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Five Types of Customer-Introduced Variability

When it comes to distributing service to customers, the most significant phenomenon refers to the fact that it is challenging to predict their behavior. Service managers should consider this fact by determining the amount of accommodation that they need. It is an essential issue because more accommodation implies more cost. Thus, the given paper will present the five types of customer-introduced variability and explain options to enhance accommodation without disadvantages to service quality.

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To begin with, Loving et al. (2017) explain that the types are arrival, request, capability, effort, and subjective preference variabilities. Jacobs and Chase (2014) support the idea and mention that each of these categories implies particular issues. Thus, arrival variability refers to the time when customers come, request variability determines the customers’ demand, while capability variability relates the customers’ failure to explain their needs (Jacobs & Chase, 2014).

At the same time, effort variability means that some customers do not bother to meet the rules and subjective preference variability addresses customers’ individual peculiarities (Jacobs & Chase, 2014).

Service managers can use a few options to address the variabilities above and keep costs low without damaging a service quality. Borgave and Koranne (2018) explain that these opportunities include classic reduction and uncompromised reduction. The former option stipulates that it is necessary to engage customers in self-service. For example, if restaurants use a reservation system, they minimize the effect of arrival variability. The latter one, however, emphasizes that it is necessary to invest in creating complementary demand to make clients limit their variabilities on their own. In conclusion, the two phenomena above are useful options to restrict the customer-introduced variabilities at low cost, but they require essential effort.


Borgave, S., & Koranne, S. (2018). Service quality tradeoffs: A strategy model. Pacific Business Review International, 11(1), 07-16.

Jacobs, F. R., & Chase, R. (2014). Operations and supply chain management (14th ed.). McGraw-Hill Education.

Loving, V. A., Ellis, R. L., Rippee, R., Steele, J. R., Schomer, D. F., & Shoemaker, S. (2017). Time is not on our side: How radiology practices should manage customer queues. Journal of the American College of Radiology, 14(11), 1481-1488.

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