move to innovatively apply the well-known techniques including CRM and relationship marketing models, FAHP, and TOPSIS in the BPM research.
Publisher: Springer Science and Business Media LLC
Date: 02-06-2021
Publisher: Informa UK Limited
Date: 06-04-2017
Publisher: Informa UK Limited
Date: 04-2007
Publisher: Informa UK Limited
Date: 31-01-2022
Publisher: SAGE Publications
Date: 08-03-2012
Abstract: Direct tests between corporate social responsibility (CSR) and firm performance (FP) have been argued to be spurious. Following this line of argument, the present study tests a mediated model in understanding the CSR–FP relationship. Specifically, we posit that reputation and customer satisfaction mediate fully the CSR–FP relationship. Based on the results from a s le of 280 Australian firms, the findings suggest that CSR is linked with FP. However, the effect is indirect: while CSR is linked to both reputation and customer satisfaction, reputation alone mediates the CSR–FP relationship. The results are interesting, suggesting that to reduce ambiguity surrounding the CSR–FP relationship scholars need to significantly expand studies that address moderating and mediating variables. Discussion is given to these findings along with paths for future research.
Publisher: Inderscience Publishers
Date: 2010
Publisher: Informa UK Limited
Date: 16-04-2017
Publisher: Springer Science and Business Media LLC
Date: 14-08-2010
Publisher: Emerald
Date: 04-06-2018
DOI: 10.1108/BPMJ-01-2016-0015
Abstract: Frequent food safety incidents caused widespread consumer concerns. Even though food safety is one of the weakest links in the fresh food supply chain and influences consumer food choice in ways different from the quality dimension, this factor is hardly proposed as one of the key traditional supplier selection criteria (e.g. quality, delivery, and price) in the literature. The purpose of this paper is to develop a business process decision model to assess the non-compensating food safety sub-criteria in order to disqualify fresh food suppliers that cannot reach the minimum threshold for low probable food safety failure. The preferred fresh food suppliers can minimize the risk of food safety failure and the associated huge food safety failure costs spanning from private consumer anguish to social distress that cause unbearable costs of sales loss and damage to brand image in business. This study proposes a novel approach that combines several well-established multi-criteria decision making (MCDM) techniques, including fuzzy AHP (FAHP), TOPSIS, and ELECTRE, and innovatively apply to analyze supplier performance and prioritize potential fresh food suppliers. This hybrid business process model can enforce compliance to all the five non-compensatory sub-criteria of food safety. Since ELECTRE is a non-compensatory MCDM method, it is therefore particularly applicable for disqualifying high risk fresh food suppliers from further full scale supplier performance evaluation by FAHP and TOPSIS. This hybrid business process decision model is able to capitalize on the strengths of these MCDM methods and offset their deficiencies. This study uses data of an international supermarket chain to validate feasibility of the proposed model. Results indicate that this model is able to assess the non-compensating food safety sub-criteria via the ELECTRE method in order to disqualify fresh food suppliers that cannot reach the minimum threshold for low probable food safety failure. Only the preferred suppliers with the required food safety capability can proceed to the second stage of the supplier selection process. Assessment via the TOPSIS method reveals the ranking order of those top performing suppliers according to their relative scores along all the supplier selection criteria. The TOPSIS ranking results with the selection of the suppliers C, E, A, and F are robust and consistent across all the different scenarios. Application to the fresh food industry is possible with the aid of the MCDM methods. The contribution to the body of knowledge in this teaching and research field demonstrates the importance of first identifying the order qualifier for disqualifying those suppliers that do not satisfy the food safety requirements via the ELECTRE method. The proposed assessment procedure complies with the regulatory policy on food safety, and would influence public policy in applying the best practice of food safety regulation. Without first qualifying the potential suppliers on the basis of food safety, wrong decision can be made to select those high food risk suppliers that have relatively higher overall scores in other supplier selection criteria. Using the assessment results has positive economic and commercial impact on the purchasing managers to formulate appropriate purchasing and supplier development strategy to enhance supplier’s food safety performance, whilst maximizing the overall supplier portfolio performance. The improved supplier’s food safety performance will certainly benefit the society’s quality of life as well. Based on the analytical MCDM methods of FAHP, TOPSIS, and ELECTRE, purchasing managers can operationalize the Hill’s framework of order qualifier and winner that has primarily been used in the literature and manufacturing industry. This study represents the first move to innovatively apply the FAHP, TOPSIS, and ELECTRE methods to operationalize the Hill’s framework of order qualifier and winner that has primarily been used in the literature and manufacturing industry. Application to the fresh food industry to validate the feasibility of the proposed model has been conceived and implemented in this study. Analysis of the data inputs of a supermarket chain via the three MCDM methods generate the results that fulfill the purpose of achieving the research objective of identifying and managing the supplier base that can deliver the best supplier performance, conditional on first passing the fresh food safety test.
