TY - JOUR

T1 - Complexity in declarative process models

T2 - Metrics and multi-modal assessment of cognitive load

AU - Abbad-Andaloussi, Amine

AU - Burattin, Andrea

AU - Slaats, Tijs

AU - Kindler, Ekkart

AU - Weber, Barbara

N1 - Publisher Copyright: © 2023 The Author(s)

PY - 2023

Y1 - 2023

N2 - Complex process models can hinder the comprehension of the underlying business processes. While several metrics have been suggested in the literature to evaluate the complexity of imperative process models, little is known about their declarative counterparts. In this paper, we address this gap through a suite of metrics that we propose to capture the complexity of declarative process models. Following this, we empirically investigate the impact of complexity, as measured by the suggested metrics, on users’ cognitive load when comprehending declarative process models. Therein, we use a multi-modal approach including eye-tracking and electrodermal activity. The findings of the empirical study provide evidence about the cognitive load emerging as a result of increased model complexity. Overall, the outcome of this paper presents empirically validated metrics to evaluate the complexity of declarative process models. Implementing these metrics and incorporating them in intelligent modeling tools would help assessing the complexity of declarative process models before being deployed. Furthermore, our empirical approach can be adopted by researchers in upcoming empirical studies to provide a multi-perspective assessment of users’ cognitive load when engaging with process models.

AB - Complex process models can hinder the comprehension of the underlying business processes. While several metrics have been suggested in the literature to evaluate the complexity of imperative process models, little is known about their declarative counterparts. In this paper, we address this gap through a suite of metrics that we propose to capture the complexity of declarative process models. Following this, we empirically investigate the impact of complexity, as measured by the suggested metrics, on users’ cognitive load when comprehending declarative process models. Therein, we use a multi-modal approach including eye-tracking and electrodermal activity. The findings of the empirical study provide evidence about the cognitive load emerging as a result of increased model complexity. Overall, the outcome of this paper presents empirically validated metrics to evaluate the complexity of declarative process models. Implementing these metrics and incorporating them in intelligent modeling tools would help assessing the complexity of declarative process models before being deployed. Furthermore, our empirical approach can be adopted by researchers in upcoming empirical studies to provide a multi-perspective assessment of users’ cognitive load when engaging with process models.

KW - Cognitive load

KW - Complexity metrics

KW - Declarative process model

KW - Electrodermal activity

KW - Eye-tracking

KW - Process model comprehension

UR - http://www.scopus.com/inward/record.url?scp=85165224159&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2023.120924

DO - 10.1016/j.eswa.2023.120924

M3 - Journal article

AN - SCOPUS:85165224159

VL - 233

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

M1 - 120924

ER -