This is an excerpt from one of my contributions on the use of responsive chatbots by service businesses. The content was adapted for this blogpost.
Suggested citation: Camilleri, M.A. & Troise, C. (2022). Live support by chatbots with artificial intelligence: A future research agenda. Service Business, https://doi.org/10.1007/s11628-022-00513-9
Chatbots are usually considered as automated conversational systems that are capable of mimicking humanlike conversations. Previous research suggested that, at times, human beings are treating computers as social beings (Nass and Moon 2000; Nass et al. 1994; Rha and Lee 2022) although they are well aware that dialogue programs do not possess emotions, feelings and identities. Individuals may still perceive that service chatbots have some sort of social presence when they interact with them (Leung and Wen 2020; McLean et al. 2020; Pantano & Scarpi, 2022; Schuetzler et al. 2020), even though these technologies are capable of responding to thousands of potential users at once (Caldarini et al. 2022).
Currently, few academic contributions are using theoretical bases like the social presence theory (Grimes et al. 2020; Schuetzler et al. 2020) and/or the social response theory (Adam et al. 2021; Huang and Lin 2011), to explore human-computer interactions, and/or the utility of dialogue systems like chatbots, albeit a few exceptions. A few commentators made specific reference to related theories to describe the characteristics of chatbots or of conversational 4 agents, that are primarily used for consumer engagement purposes (Cheng and Jiang 2020; Kull et al. 2021; Mostafa and Kasamani 2021; Nuruzzaman and Hussain 2020).
The human machine communication theory was formulated in response to the growing number of technologies like AI and robotics, that are designed to function as message sources, rather than as message channels (Flavián et al. 2021). Lewis et al. (2019) contended that social bots, and even a few chatbots have pushed into the realm of what was thought to be a purely human role. Wilkinson et al.’s (2021) study shed light on the human beings’ perceptions about conversational recommender systems. In this case, the authors went on to suggest that experienced users trusted their disruptive technologies and had higher expectations from them.
Other researchers examined the online users’ trust toward chatbots in various settings (Balakrishnan and Dwivedi 2021; Borau et al. 2021; Cheng and Jiang 2020; De Cicco et al. 2020; Hildebrand and Bergner 2021; Kushwaha et al. 2021; Mozafari et al. 2021; Nuruzzaman and Hussain 2020; Pillai and Sivathanu 2020). Eren (2021) confirmed that the users’ performance perceptions regarding the use of chatbots positively affected their customer satisfaction levels in the banking sector. This finding is in line with the expectancy violation theory, as individuals form expectations following their interactions with information systems (Chopra 2019; Neuburger et al. 2018).
The individuals’ social expectations from conversational technologies are especially pronounced when they incorporate cues of humanness (Adam et al. 2021; Pfeuffer et al. 2019), that are not present in traditional systems like websites, mobile applications, and databases (Belanche et al. 2021). The anthropomorphic features of AI dialogue systems make it easier for humans to connect with them (Adam et al. 2021; Becker et al. 2022; Forgas-Coll et al. 2022; Van Pinxteren et al. 2020).
In many cases, a number of quantitative researchers have investigated online users’ perceptions and attitudes toward these interactive technologies. Very often, they relied on valid measures that were tried and tested in academia. Some utilized the theory of reasoned action (Huang and Kao, 2021), the theory of planned behavior (Brachten et al. 2021; Ciechanowski et al. 2019), the behavioral reasoning theory (Lalicic and Weismayer 2021), the technology acceptance model (Kasilingam 2020) or the unified theory of acceptance and use of technology (Mostafa and Kasamani 5 2021), as they sought to investigate the individuals’ utilitarian motivations to use chatbot technologies to resolve their consumer issues. Others examined the users’ gratifications (Cheng and Jiang 2020; Rese et al. 2020), perceived enjoyment (De Cicco et al. 2020; Kushwaha et al. 2021; Rese et al. 2020), emotional factors (Crolic et al. 2021; Lou et al. 2021; Schepers et al. 2022; Wei et al. 2021), and/or intrinsic motivations (Jiménez-Barreto et al. 2021), to determine whether they were (or were not) affecting their intentions to use them.
The full paper can be downloaded via: Academia, OAR, Repec, ResearchGate, Springer, and SSRN.
Substantia Mea 
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