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One explanation may be related to the previous point suggesting that users were afraid to experience miscommunication. However, as miscommunication was more frequently occurring when the user answered the political attitude statement rather than when asking a question to the system, an alternative explanation, is that users, who are known to make only a minimal effort to request information [13, 14], found using their voice a too big an effort. In our view, this explanation is very plausible especially in light of our finding that in the combined condition users first and foremost used text clicking to request information and not voice. The occurrence of miscommunication does not count as an alternative explanation for the reliance on text in the combined condition, as we observed no miscommunication whatsoever in the combined condition.

In our view, it is therefore likely that a textual communication mode simply fits the user better in the specific usage context of CAVAAs. To be more certain of what has caused the lesser amount of information requests in the voice condition, and therefore probably the lower scores on usability measures, however, it would be worthwhile to conduct a replication study with an improved version of the voice-based chatbot. In 46 C. Another result of the current study is that no differences between the three CAVAA versions were found for perceived enjoyment. This finding is in contrast with earlier studies showing that users of voice-based chatbots frequently enjoy the interaction [25, 33]. Therefore, a novelty effect [9, 16] may have occurred across all three versions since experiences of enjoyment and novelty are closely related [23].

A final results of the current study is that we found no differences between the three modalities for perceived and factual knowledge. As we expected the effects of the political measures to be the result of the actual usage and evaluation of the tool, and as we did find modality effects for these latter measures, the absence of differences for the political measures may be unexpected. In all three conditions relatively high scores on perceived means around 5 on a 7-point scale and factual means around 5 on an 8-point scale knowledge were obtained. This may suggest that answering political attitude statements in a CAVAA, irrespective of modality, leads to relatively high scores on these political measures.

To further understand this finding it would therefore be interesting for a future study to include not only a post-CAVAA measure of perceived and factual knowledge, but also a pre-CAVAA measure. This way the delta of these two measures can be calculated and used as a more fine-grained measure of perceived and factual knowledge. However, differences were found for usefulness and ease of use: the voice-based CAVAA was both considered less easy to use and less useful than the other two modalities. The content analysis of the chatlogs revealed that users request more information in both the text condition and the combined condition as compared to the voice condition.

Moreover, more miscommunication occurred between the tool and the user in the voice condition than in the other two conditions. Finally, results showed that in the combined condition users hardly used the opportunity to control the chatbot using voice and they relied on the type and click functionality in most of the cases. All in all, these results suggest Voice Your Opinion! In order to achieve an optimal fit between users, task, and technology — as formulated by the TTF theory — chatbot developers in the context of political CAVAAs could best develop text-based chatbots, since such chatbots do not only avoid miscommunication, but also stimulate users to request more information in an easy way.

This should ultimately lead to more voters actually casting a vote in real-life elections and to a stronger democracy. A summary of the results of this study has also been published in the Dutch popular-scientific magazine Tekstblad [22]. Ahn, T. Angga, P. IEEE, October 3. Ashktorab, Z. Bohus, D. An investigation of non- understanding errors and recovery strategies. Cho, E. De Graaf, J. In: Cedroni, L. Scriptaweb, Napoli 7. Accessed 8 Feb 8. Computing 12 , — Fryer, L.

Garzia, D. Gemenis, K. Goodhue, D. MIS Q. Kamoen, N. Response difficulties in answering political attitude statements in Voting Advice Applications. Springer International Publishing, Cham Kanda, T. Kim, Y. Kocielnik, R. Krouwel, A. Le Bigot, L. Lee, M. Liebrecht, C. Tekstblad 27 1 , 22—24 McLean, G. Nass, C. MIT Press, Cambridge Pal, D. Riefle, L.

In: Wirtschaftsinformatik Proceedings, vol. Schroeder, J. Shulman, H. Sundar, S. In: Metzger, M. Digital Media, Youth, and Credibility. The John D. Van Camp, K. In: Garzia, D. Matching Voters with Parties and Candidates. Voting Advice Applications in Comparative Perspective, pp. Xu, J. Yang, H. IseB 17 1 , 65—87 Gamification is a powerful instrument to motivate consumers to intensify their brand relationships. Though this potential, the effects of enriching chatbot interactions with gameful experiences on brand engagement has not been studied. Specifically, we investigate whether and to what extent the hedonic and utilitarian values provided in interactions with gamified chatbots affect cognitive, emotional, and behavioral brand engagement.

