Multidimensional Performance Assessment in Financial Contact Centers: An Interval-Valued Fuzzy Logic Approach to Debt Recovery Optimization
Muhammad Kamran
1
(
Department of Business Analytics and Supply Chain Management, Research Institute of Business Analytics and SCM, College of Management, Shenzhen University, China.
)
Muhammad Tahir
2
(
Department of Mathematics, Institute of Numerical Sciences, Gomal University, Dera Ismail Khan, 29050, KPK, Pakistan.
)
Muhammad Farman
3
(
Department of Mathematics, Faculty of Arts and Sciences, Department of Mathematics, Near East University, Nicosia, 99010, Turkey.
)
Narzullayeva Umida Raxmatullayevna
4
(
Center for Reseaech and Innovation, Asia International University, Yangibod MFY, Gijduvon street, House 74, Bukhara, Uzbekistan.
)
Aseel Smerat
5
(
Department of Mathematics, Faculty of Educational Sciences, Al-Ahliyya Amman University, Amman 19328, Jordan.
)
کلید واژه: Call center, Performance measurement, Pythagorean fuzzy sets, Uncertainty modeling, Service quality.,
چکیده مقاله :
Businesses have seen increased difficulties in receiving payments due to the sluggish economic growth. This global tendency significantly impacts both affluent and impoverished nations. Enhancing debt collection practices is important to keeping economic stability. Debt collection firms and affiliated contact centers are enterprises that concentrate on securing payments for outstanding bills. Their proficiency simplifies the payment process. It is widely recognized that assessing the performance of a debt collection agency is challenging and often fraught with ambiguity. A performance measurement is a collection of statistically derived attributes that indicate the efficacy of an activity or its progress. Each project may be administered at an individual, team, departmental, or organizational level. The primary objective is to establish a systematic and unbiased methodology for debt collection by improving the performance of the call center. Initially, our research examined the issue of monitoring call center performance through an exploratory approach. The parent companies utilizing contact centers for debt collection and the call centers have convened numerous times. The proposed methodology offers an equitable assessment of performance criteria. This study initially presents indicator performance functions to ensure the outcomes are equitable. We formulate nine unique functions together with their corresponding parameters to objectively evaluate the effectiveness of the indicators, based on imprecise interval-valued Pythagorean fuzzy preference connections. This article is beneficial, as it presents a straightforward and adaptable method for monitoring the performance of a call center.
چکیده انگلیسی :
Businesses have seen increased difficulties in receiving payments due to the sluggish economic growth. This global tendency significantly impacts both affluent and impoverished nations. Enhancing debt collection practices is important to keeping economic stability. Debt collection firms and affiliated contact centers are enterprises that concentrate on securing payments for outstanding bills. Their proficiency simplifies the payment process. It is widely recognized that assessing the performance of a debt collection agency is challenging and often fraught with ambiguity. A performance measurement is a collection of statistically derived attributes that indicate the efficacy of an activity or its progress. Each project may be administered at an individual, team, departmental, or organizational level. The primary objective is to establish a systematic and unbiased methodology for debt collection by improving the performance of the call center. Initially, our research examined the issue of monitoring call center performance through an exploratory approach. The parent companies utilizing contact centers for debt collection and the call centers have convened numerous times. The proposed methodology offers an equitable assessment of performance criteria. This study initially presents indicator performance functions to ensure the outcomes are equitable. We formulate nine unique functions together with their corresponding parameters to objectively evaluate the effectiveness of the indicators, based on imprecise interval-valued Pythagorean fuzzy preference connections. This article is beneficial, as it presents a straightforward and adaptable method for monitoring the performance of a call center.
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