Publisher: TECSI
Date: 19-04-2016
Publisher: Informa UK Limited
Date: 13-09-2017
Publisher: Emerald
Date: 11-07-2016
DOI: 10.1108/IMDS-10-2015-0410
Abstract: – Strategic analysis of customer profitability for assessing market segmentation and reconfiguring customer relationship management (CRM) activities remains the key factor for achieving high return on CRM investment. The purpose of this paper is to map the profit-based ranking of corporate customers into the current market segments, with a view of determining the relative profitability of each market segment. – This study develops a novel model that combines activity-based costing (ABC), CRM, fuzzy analytic hierarchy process (AHP), and technique for order preference by similarity to ideal solution (TOPSIS) methods to evaluate strategically customer profitability and prioritizing corporate accounts. This case study airline company has invested heavily in CRM over the past seven years on integrating multi-functional departments that touch customers. The airline operations management and marketing functions provide key inputs. Results of the hybrid model validate feasibility of the proposed model. – The airline management makes use of the ranking results to optimize customer profitability by reconfiguring marketing programs, integrated schedule design, fleet assignment, maintenance routing, crew scheduling, and real-time optimization of schedule recovery in the aftermath of disruptions or irregularities. The proposed model also directs the marketing function to customize service offerings and introduce appropriate service levels to engage customers of different segments for the purpose of maximizing corporate profitability. – Significant amount of investment is necessary to design and implement the extensive CRM database and systems to assure customer data quality and availability so as to bear fruits in the proposed hybrid model. These data requirements can especially be a critical barrier for small to medium-sized companies. – This hybrid model is able to capitalize on the benefits of the ABC, CRM, fuzzy AHP, and TOPSIS methods and offset their deficiencies. Most importantly, it can be applied to various industries without complex modification. – This study represents the first move to adopt the fuzzy AHP and TOPSIS methods to analyze the ABC and CRM data inputs of an airline company. In mapping the profit-based ranking of corporate customers into the current market segments, the relative profitability of each market segment can be determined.
Publisher: Common Ground Research Networks
Date: 2006
Publisher: Elsevier BV
Date: 2020
Publisher: University of Adelaide Press
Date: 2015
Publisher: Emerald
Date: 03-08-2018
DOI: 10.1108/IMDS-08-2017-0347
Abstract: The purpose of this paper is to propose an outcome-based process optimization model which can be deployed in companies to enhance their business operations, strengthening their competitiveness in the current industrial environment. To validate the approach, a case ex le has been included to assess the practicality and validity of this approach to be applied in actual environment. This model embraces two approaches including: fuzzy logic for mimicking the human thinking and decision making mechanism and data mining association rules approach for optimizing the analyzed knowledge for future decision-making as well as providing a mechanism to apply the obtained knowledge to support the improvement of different types of processes. The new methodology of the proposed algorithm has been evaluated in a case study and the algorithm shows its potential to determine the primary factors that have a great effect upon the final result of the entire operation comprising a number of processes. In this case ex le, relevant process parameters have been identified as the important factors causing significant impact on the result of final outcome. The proposed methodology requires the dependence on human knowledge and personal experience to determine the various fuzzy regions of the processes. This can be fairly subjective and even biased. As such, it is advisable that the development of artificial intelligence techniques to support automatic machine learning to derive the fuzzy sets should be promoted to provide more reliable results. Recent study on the relevant topics indicates that an intelligent process optimization approach, which is able to interact seamlessly with the knowledge-based system and extract useful information for process improvement, is still seen as an area that requires more study and investigation. In this research, the process optimization system with an effective process mining algorithm embedded for supporting knowledge discovery is proposed for use to achieve better quality control.
Start Date: 2015
End Date: 2017
Funder: Competitive Research Funding Schemes for the Local Self-Financing Degree Sector
View Funded ActivityStart Date: 2021
End Date: 2023
Funder: Research Grants Council, University Grants Committee
View Funded Activity