Identification of Customer Preferences for New Service Development in the Electricity Domain

Abstract

The electricity domain is currently facing fundamental change on both, the production and the distribution side. In order to compete on this fast changing market, there is a high need for differentiation possibilities of the electricity suppliers as e.g., through new and innovative services. To set the right incentives to customers and offer them the right value propositions, beside a sound knowledge of the customer preferences also the involvement of the customers in the new service development process by means of co-creation is of high importance. This paper contributes to the new service development process in the electricity domain through the identification of customer segments and customer preference relations, represented by respective utility functions. The main findings presented are the results of a choice based conjoint analysis with a total of 107 respondents executed in order to gather the said customer's preferences regarding possible new future services. The customer segmentation was carried out with latent class. The analysis is embedded in a research framework that uses a multi-method approach with a combination of quantitative and qualitative methods, integrating the customers by means of co-creation.

A hybrid heterogeneous Pythagorean fuzzy group decision modelling for crowdfunding development process pathways of fintech-based clean energy investment projects

“…This will endanger the sustainable performance of clean energy companies in the market. Domigall et al (2014) focused on the new service development process for clean energy generation, conducting a choice-based conjoint analysis with 107 respondents. They identified that companies should satisfy customer needs for success and that new services should be generated based on customer expectations.…”

Improving Patient Preference Elicitation by Applying Concepts From the Consumer Research Field: Narrative Literature Review

“…Reasons for excluding the other seven papers were as follows. One paper was excluded as there was no description of a preference experiment, and another paper was judged not to present any innovative ideas, as these turned out to be already included in standard software [34,35]. Further, one paper focused on forecasting decision behavior instead of quantitative preference measurement, and another discussed a compositional approach to evaluate the attributes one by one, which is not complementary with the concept of real-life decision making [36,37].…”