Using artificially generated pictures in customer-facing systems: an evaluation study with data-driven personas

Abstract

We conduct two studies to evaluate the suitability of artificially generated facial pictures for use in a customer-facing system using data-driven personas. STUDY 1 investigates the quality of a sample of 1,000 artificially generated facial pictures. Obtaining 6,812 crowd judgments, we find that 90% of the images are rated medium quality or better. STUDY 2 examines the application of artificially generated facial pictures in data-driven personas using an experimental setting where the highquality pictures are implemented in persona profiles. Based on 496 participants using 4 persona treatments (2 × 2 research design), findings of Bayesian analysis show that using the artificial pictures in persona profiles did not decrease the scores for Authenticity, Clarity, Empathy, and Willingness to Use of the data-driven personas.

Can Unhappy Pictures Enhance the Effect of Personas? A User Experiment

“…Table 1 reports the constructs and measurement items (indicators). The indicators for empathy, usefulness, realism, completeness, and likability were adopted from the Persona Perception Scale [70], an instrument specifically developed for measuring user perceptions of personas and applied in several previous persona user studies [61,62,64,69]. The indicator for physical attractiveness was adopted from Berscheid and Walster [9].…”

Designing a Data Visualisation and Analysis Tool for Supporting Decision-Making With Public Transportation Network

“…Nowadays, we are constantly surrounded with multiple streaming data sources (Internet Of Things, sensors…) that support our daily lives and activities. From that observation, design engineering also becomes more influenced by data-driven inspirations in several areas of interest, for instance: datadriven construction of personas (Stevenson and Mattson, 2019) (Salminen et al, 2020); creation of an undergraduate curriculum on data materialisation, i.e. learning how to interact with data (Beghelli, Huerta-Canepa and Segal, 2019); exploration of the design space in Design by shopping (Abi-Akle, Minel and Yannou, 2017); elaboration of a data-driven concept generation and evaluation approaches for supporting designers in the early phases of the design process (Han et al, 2020).…”