# Assessing the Utility of an Interaction Qualities Framework in Systematizing the Evaluation of User Control

Aletta Smits, Chris Detweiler, Ester Bartels, Katja Pott, Esther van Der Stappen, Suzanne Van Rossen, Karine Cardona, Shakila Shayan, Jürgen Ziegler, Koen Van Turhout

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Aletta Smits, Chris Detweiler, Ester Bartels, Katja Pott, Esther van Der Stappen, et al.. Assessing the Utility of an Interaction Qualities Framework in Systematizing the Evaluation of User Control. 19th IFIP Conference on Human-Computer Interaction (INTERACT), Aug 2023, York, United Kingdom. pp.185-195, 10.1007/978-3-031-61698-3_17. hal-05305357

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# Assessing the Utility of an Interaction Qualities Framework in Systematizing the Evaluation of User Control

Aletta Smits<fnref n="1" />(✉) ID, Chris Detweiler<fnref n="2" /> ID, Ester Bartels<fnref n="1" /> ID, Katja Pott<fnref n="3" /> ID, Esther van der Stappen<fnref n="4" /> ID, Suzanne van Rossen<fnref n="4" /> ID, Karine Cardona<fnref n="5" /> ID, Shakila Shayan<fnref n="1" /> ID, Jürgen Ziegler<fnref n="6" /> ID, and Koen van Turhout<fnref n="1" /> ID

**Abstract.** The user's experience with a recommender system is significantly shaped by the dynamics of user-algorithm interactions. These interactions are often evaluated using interaction qualities, such as *controllability*, *trust*, and *autonomy*, to gauge their impact. As part of our effort to systematically categorize these evaluations, we explored the suitability of the interaction qualities framework as proposed by Lenz, Dieffenbach and Hassenzahl. During this examination, we uncovered four challenges within the framework itself, and an additional external challenge. In studies examining the interaction between user control options and interaction qualities, interdependencies between concepts, inconsistent terminology, and the entity perspective (is it a user's *trust* or a system's *trustworthiness*) often hinder a systematic inventory of the findings. Additionally, our discussion underscored the crucial role of the decision context in evaluating the relation of algorithmic affordances and interaction qualities. We propose dimensions of decision contexts (such as 'reversibility of the decision', or 'time pressure'). They could aid in establishing a systematic three-way relationship between context attributes, attributes of user control mechanisms, and experiential goals, and as such they warrant further research. In sum, while the interaction qualities framework serves as a foundational structure for organizing research on evaluating the impact of algorithmic affordances, challenges related to interdependencies and context-specific influences remain. These challenges necessitate further investigation and subsequent refinement and expansion of the framework.

**Keywords:** Interaction Qualities · Interaction Aesthetics · Algorithmic Affordances · User Control · Human-AI Interaction · Pattern Library

<footnote n="1">¹ HU University of Applied Sciences Utrecht, Utrecht, The Netherlands
aletta.smits@hu.nl</footnote>

<footnote n="2">² The Hague University of Applied Sciences, The Hague, The Netherlands</footnote>

<footnote n="3">³ University of Applied Sciences and Arts Northwestern Switzerland FHNW, Basel, Switzerland</footnote>

<footnote n="4">⁴ Avans University of Applied Sciences, Breda, The Netherlands</footnote>

<footnote n="5">⁵ Appealy Design, Amsterdam, The Netherlands</footnote>

<footnote n="6">⁶ University of Duisburg-Essen, Duisburg, Germany</footnote>
# 1 Introduction

When using a recommender system, such as a navigation system guiding our journey or a social media platform curating our feed [1], the user's experience with the tool significantly influences their decision to comply with the advice. This also holds true for decision support systems aiding in personal and professional choices, such as selecting a new computer, finding a suitable elective course, or evaluating a loan application [2–4]. Ultimately, how the user perceives the tool's support determines whether they will choose to adopt, ignore, or negotiate the AI's recommendations [5–7]. Initially, studies on these AI tools were technical in nature and focused on qualities such as *accuracy* and *efficiency* and how various algorithmic models affected those qualities [8]. It was only at a later stage that the user experience was considered part of the equation [9, 10], and studies started to examine the impact of recommender systems on other qualities of the user experience, such as *novelty, diversity, fairness, safety and autonomy* [5, 11, 12].

Initially such qualities were primarily attributed to the algorithm [13]. For example, Smits et al. [14] model interaction qualities as a result of a user experience that is (partly) generated by an automated system. Nowadays, research interests have expanded to include two other relevant approaches to algorithmic tools: a contextual approach, in which the recommender is considered in its immediate and societal context-of-use, and an interactional approach, which studies the design of interaction mechanisms between user and algorithm [15]. Studies on the context of AI, inspired concepts such as responsible AI, contestable AI, algorithmic redress, algorithmic literacy, and algorithmic aversion [16–19]. This paper focuses, however, on the increasing volume of studies examining interactions between users and algorithms and how they impact interaction qualities.