Based on an empirical study involving a fully functional gamified chatbot, we show that the perceived hedonic value has a major impact on all three brand engagement dimensions, and especially the cognitive dimension. Utilitarian values, though not related to the cognitive dimension of brand engagement, significantly boost the emotional and the behavioral engagement dimensions. These findings point to the potentials of extrinsic and utilitarian motivations for boosting brand engagement also in entertainment-oriented settings like gamified chatbot interactions.

So far chatbots are primarily deployed as non-human versions of frontline service employees resolving simple, standardized tasks in a highly efficient way. Such an augmented view of conversational-based agents for triggering customer engagement beyond solving single service issues has been frequently called for in current literature [35]. Since individuals play games for intrinsic, almost addictive reasons [23], using game design elements as an approach for engagement stimulation is an emerging trend in the marketing field e. Augmenting chatbots through game elements is becoming increasingly possible as major technological advancements give computer agents the ability to interact with users in a much wider variety of contexts [1, 40].

Integrating game elements into chatbots could have the potential to elicit experiences that are similarly powerful as those instilled through gameplay and hence can effectively motivate engagement responses [16]. A gamified interaction detaches individuals from their surroundings and immerses them in the experience thus provoking a sense of natural flow [9]. Therefore, combining the immersive and motivational power of gamification in human-chatbot interactions could be a strategy for revolutionizing the way brands engage consumers.

In this study, by using a real-life chatbot interaction, we empirically investigate how interacting with a chatbot featuring a gamification design affects consumer-brand engagement. We argue that infusing a gamified experience into a chatbot generates not only hedonic but also utilitarian value for users, which in turn strengthens cognitive, emotional, and behavioral engagement with the brand. The empirical findings provide nice contributions to the recently emerging research on the intersection of gamification and conversational technologies. We shed light on the mechanisms that enable gamified chatbots to effectively boost brand engagement by identifying two opposing psychological processes utilitarian vs.

We show that embedding gamification in a given system is effective when the game elements can impact the targeted users in terms of what they personally value [19, 62]. It is not surprising that firms see chatbots also as a potential means to enhance firm and brand engagement. Companies are heavily investing in conversational agents to engage their customers better, and the use of these agents is predicted to 52 S. Silva et al. Gamification is an emerging technology process that enables to mimic the entertaining experiences that games are all about, by using game elements just like playful design does, while having rules, goals, and feedback systems [36].

Literature on gamification applications in marketing stresses that brand engagement occurs mostly because of interactive and challenging experiences and that game elements can facilitate such experiences [6, 36]. There are many types of game components, such as avatars, points, badges, levels, gifting, levels, as well as leaderboards [59], that are more likely to be recognised by users and better integrated into chatbots [44]. This makes the integration of such gamification elements into chatbots a clear option for engagement stimulation.

Despite this, no study so far has examined how gamified chatbots enhance brand engagement. As a result, since in the marketing domain a lot of attention has been cast on how consumers can be triggered to become more aware of and engage more intensively with the brand, marketing practitioners have recently started seeking new solutions to overcome consumer engagement hurdles by using insights from the research on games [29, 36, 61]. Since consumer-brand engagement occurs due to interactive and co-created customer experiences with a brand, it is expected that brand engagement may increasingly occur when iteratively using a gamified service [30]. While some researchers consider engagement to have one dimension, namely behavioural [54] some others believe engagement to be a complex state hanging on several dimensions that deserve further understanding [33] with a one-dimensional concept not fully reflecting its complex scope [32].

Based on this, the present study includes all three main dimensions, i. Cognitive engagement is the psychological investment or degree of interest [58] when interacting with a brand. Emotional engagement is related to the development of emotional connections and affection towards a brand [33]. The examination of these three different foci of brand engagement is important for several reasons. While these different foci often coexist in a given consumption context [14], one focus might prevail over another in the Value Creation in Gamified Chatbot Interactions and Its Impact 53 formation of relevant consumer relationship outcomes according to the context and the different foci may play various and variable roles in shaping engagement in terms of the underlying psychological processes that may be activated.

According to Means-Ends model by Woodruff and Gardial [62], which is one of the most prominent value models in the literature, consumers act according to what might produce desired benefits and avoid negative consequences. In other words, consumers make a self-evaluation of the correlation between what they value from different perspectives and the perceived value offered by the experience, and in case the experience supports them, engagement behaviours are expected [13]. Therefore, personal values work as antecedents of consumer engagement [69], meaning that when aiming at engagement outcomes, managers must understand how consumers perceive value so they can develop experiences that outperform the ones from competitors [63].

