Voice Assistant vs. Chatbot – Examining the Fit Between Conversational Agents’ Interaction Modalities and Information Search Tasks

With rapid advances in artificial intelligence, specifically natural language processing, speech holds promise as an interaction modality between humans and computers (Enholm et al., 2021; Hirschberg & Manning, 2015). Voice assistants (VAs) enable speech interaction with existing devices (e.g., smartphones) and serve as the main interface of new devices (e.g., smart speakers). Similar to chatbots, which interact in written form, VAs engage the user in conversations that are set up to assist in various tasks, such as the answering of questions and smart home control (Hoy, 2018). Both VAs and chatbots can be referred to as conversational agents (CAs) (Diederich et al., 2022; Gnewuch et al., 2017). Companies increasingly adopt CAs in banking, healthcare, and e-commerce, with customer service being a key use case (Behera et al., 2021; Woodford, 2020). A customer service encounter describes “any customer-company interaction that results from a service system that is comprised of interrelated technologies, human actors (employees and customers), physical/digital environments and company/customer processes” (Larivière et al., 2017, p. 2). CAs enable cost savings while still providing human-like interaction, thereby bridging the existing service provision gap between frontline employees and self-service technologies (Adam et al., 2021; Larivière et al., 2017). However, the availability of speech- and text-based conversational interfaces to communicate with companies raises questions about the impact of the interaction modalities on customer service encounters (De Keyser et al., 2019).

Extant literature has examined costumers’ satisfaction with service encounters to assess whether offering a CA is beneficial (Diederich et al., 2019; Diederich et al., 2020). In the service literature, customers’ satisfaction with the service is used as a measure for the company’s success in addressing their expectations and needs (Dai & Salam, 2020; McKinney et al., 2002). To satisfy customers, service encounters must provide them with both utilitarian and hedonic value (Childers et al., 2001; Chiu et al., 2014; Jones et al., 2006). Because satisfied customers tend to continue using a service (Bhattacherjee, 2001) or to buy repeatedly (Bartl et al., 2013), satisfaction is an appropriate construct to evaluate the effect of CAs’ distinct interaction modalities on service encounters. Indeed, existing research comparing speech- and text-based interactions shows that speaking generates positive attitudes toward the CA (Cho et al., 2019; Novielli et al., 2010; Schroeder & Schroeder, 2018). These attitudes are driven by both utilitarian and hedonic values: Studies on users’ perceived efficiency find that speaking is faster than typing, which increases users’ productivity (Le Bigot et al., 2007; Ruan et al., 2017). Speech is also more intuitive and natural (Kock, 2004), increasing its ease of use and decreasing cognitive effort. Users also report simply enjoying the interaction, which stresses VAs’ hedonic benefits (Pal et al., 2020; Yang & Lee, 2019). However, research explicitly comparing speech- and text-based interactions reports inconclusive results. While speaking is faster and easier (Ruan et al., 2017), listening to the CA’s answers reduces the interaction speed, thereby increasing the relative efficiency of text-based conversations (Le Bigot et al., 2004).

Whether speaking or texting is more beneficial during a service encounter depends on the type of task the customer wants to solve. Extant service literature distinguishes between (goal-directed) searching and (experiential) browsing tasks (Hoffman & Novak, 1996; Hong et al., 2004). Depending on the customers’ goal when interacting with, e.g., a chatbot, the CA’s conversational style should be more or less friendly (Chen et al., 2021). Initial research on speech- and text-based CAs found that users’ preference for one interaction modality over another can change depending on the task and context of the interaction. Krämer et al. (2009) show that users’ preference for text interaction decreased after service failures, while speech interaction preferences increased. Cho et al. (2019) find that attributing a human likeness mediates users’ attitudes in utilitarian tasks but not in hedonic tasks. Additionally, VA users frequently adopt VAs for simple routine tasks, but remain reluctant to use them for more complex tasks (Ma & Liu, 2020). Consequently, no interaction modality is superior to the other. While previous research has examined the fit between specified tasks and technologies to support these tasks (Goodhue & Thompson, 1995), the literature has not yet considered differences between speech- and text-based interaction for information search tasks. Against this background, we aim to answer the following research question:

To address this research question, we investigate the applicability of speech- and text-based conversational interaction to different information search task types. Because speech- and text-based interactions differ in the way humans produce, transmit, and process information, we use cognitive load theory and cognitive fit theory (Vessey & Galletta, 1991) as underlying theories to derive our hypotheses. Based on this theoretical foundation, task-technology fit theory (Goodhue & Thompson, 1995) guides the inclusion of the task type in the research model. We conduct a 2 (interaction modality: speech vs. text) × 2 (search task type: goal-directed vs. experiential) between-subject laboratory experiment to test our research model. The results show that the search task’s perceived goal-directedness moderates the interaction modality’s effect on users’ perceptions of the interaction. Thereby, our study extends cognitive fit theory to conversational information requests and presentations and task-technology fit theory to users’ choice of interaction modalities. Our findings further inform practitioners about the benefits of speech-based interactions over text-based interactions, and of their effects on customers’ satisfaction. Considering that visual and auditory modalities suit distinct types of information and tasks in different ways, it is essential that service providers understand how different tasks and interaction modalities jointly affect customers’ satisfaction in the service encounter (De Keyser et al., 2019; Lee et al., 2001). In this way, depending on the task’s goal-directedness, system designers can learn when and how the offering of speech as an interaction modality is beneficial.

This paper is structured as follows: In section 2, as the conceptual background of our study, we provide an overview of extant research on CAs and the relevant theories. Section 3 introduces the research model and derives the underlying hypotheses. We describe the experiment including its procedure, measurements, and data collection process in section 4 and report our analyses’ results in section 5. Section 6 presents the main findings and indicates their implications in the light of extant literature. The paper briefly concludes in section 7.

The use of CAs promises benefits in terms of efficiency, specifically in the service encounter (De Keyser et al., 2019; Kraus et al., 2019). However, we do not know to what extent these benefits depend on the interaction modality. Le Bigot et al. (2004), for instance, find that text interaction is faster than speech. With current technical advancements, however, VAs offer more natural conversational interaction possibilities and faster processing speeds (Dubiel et al., 2018). Consequently, comparisons in prior literature may only hold to a certain extent (Schmitt et al., 2021). Recent qualitative investigations on VA use suggest that users particularly value the efficiency of speech interaction (Moussawi, 2018; Rzepka, 2019). From a users’ perspective, speech production is faster than writing because it does not involve the physical process of writing (Akinnaso, 1982). Hence, we assume that customers achieve and perceive more efficiency in speech interaction compared to text. At the same time, extant research shows that reading a system’s prompts requires less time than listening to them because humans can selectively retrieve relevant information from text (Rubin et al., 2000). In contrast, listening to the VA’s information in a sequential manner takes time and reduces users’ efficiency (Dubiel et al., 2020; Schmitt et al., 2021). The amount of information that users must listen to depends on the tasks they conduct. Because customers listen to less information during tasks they perceive to be more goal-directed (Hong et al., 2004), the advantages of speech interaction should be more pronounced if the level of goal-directedness is high. However, if customers want to compare and evaluate different alternatives in experiential tasks, they need to listen to more information sequentially in speech-based interactions, which reduces their efficiency compared to text-based interactions (Dubiel et al., 2020). Therefore, we hypothesize:

Besides efficiency, cognitive effort is a suitable variable to account for differences between interaction modalities and tasks (Hong et al., 2004). While Le Bigot et al. (2004, 2007) consistently show that cognitive effort is higher for speech than for textual interaction, Ruan et al. (2017) report the opposite. Recent technological advancements and media naturalness theory offer a potential explanation for this phenomenon (Kock, 2004; Schmitt et al., 2021). Humans perceive speech interaction as more natural and intuitive, which reduces cognitive effort (Kock, 2004). However, any decrease in naturalness through speech recognition errors or synthetic speech output weakens this effect. As VAs have recently advanced technologically, we refer to recent empirical results (Dubiel et al., 2018; Foley et al., 2020; Ruan et al., 2017) and suggest that speech interaction requires less cognitive effort than text interaction. This is in line with the basic assumption of media naturalness theory and is based on the fact that speech does not require the formal written structuring of information (Akinnaso, 1982). Producing text involves a process of consciously applying intentionally learnt rules, while humans intuitively build and process speech. In particular, we expect this effect to be strong if customers have a high goal-directed perception of a task. For low goal-directedness, customers are likely to use more interaction turns to explore the CA’s information, analogous to the increasing number of clicks on a website (Hong et al., 2004). These interactions increase cognitive effort because customers need to remember the agent’s information, which becomes more difficult considering the increasing information load of multiple turns (Paas et al., 2004). Consequently, when evaluating different alternatives in experiential search, the sequential information representation in speech-based interactions applies cognitive pressure on customers as they need to retain different options in their memory (Dubiel et al., 2020). Customers can, however, selectively retrieve relevant information from the system’s written answers, which reduces their cognitive effort compared to listening to and memorizing the information given in spoken interactions (Rubin et al., 2000). Therefore, we assume speech requires more cognitive effort in experiential tasks:

Moreover, CAs promise to provide personal customer encounters, similar to relationships with human personnel (Larivière et al., 2017). With their anthropomorphic characteristics and social cues, CAs can promote feelings of humanness and social presence (Diederich et al., 2019). Speech-based interactions trigger such perceptions more than text-based interactions do, which leads to more positive (Cho et al., 2019) and personal attitudes (Novielli et al., 2010). This indicates that users prefer VAs because they enjoy the interaction itself (Rzepka, 2019). Indeed, extant research shows that perceived enjoyment has a significant positive effect on humans’ intention to use VAs (Pal et al., 2020; Yang & Lee, 2019). Hence, we propose that speech interaction will lead to greater enjoyment than textual interaction. We furthermore assume that this effect will be weaker if customers perceive tasks to be highly goal-directed because their focus on the particular goal leaves less space for enjoying the actual interaction (Hoffman & Novak, 1996). In contrast, for tasks perceived as less goal-directed, the interaction serves to explore different options. In this way, the benefits of speaking in a two-sided conversation should become more pronounced as the duration of the interactions increases and because of the hedonic nature of experiential tasks (Li et al., 2012; Novak et al., 2003). So, our next hypotheses are:

Finally, we assume that cognitive effort, efficiency, and enjoyment affect customers’ satisfaction with the service outcome, i.e., the search result. Satisfaction is mostly conceptualized as a state in which an interaction meets or exceeds a customer’s expectation (McKinney et al., 2002), and, therefore, is also dependent on the customers’ expectations of the service encounter. Technology is used in the service encounter to provide efficient service which, in turn, should enhance satisfaction with the encounter (Larivière et al., 2017). Because customers using CAs in the service encounter have similar expectations and request fast service (Kraus et al., 2019), meeting these expectations should positively affect their satisfaction. Furthermore, initial results on customers’ satisfaction determinants of voice commerce show high convenience expectations (Kraus et al., 2019), which indicates that keeping customers’ cognitive effort low promotes their satisfaction regarding service encounters via CAs. This relationship has already been examined for online shopping, showing that reducing cognitive effort through electronic decision aids has a positive impact on customers’ satisfaction (Bechwati & Xia, 2003). Hence, we assume that high cognitive effort negatively impacts customers’ satisfaction with the search outcome. Furthermore, extant research has shown that customers’ satisfaction are not only driven by extrinsic motivations, but also by intrinsic expectations (Lowry et al., 2015). Since CAs offer an enjoyable experience through their interactive conversational capabilities (Cho et al., 2019; Novielli et al., 2010), we expect that enjoyment also drives customers’ satisfaction with the service encounter:

Figure 1 illustrates the hypotheses on the fit between interaction modality and task goal-directedness as regards their impact on customers’ satisfaction. Following our research question and the extant literature, we hypothesize that speech differs from textual interaction in its perceived efficiency, cognitive effort, and enjoyment, thus ultimately affecting customers’ satisfaction with the service encounter. However, this relationship can change depending on the search task’s perceived goal-directedness.

We tested our research model using partial least squares (PLS) structural equation modelling carried out with SmartPLS3 (Ringle et al., 2015). Therefore, we determined the significance of path coefficients by running the bootstrapping resampling approach with 5,000 subsamples (Chin, 1998). For all other analyses, we relied on IBM SPSS Statistics 26.

Overall, our study sheds first light on the impact of task characteristics on the benefits of speech- based interaction modalities for information search tasks. We specifically show that, in terms of perceived efficiency, cognitive effort, enjoyment, and satisfaction, speech-based interaction modalities are perceived as being superior to text-based interactions. We further show that the task’s goal-directedness influences these effects. In this way, we provide initial evidence that opens new avenues for future research; research that can build on these results and that can inform practitioners about the applicability of both speech- and text-based CAs. With continuous technical advancements, companies increasingly need to decide whether a human, a VA, or a chatbot should answer customers’ service queries. We hope to inspire and motivate future research in this area.