Novice digital service designers' decision-making with decision aids — A comparison of taxonomy and tags

Highlights

• We analyze how taxonomy and tags help design novices select design techniques.

• Taxonomy leads to lower cognitive effort and higher selection accuracy than tags.

• Increasing cognitive effort reduces the selection accuracy.

• Rational decision style moderates the relation between taxonomy and accuracy.

Abstract

Digital services are a key driver of contemporary businesses. In order to scale the implementation of design-centric development processes, companies increasingly assign design work to design novices. As design novices have limited design knowledge and experience, they are challenged to select adequate design techniques throughout the entire lifecycle of digital services. Thus, providing decision aids to design novices is becoming increasingly important. In this research, we investigate taxonomy-based and tags-based decision aids. We draw on cognitive fit theory to construct a research model explaining the relationship between different decision aids and selection accuracy while considering the cognitive effort and the decision styles of novice designers. To test our hypotheses, we conducted a between-subject laboratory experiment with 195 subjects. Our experimental results provide extensive support to our hypotheses. Taxonomy-based decision aids outperform tags-based decision aids concerning selection accuracy mediated by cognitive effort. Furthermore, the results suggest rational decision style as a moderator in the relationship between taxonomy-based decision aids and selection accuracy. Our results have practical implications: First, taxonomy-based decision aids should be primarily leveraged on decision support platforms supporting design processes. Second, design novices' decision style and cognitive effort are influential factors when developing decision aids to support digital service design processes.

Introduction

Design plays an essential role in all sectors. Design-oriented companies are characterized by higher revenue growth than their benchmark [1]. As we transition toward a digital society, face-to-face contacts are increasingly replaced by digital and human-free services [2]. Given the importance of design, digital service providers benefit from implementing design-centric processes within their organization. Specifically, selecting and applying adequate design techniques are influential tasks, as design techniques guide the design process by providing a set of necessary steps to accomplish a specific design goal [3]. However, hundreds of different design techniques are available [4]. Each design technique has specific design conditions that need to be met to take full advantage of it. Given the increasing amount of design techniques, particularly novice designers¹ are confronted with too much choice, which leads to higher complexity and making the selection of suitable design techniques increasingly difficult [5].

Much research has been done on the development and use of decision aids to assist individuals in making choices [6]. An example is e-commerce [7] leveraging decision aids to support customers in the selection of products, whereby product attributes are classified into predefined categories to reduce the number of alternatives [8]. Scholars have investigated decision aids in manifold contexts, such as e-commerce [9], people-to-people loans [10], benchmarking tools [11], product recommendations [12], and medical diagnoses [13]. Decision aids have various representation formats, such as decision trees [14], rules [8], tables [15], tags [16], and taxonomies [17]. In the context of loan application, the comprehensibility of decision trees, rules, and tables has been compared and evaluated based on their performance implications [18]. In the context of library collections, tags (i.e., a classification based on a flat hierarchy) have been investigated relative to their similarity and difference with expert-based classifications to evaluate the applicability of social tagging for library systems [19]. In the context of e-commerce, product taxonomy (i.e., a classification based on dimensions and characteristics) has been investigated to suggest customized categories [17]. However, there is a research gap concerning the question of how taxonomy-based or tags-based decision aids differ with regard to task performance when considering the individual characteristics, such as decision style, of the decision-maker.

We drew on cognitive fit theory [20] as the theoretical foundation for our research on the effect of decision aids on task performance. Specifically, we investigated cognitive effort as a mediation variable, and we found that structural differences between taxonomy-based and tags-based decision aids affects individual's cognitive efforts and task performance. In addition, we suggest that individuals with diverse decision styles use the available information during the decision-making process in different ways when conducting a selection task [21]. Previous research has investigated the direct influence of intuitive and rational styles on choice quality [22] and task performance [23]. However, decision style is an aspect often overlooked by previous research on cognitive fit theory. Thus, we also investigated individuals' intuitive and rational decision style when using different decision aids, and we formulated the following research questions:

• How do taxonomy-based and tags-based decision aids affect novice designers' cognitive effort and selection accuracy when selecting design techniques?

• How do decision styles influence the relationship between decision aids and selection accuracy?

We conducted a laboratory experiment with 195 subjects to answer these questions. Our work provides several contributions. First, we advance research by constructing a nomological network around the use of decision aids, thus integrating research on information systems and human-computer interaction. Second, given the large number of design techniques and their importance for organizations, we advance the understanding of the use of decision aids and decision styles for selection tasks (e.g., [22]). Third, we extend previous research on cognitive fit theory by highlighting the importance of cognitive effort as well as rational and intuitive decision styles [24]. Finally, practitioners can benefit from our results on the effectiveness of decision aids for novice designers when considering their decision styles and cognitive effort. Beyond this specific context, we believe that our work can also inform the design of classification-based decision aids in general.

This paper is structured as follows: First, we introduce the conceptual foundations of decision aids, cognitive effort, and decision styles. Second, we suggest our research model and develop testable hypotheses. Third, we explain our research approach (i.e., the design of the laboratory experiment). Next, we present our data analysis and results. Finally, we discuss our results, contributions, and limitations.