Generally, various types of value are taken into account and depending on what is expected from an experience, consumers are contemplated as either problem-solvers or as individuals that seek emotional stimulus [28]. Babin et al. In previous research, Carpenter et al. So, there is room to believe that, ideally, both types of value should be considered for consumer engagement creation, especially in case of new technologies. Perceived utilitarian value refers to the utilitarian outcome resulting from some type of conscious pursuit of an intended consequence [3], which is defined as a way of assessing the functional and economic benefits that consumers receive for choosing a product or service [45]. In general, utilitarian value has been considered to be driven by the desire to fill a basic need or to accomplish a functional task [11].

Overall, when considering utilitarian value, consumers assess the perceived value of an experience through a more cognitive perspective [52], which is recognized as a determinant of consumer engagement as well as behavioural intention [57], reflecting judgments of time-saving, function, and convenience, that relate to a more task-oriented and rational form of evaluation [47]. Utilitarian value has a marked influence on the attitude toward Internet users [18], while showing direct positive effects on consumer satisfaction and word-of-mouth [4]. Based on the above we propose that in a gamified human-chatbot interaction: H1: Utilitarian value is positively related to cognitive engagement with the brand H3: Utilitarian value is positively related to the emotional engagement with the brand H2: Utilitarian value is positively related to behavioural engagement with the brand Overby and Lee [45] define perceived hedonic value as an assessment of the experimental benefits of choosing a specific product or service, such as the enjoyment, the fun, and the excitement perceived when consuming [28], meaning that an activity that offers these hedonic elements should motivate users to engage in the experience [55].

Being hedonic value intrinsically motivating, this should drive higher levels of engagement in the interaction and equally reflect the positive experience to who conceived such activity [30]. Overall, when considering hedonic value, consumers assess the perceived value of an experience by evaluating emotional and affective factors [34]. From this perspective, if the experience provides a relevant hedonic benefit for consumers, like gamification should do due to its inherent nature, this should drive continuing engagement behaviours e. Like the utilitarian value, the hedonic value was also found to have a positive effect on preference, satisfaction, and behavioural intention [56]. As found in Chan et al. According to Cronin et al.

According to Batra et al. Since for more hedonic-oriented users, pleasure and fun are primary benefits while for more pragmatic-oriented users, utilitarian benefits, such as reaching a goal should be more reasoned, brand engagement may be impacted by hedonic elements of extrinsic attributes in all contexts where a utilitarian benefit, such as a discount, might be achieved. Thus, in keeping with the above rationale, we propose that in a gamified human-chatbot interaction: H4: Hedonic value is positively related to the cognitive engagement with the brand H5: Hedonic value is positively related to the behavioural engagement with the brand H6: Hedonic value is positively related to the emotional engagement with the brand The proposed model would then be the one depicted in the next figure.

This is less likely to happen when scenarios or screenshots are used, which happens in most of chatbots studies. Research model A pilot study was run on 60 participants to understand what elements and gamification strategies revealed more relevant results when applied to human-chatbot interaction. These game elements were retrieved from the DMC Pyramid by Werbach and Hunter [59] and included three categories: 1 game dynamics, in the form of emotions, narrative, progression, and constraints; 2 game mechanics, in the form of challenge, reward system, and feedback; 3 game components, in the form of points and badges. Each question had three alternative answers for participants to choose from and was related to a different level of difficulty, to give them a sense of progression and challenge.

Badges were used as the main game elements in the interaction, as in Hamari [25]. Participants received instant feedback about whether they answered questions correctly and about their progression in the game. If the answer was correct, participants received a recognition badge and were able to progress to the next level to achieve the highest discount possible. If their answer was wrong, participants would be given the code for the discount associated with the reached level. Figure 1 below provides an extract of the gamified chatbot design. The chosen products for the study were suitcases because of the necessity to use a neutral product with no cultural interferences, to avoid biased results Fig.

Extract from the gamified chatbot design 3. The data collection took place throughout December The sample included a population of young Portuguese consumers possessing a Facebook Messenger account that was required to enable the interaction with the chatbot. Later, the following constructs of interest were measured: 1 hedonic value, 2 utilitarian value, 3 cognitive engagement, 4 emotional engagement, and 5 behavioural engagement.

To measure both hedonic value and utilitarian value see Table 1 below , we adapted the scales from Babin et al. The three dimensions of engagement, including the cognitive engagement dimension, emotional engagement dimension, and the behavioural engagement dimension, were assessed to evaluate the brand engagement of consumers. This scale was chosen based on its variety of engagement focimaking it a more uniform measure for consumer engagement — and its ability to clarify the dimensionality of engagement.