These *user control mechanisms* [20], or *algorithmic affordances* [21], allow the user to input data in various stages of the recommendation process. Algorithmic affordances, therefore, provide the user with tangible control over an algorithm's recommendations [20, 21]. This control can, for instance, can be limited to a straightforward rating of recommendations (which influences a next round of recommendations), but can also involve more complex interactions, such as continuously adjusting the weight of parameters, so the user can actively attempt to achieve a better fit between recommendations and user needs [22–24]. Rather than as a monologue, where the AI presents a list of recommendations that the user can consume, these studies frame working with an AI tool more as a conversation [25]. The algorithmic affordances are then the tools that shape the turn taking mechanisms, the length of the turn, and the type of information that can be conveyed in each turn.

The impact of algorithmic affordances on interaction qualities, such as *controllability, autonomy, transparency, interpretability, autonomy, fun*, etc. has been demonstrated overwhelmingly in literature [3, 8, 11, 20, 26–34]. However, currently, no systematic overview exists in which the individual studies and their impact on the various interaction qualities have been brought together. We are in the process of constructing such an overview, and presenting the results, amongst other venues, in a dynamic library of algorithmic affordances [21, 35, 36]. Our hypothesis is that such a framework with generalized relations between algorithmic affordances and interaction qualities, will allow for an easier transfer of outcomes of academic studies to problems that practitioners face in real-time. In preparation of this library, we are examining systematic means
to evaluate the impact of algorithmic affordances on interaction qualities. During the INTERACT23 workshop on Algorithmic Affordances in Recommender Interfaces [37], the participants explored a current framework of interaction qualities [38, 39], its challenges, and its potential to systematize impact of algorithmic affordances in a pattern library. This paper is a reflection on the discussions during this part of the workshop.

## 2 Interaction Qualities

When Noam Tractinsky in the late 1990s connected the “subjective” beauty of an interaction to the “objective” usability of that interaction [40], it inspired initially a line of research on how the visual beauty of an interface may affect the perception of and the interaction with the product [41]. Later, at the urging of, among others, Djajadinigrat et al. [42], “a beautiful appearance” shifted to “a beautiful interaction”. From that point onwards, *efficiency* – which had long been the main characteristic of *usability* – was no longer the ultimate evaluation of interaction; it had become too narrow a concept [43].

Other qualities, such as *surprise, fun, competences, relatedness, control, diversity*
demanded their fair position in the evaluation of interaction. In an elaborate review of studies on interaction qualities carried out by Lenz et al. [39], a set of 151 interaction qualities were identified. Broadly, these qualities seemed to fall into two groups: One dealt with spatio-temporal attributes on a senso-motoric level describing physical aspects of interaction (e.g., *speed, duration, pressure, orientation*). The other consisted of qualities that referred to “feelings and meaning emerging through interaction” (e.g., *surprise, thrill, challenge, trust*) [39]. Not surprisingly, many of the identified qualities in each group overlapped (both in the sense that different concepts were used for similar experiences; and that identical concepts were used for different experiences). However, what was surprising, was that hardly ever relations between the groups of interaction qualities were systematically discussed let alone postulated. Following Hassenzahl [44], Lenz et al. [38] attempt to remedy this oversight by proposing a hierarchical relationship between the identified qualities. They present a framework where the one set of qualities – the How-qualities or senso-motoric qualities – is contended to evoke the other set of qualities, the Why-qualities (or *experiential goals*). They organized the Why-qualities in categories, derived from the analysis of psychological literature on motivations [45] (see Table 1).

How-qualities, such as *seamless design* or *fast-paced interaction*, are then supporting the evocation of experiential goals [38]. In a later paper, Hassenzahl et al. [43] present an empirical study in which they examine the relationship between interaction qualities (experiential goals), senso-motoric qualities, and contextual aspects. They contrast the opening of a bottle of wine (the goal of the interaction) in the scenario where a sommelier in a restaurant opens the bottle, and in a picnic-scenario. The senso-motoric qualities of the corkscrew in Fig. 1A are a better fit for a picnic scenario, where *relatedness* is an intended experience. In a restaurant scenario, where the sommelier, aims for *competence* as an intended experience, the senso-motoric qualities of the corkscrew Fig. 1B are a better fit.

For this workshop, we take Lenz et al.’s framework [38, 39] as a starting point – since as far as we know – it is the one framework that explicitly and systematically connects
<table><caption>**Table 1.** Experiential goals: ‘why’-qualities (Lenz et al. 2013).</caption><thead><tr><td>Why-categories</td><td>Example</td></tr></thead><tbody><tr><td>Stimulation</td><td>Excitability, resolution, surprise, playful, etc</td></tr><tr><td>Security</td><td>Control, trust, anxiety, anticipation, trustworthy</td></tr><tr><td>Competence</td><td>Challenge, risk, transparency, difficulty</td></tr><tr><td>Autonomy</td><td>Freedom of interaction, identity, control, autonomy, openness, privacy</td></tr><tr><td>Relatedness</td><td>Fellowship, connectedness, company, personal, social action space</td></tr><tr><td>Meaning</td><td>Expression, seductivity</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** The two corkscrews in Hassenzahl et al. [43].</figcaption></figure>

different levels of interaction qualities. The question to be discussed during the workshop was then: what is the relationship between algorithmic affordances on the one hand, and their experiential goals on the other hand, with as sub-questions:

* Has Lenz et al.’s framework enough potential to organize research findings of algorithmic affordances in recommender interfaces on interaction qualities?

* How is the decision context – the ‘scenario’, in Hassenzahl et al.’s terms [43] – related to the impact of the algorithmic affordance on the interaction qualities?

In this workshop we explored these questions with the hci-researchers (4), experts on ethical application of AI (4), and industry partners (1) that were present.

## 3 Challenges and Discussions

During our discussion, and preceding literature review, considerable challenges emerged, most of them integral to the framework, but some more contextual.
## 3.1 Challenges Within the Framework

Firstly, when considering a systematic overview of how algorithmic affordances impact interaction qualities, we need to acknowledge that this impact originates from different sources (see Fig. 2). For instance, which part of this impact can be attributed to the fact that a specific interaction is possible at all; and which part of this impact can be attributed to a particular implementation of this interaction. Additionally, which part can be attributed to the fit between the algorithmic affordance and the context; and which part can be attributed to the stage of the recommendation process?

Take the straightforward algorithmic affordance of *rating*. The fact that it is possible to rate recommended items at all has an impact ("Google maps does not allow to dislike a selected route; it can be changed, but there is no rating"). Depending on the context, affording the user to rate can be considered inappropriate ("How many stars for the recommendation to discharge this patient from the critical care unit?"). How the rating is implemented can affect the experience, too ("Often, I can only give anywhere between 1 and 5 stars, but not the zero stars that I wanted"). And finally, the moment in the recommendation process may also make a difference ("If I can discard an unknown recommended movie immediately when it has been recommended, then I don't like the title, the thumbnail or any other superficial feature of the movie; when I discard after I started watching, or even finished it, then I discard for experiential reasons").

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Four different sources for impact on experiential goals</figcaption></figure>

The second challenge that was identified is similar to the one Lenz et al. [39] encountered. In studies on user-algorithm interactions, we encountered different terms used interchangeably (*explainability*, *interpretability*, *transparency*, *trust*, *trustworthiness*), and similar terms used for different experiences (again, *explainability*).

The third challenge is related to the second, namely the fact that interaction qualities have a perspective. Linguistically, a user feels *trust* when they deem the recommender system *trustworthy*. Strictly speaking *trustworthiness* is then not an experiential goal but an adjective that describes the recommender system. Trustworthiness may evoke the experiential goal of *trust*. *Autonomy* can refer to how the user feels, but also to the extent of which the system is proactive and makes autonomous decisions. As a final example, *explainability* is often considered a feature of the algorithm, while *interpretability* is the experiential goal that is hopefully achieved with an algorithm that can explain itself. However, the two terms are often used interchangeably. For this workshop, we concluded that all and every use of such terms should be analyzed for its perspective and treated
appropriately, specifically because while the one hopefully entails the other, it is not
necessarily true. The fact that a platform aims for *trustworthiness*, does not necessarily
mean the user experiences the hoped-for *trust*.

Fourthly, in many cases the two levels of experiential goals and senso-motoric quali-
ties do not seem sufficient. For instance, Lenz et al. consider both *transparency* and *trust*
to be an experiential goal (see Table 1). However, *trust* is often defined as a compos-
ite concept: an experiential goals that is evoked via other experiential goals. Glikso &
Wooley [7], for instance, consider *cognitive trust* in AI to develop along the paths of
*transparency*, *tangibility*, *reliability* and *autonomy* of the AI. Some of these qualities are
positioned at the Why-level by Lenz et al. [38]. For Ali et al. [46], an AI's trustwor-
thiness is a compound of *stability*, *satisfaction*, *fairness*, *responsibility*, *transparency*,
*interpretability*, *explainability* and *robustness*. *Explainability*, too, is often considered a
composite of *transparency*, *control* and *interpretability* [47]. Apparently, while How-
level attributes are considered to evoke the Why-level goals, the Why-level goals can
also be instrumental in evoking other Why-level goals, and those relations can be inter-
dependent and mutual. *Autonomy* can be needed to experience *control*. *Control* can be
needed to experience *autonomy*.