It recognises consumer engagement as a three-dimensional concept and adds more detail to its conceptualization. While the behavioural dimension is related to the level of effort and time spent [33], the cognitive aspect of engagement is about the level of absorption that a user is able to reach, and the emotional dimension relates to the measure of enjoyment and enthusiasm [58]. The majority of participants were able to reach the final levels. Those participants 38 who could not reach the final levels were discarded from the analysis, which led to a final sample of participants 74 men, 91 women.

The majority of participants are between 18 and 23 More than half of participants declared to have already experienced an interaction with a chatbot Almost all participants liked the products showcased by the chatbot The final model consisted of 5 constructs and 21 items. All the VIF values and factor loadings for the remaining constructs are reported in Table 1. All indicators have exceeded the threshold established at 0. Similarly, the minimum cut-off values, established by Fornell and Larcker [20], at 0. In all cases the AVE values exceed the corresponding squared inter-composite, correlational values. Discriminant validity was also assessed by HeteroTrait-MonoTrait ratio HTMT of correlations, because of its superior performance compared to more traditional methods [27], with values below the threshold 0.

Standardized loadings were used to assess the indicator reliability. For the HTMT criterion, the threshold level of. Henkel et al. Abd-alrazaq, A. Informatics , 2. Ajzen, I. Almahri, F. Barak, A. Bellman, S. Bergström, A. Brandtzaeg, P. In: Kompatsiaris, I. Carpenter, M. Health Matters 24 47 , 74—84 9. Carpenter, C. ILR Rev. Chaix, B. Chang, I. Expert Syst. Chazin, D. Case Stud. Cheung, A. Chocarro, R. Croes, E. In: Chatbot Research and Design, pp. Davis, F. Fish, J. Prevention Sci. Fishbein, M. Addison-Wesley, Reading Fitzpatrick, K. Fulmer, R. Encyclopedia Britannica n.

Goklani, B. Mindinventory, 15 September Guo, X. Ho, A. Hoofnagle, C. ID Hoy, M. Inkster, B. Isaias, P. Smart Educ. Jackson, S. Kretzschmar, K. Lee, Y. ACM Human-Comput. Lee, J. Factors 46 1 , 50—80 Lipschitz, J. Liu, K. Lucas, G. Ly, K. Internet Interv. Magsamen-Conrad, K. Mandal, D. Managing Inf. IJMIT 4 4 , 1—11 McInroy, L. Child Adolesc. Work J. Issue Tour. Mostafa, R. Nadarzynski, T. Digital Health 5, Neufeld, D. Pennebaker, J. Powell, J.

Prakash, A. Pacific Asia J. Rogers, E. Diffusion of Innovations. Free Press, New York Russell, S. Schueller, S. Sheehan, K. Steele, L. Taddicken, M. Tarhini, A. People 29 4 , — Toch, E. User Model. A literature review. Venkatesh, V. Woebot Health. Relational agent for mental health, 12 January Accessed 31 Jan Mental health support, for everyone. Wysa Retrieved 27 Jan Yarns, B. Psychiatry Rep. Yi, M. Conversational troubleshooting is an increasingly popular technology that consists in utilising dialogue agents to support users of a system through a conversation-based question-answering process, typically through a chatbot.

Despite their widespread use, current troubleshooting technologies lack a thorough integration with the applications on which they are overlaid, as they rely only on text to help the users. Finally, it proposes a solution which engages the user multi-modally: with text in the chat and hints in the graphical interface. At a help desk — physical or virtual — customers of a service can ask for information and guidance. Thanks to this procedure, users do not have to read instruction manuals or lists of frequently asked questions; instead, they are helped by a system expert [3].

In recent years conversational agents have been employed to automate troubleshooting services [5,22] thanks to their availability and low costs associated compared to their human counterparts. Abbo et al. First, current technologies are not context-aware: they are not designed to complement the information extracted from the conversation with contextual data gathered from the interaction on the graphical interface [21,25].

A multi-modal response would allow the troubleshooting system to give actionable solutions to users, for example, by highlighting the relevant element on the interface, further improving the quality of the response [9,11,27,35]. A conversation designer or a domain expert can not modify the user experience without minimal programming skills. In the same way, programmers who want to modify the functionalities of the troubleshooting application must have a good understanding of the application domain since they will inevitably intervene in the conversation structure. To overcome these issues, we propose TINI, a conversational kit for troubleshooting that is natively context-aware and multi-modal.

TINI is an open-source plugin ready to be integrated into a new or existing web application. When TINI is embedded into a website, users can describe the issue faced in a dedicated chat panel in the application interface. In this paper, we improve and extend the work on MCTK published in a poster paper [1]. On top of that, we formalise the problem with a mathematical formulation and describe a complete case study to clarify its usage. Enhancing Conversational Troubleshooting with Multi-modality 2 2.

Dialogue management can be performed by following two main kinds of techniques [17]: handcrafted and probabilistic. The probabilistic strategies for dialogue management consist in training an algorithm from large data sets of samples [42]. These dialogue managers require a large base of conversation examples to be trained, but, based on the samples provided, they can respond to unforeseen user messages that were not considered at design time. Some solutions adopt a mixed approach. For example learning from a corpus with an external knowledge base or ontology [2,26]. These approaches are largely uni-modal: the user can interact using only the conversation, be it in written or spoken form, and they require other external components to achieve a multi-modal interaction [24].

However, multi-modality in chatbots for user assistance in troubleshooting remains largely unexplored. Subramaniam et al. The automated approach avoids manually setting up a complex knowledge base by employing automated learning techniques [40]. Embedding it into a new or existing website allows users to receive suggestions on how to solve the issues they encounter. A system is multi-modal if it supports more than one means of interaction: text, graphics, gestures, and more [34]. Multiple research studies prove that the response to multi-modal stimuli is better compared to unimodal [27,35]. In addition, since the primal interaction ground for a web application is the graphical interface, we want to intervene and guide the user in that environment as much as possible, instead of asking the user to switch to a completely separate conversational one [7].

Contrarily to most used chatbot frameworks, TINI will natively support multi-modality. Separation of Concerns. We want to empower the conversation designer and the domain expert to customise the behaviour of TINI independently of the developer. In addition, the structure containing this data must be accessible to those with little programming knowledge. An extensible solution allows adding new behaviours and modifying the existing ones with minimal intervention in its internal structure.

Andy is a user of a web application developed by Barb. When Andy needs help, he can click on a button added by TINI to start chatting with the conversational agent. Andy visits the web app and loads the image he wants to edit, then proceeds to use the available tools to crop and rotate the image. He clicks on G. A photo editing application, enhanced with the TINI tool. On the right, the chat panel displays the conversation with the chatbot; on the left, the available tools are displayed in a panel, with those relevant to the solution highlighted in red. The developer can customise every aspect of the interface. At the same time, the system highlights in the interface the relevant tool and the panel in which it can be found, as shown in Fig.

Modules and Parameters Definition. In order to represent the context, we can consider the options available to the user and group them into modules. The modules can be active or not, depending on whether they are available to the user. For example, in a photo editing application, the modules could be various tools, such as the brush, crop, or stamp tool. In addition, each module can have one or more parameters, representing the settings of its module. In the image editor, these could be the size and transparency levels of the brush tool Enhancing Conversational Troubleshooting with Multi-modality and the proportions of the area in the crop tool.

The system will then receive the information about which modules are active, as inactive modules are not available to the user and cannot be used to solve the issue the user is facing. Mathematical Formulation. In mathematical terms, the problem can be formalised as follows. Let M be the set of all the modules in the system and P the set of parameters; we suppose that each parameter is associated with one and only one module.

Let q be the question of the user. A description of the execution will be given in Sect. The sentence is used to identify the problem faced, through intent extraction. Sequence of operations performed by the system. This table, as reported in Table 1, contains the connections between problem types, modules, parameters, and answers. This structure balances maintainability, relevant when reading and updating the table, with extensibility, which is important when expanding the table with new problem types and solutions.

Adding a module or a parameter is as easy as adding a row to the table, adding a new 1 www. Enhancing Conversational Troubleshooting with Multi-modality Table 1. Each row corresponds to a module parameter, and every column is a problem TINI wants to address. The conversation designer can directly operate on this table to add problems and utterances and create new connections between problems and parameters. It consists of and can be used as a self-contained Vue. Interface Component. This component is available to the developer and can be embedded in the web application.

It displays a circular overlay button on the bottom right of the screen. The button allows toggling the visibility of a chat panel, which handles the communication with the conversational agent. When the user sends a message, the interface will forward it to the Core component, and when it receives a response, it will display it in the chat; in addition, it will update the information on which modules and parameters should be highlighted in the application. The component is implemented in Vue. The Core implements the logic of the system. It is initialised with information about which modules are currently active.

The details about the algorithm are reported in Sect. The response is returned to the Interface Component. This part is implemented in JavaScript and is independent of the interface: in the future, other interfaces can be added, extending the library to other frontend frameworks. We chose NLP. Configuration Handler. Finally, the Configuration Handler retrieves and parses the utterances and configuration table documents and provides their contents to the Core component.

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