We considered the following options to address these challenges: 1. by assuming a
level of interaction qualities that is hierarchically still higher than Lenz et al.'s expe-
riential goals; at that point we would evaluate the 'composing qualities of high-level
why-qualities'; 2. to leave it as it is and acknowledge that the experiential goals are
interdependent and mutually defining; 3. to construct an entirely different framework.
All options present considerable challenges. To discuss just the challenges of the first –
intuitively most logical – solution: consider Lenz et al.'s theoretically grounded 'super-
categories' of interaction qualities (see Table 1). *Trust*, the quality that is in studies on user
control and human-ai-interaction most often encountered as a composite concept, does
not appear at that superlevel. *Autonomy*, however, features both as a 'super-category'
that includes qualities such as *control*, and as a 'regular' category. Similarly, *relatedness*
is a 'super category', but a term that is often used interchangeably with *relatedness*,
namely *connectedness*, is not. A promising suggestion for composite categories were
the three motivations identified in Ryan and Deci's self-determination theory, namely
*autonomy*, *mastery* and *connectedness* (also often labeled as *autonomy*, *competence* and
*relatedness*) [48, 49]. These three, however, do not include *trust* as a goal, although all
three of them have trust as a conditional experience. Competence for instance can only
be experienced when there is also an experience of trust in, for instance, skills (either to
carry out the task, or to be able to bounce back from failing the task).

## 3.2 Contextual Challenges

Finally, we discussed the impact of the situational context on the relation between algo-
rithmic affordances and interaction qualities, since a poor fit may also mean that decision
makers do not accept the results of the recommender [50, 51]. An ‘I feel lucky’-button
may generate surprise and anticipation in the context of a search engine or when look-
ing for a movie for Saturday night but will evoke entirely different experiences in the
context of a decision support system assisting with the evaluation of loan applications. A
large part of the workshop's discussion was then dedicated to identifying features of the decision context that could potentially affect this fit. Table 2 presents an initial overview.

<table><caption>**Table 2.** Suggested dimensions of the decision contexts of an algorithmic affordance.</caption><thead><tr><th>Entity</th><th>Dimensions</th></tr></thead><tbody><tr><td>Recommender System</td><td>level of autonomy (pro-active, re-active),<br>level of interaction,<br>algorithmic model (filtering, collaborative, etc.)<br>availability</td></tr><tr><td>User</td><td>acceptance of AI<br>belief in competence to deal with AI,<br>belief in competence in the domain<br>availability (time, attention)</td></tr><tr><td>Context</td><td>time pressure,<br>risk (reversibility of the decision, impact of the decision, investment in<br>the decision)<br>distractions (other people, other entities)<br>single source/contradicting advice (more recommenders or just the<br>one)</td></tr></tbody></table>

The factors *risk*, *user expertise* and *time pressure* also feature prominently in Simkute et al.'s ERT-model of decision contexts, a psychologically informed model of the most important factors in decision contexts [47, 52]. The time in this workshop was too short to explore this venue thoroughly, but it was suggested that organizing algorithmic affordances based on their fit with context features could be an interesting line of research.

## 4 Conclusion

To our knowledge, Lenz et al.'s framework of interaction qualities [38, 39] is the one framework that consistently connects experiential goals to implementations and found empirical evidence for these connections. The framework, too, has been tested in situations where the contextual fit played a role [43]. Initially, it provides therefore an adequate framework to help systematize the host of literature that examines the impact of algorithmic affordances on interaction qualities. However, the workshop's discussions have identified challenges that jeopardize the usefulness of this framework in its current presentation. The minor challenge that terms are not used consistently, both with regard to meaning and with regard to perspective can, with careful analysis of the terms and definitions used in the various studies, be resolved.

With regard to the framework itself, the main challenge is, however, the fact that it does not address hierarchical relationships, interdependencies, and mutual definitions at the Why-level. Those are, however, essential in order to understand how a quality as *trust* or *autonomy* is impacted by various algorithmic affordances.
Finally, it has also been established that decision contexts affect the relationship between experiential qualities and algorithmic affordance. In the one context, a user's affordances to rate recommendations can evoke a sense of control, in another annoyance or even disgust. It is therefore necessary to connect the framework of interaction qualities to dimensions of a decision context. A systematic study of this question is currently being launched, starting with a systematic literature review of studies that have examined algorithmic affordances, or user control mechanisms, and their impact on sense of controllability, trust, fairness and other experiential goals.

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