Category Archives: customer service

Factors affecting intentions to use interactive technologies

This is an excerpt from one of our latest academic articles (that was accepted by the Journal of Services Marketing).

Theoretical implications

Previous studies reported that interactive websites ought to be accessible, appealing, convenient, functional, secure and responsive to their users (Crolic et al., 2021; Hoyer et al., 2020; Kabadayi et al., 2020; Klaus and Zaichkowsky, 2020; Rosenmayer et al., 2018; Sheehan et al., 2020; Valtakoski, 2019). Online service providers are expected to deliver a personalized customer service experience and to exceed their consumers’ expectations at all times, to encourage repeat business and loyal behaviors (Li et al., 2017; Tong et al., 2020; Zeithaml et al. 2002).

Many service marketing researchers have investigated the individuals’ perceptions about price comparison sites, interactive websites, ecommerce / online marketplaces, electronic banking, and social media, among other virtual domains (Donthu et al., 2021; Kabadayi et al., 2020; Klaus and Zaichkowsky, 2020; Rosenbaum and Russell-Bennett, 2020; Rosenmayer et al., 2018; Valtakoski, 2019; Zaki, 2019). Very often, they relied on measures drawn from electronic service quality (e-SQ or e-SERVQUAL), electronic retail quality (eTailQ), transaction process-based approaches for capturing service quality (eTransQual), net quality (NETQual), perceived electronic service quality (PeSQ), site quality (SITEQUAL) and website quality (webQual), among others.

Technology adoption researchers often adapted TAM measures, including perceived usefulness and behavioral intentions constructs, among others, or relied on psychological theories like the Theory of Reasoned Action (Fishbein and Ajzen, 195) and the Theory of Planned Behavior (Ajzen, 1991), among others, to explore the individuals’ acceptance and use of different service technologies, in various contexts (Park et al., 2007; Chen and Chang, 2018). Alternatively, they utilized IAM’s theoretical framework to investigate the online users’ perceptions about the usefulness of information or online content. Very often they examined the effects of information usefulness on information adoption (Erkan and Evans, 2016; Liu et al., 2017).

A review of the relevant literature suggests that good quality content (in terms of its understandability, completeness, timeliness and accuracy) as well as the sources’ credibility (with regard to their trustworthiness and expertise) can increase the individuals’ expectations regarding a business and its products or services (Cheung et al., 2008; Li et al., 2017; Liu et al., 2017). ELM researchers suggest that a high level of message elaboration (i.e., argument quality) as well as the peripheral cues like the credibility of the sources and their appealing content, can have a positive impact on the individuals’ attitudes toward the conveyors of information (Allison et al., 2017; Chen and Chang, 2018; Petty et al., 1983), could affect their intentions to (re)visit the businesses’ websites (Salehi-Esfahani et al., 2016), and may even influence their purchase intentions (Chen and Chang, 2018; Erkan and Evans, 2016).

This contribution differentiates itself from previous research as the researchers adapted key measures from ELM/IAM namely ‘information quality’ (Filieri and McLeay, 2014; Salehi-Esfahani et al., 2016; Shu and Scott, 2013; Tseng and Wang, 2016) and ‘source credibility’ (Ayeh, 2015; Leong et al., 2019; Wang and Scheinbaum, 2018) and integrated them with an ‘interactive engagement’ construct (McMillan and Hwang, 2002), to better understand the individuals’ utilitarian motivations to use the service businesses’ interactive websites. The researchers hypothesized that these three constructs were plausible antecedents of TAM’s ‘perceived usefulness’ and ‘intentions to use the technology’. Specifically, this research examines the direct effects of information quality, source credibility and interactive engagement on the individuals’ perceived usefulness of interactive website, as well as their indirect effects on their intentions to continue using these service technologies.

To the best of the researchers’ knowledge, there is no other research in academia that included an interactive engagement construct in addition to ELM/IAM and TAM measures. This contribution addresses this gap in the literature. The engagement construct was used to better understand the respondents’ perceptions about the ease-of-use of interactive websites, to ascertain whether they are captivating their users’ attention by offering a variety of content, and more importantly, to determine whether they consider them as responsive technologies.

Managerial implications

This study sheds light on the travel websites’ interactive capabilities during an unprecedented crisis situation, when businesses received higher volumes of inquiries through different channels (to change bookings, cancel itineraries and/or submit refund requests). At the same time, it identified the most significant factors that were affecting the respondents’ perceptions and motivations to continue using interactive service technologies in the future.

In sum, this research confirmed that the respondents were evaluating the quality of information that is featured in interactive websites. The findings reported they were well acquainted with the websites’ content (e.g. news feeds, product information, differentiated pricing options, images, video clips, and/or web chat facilities). The researchers presumed that the respondents were well aware of the latest developments. During COVID-19, a number of travel websites have eased their terms and conditions relating to cancellations and refund policies (EU, 2020), to accommodate their customers. Online businesses were expected to communicate with their customers and to clarify any changes in their service delivery, in a timely manner.

The contribution clarified that online users were somehow influenced by the asynchronous content that is featured in webpages. Therefore, service businesses ought to publish quality information to satisfy their customers’ expectations.  They may invest in service technologies like a frequently answered questions widget in their websites to enhance their online customer services, and to support online users during and after the sales transactions. Service businesses could integrate events’ calendars, maps, multi-lingual accessibility options, online reviews and ratings, high resolution images and/or videos in their interactive websites, to entertain their visitors (Cao and Yang, 2016; Bastida and Huan, 2014).  

This research underlines the importance for service providers to consistently engage in concurrent, online conversations with customers and prospects, in real-time (Buhalis and Sinarta 2019; Chattaraman et al., 2019; Rihova et al., 2018; Harrigan et al., 2017). Recently, more researchers are raising awareness on the provision of live chat facilities through interactive websites or via SNSs like WhatsApp or Messenger (Camilleri & Troise, 2022). Services businesses are expected to respond to consumer queries, and to address their concerns, as quickly as possible (McLean and Osei-Frimpong, 2019), in order to minimize complaints.

AI chatbot technologies are increasingly enabling service businesses to handle numerous interactions with online users, when compared to telephone conversations with human customer services representatives (Adam et al., 2021; Hoyer et al., 2020; Luo et al., 2019; McLean and Osei-Frimpong, 2019; Van Pinxteren et al., 2019). The most advanced dialogue systems are equipped with features like omnichannel messaging support, no code deployment, fallback options, as well as sentiment analysis. These service technologies are designed to improve the consumers’ experiences by delivering automated smart responses, in an efficient manner. Hence, online businesses will be in a better position to meet and exceed their customers’ service expectations. Indeed, service businesses can leverage themselves with a responsive website. These interactive technologies enable them to improve their positioning among customers, and to generate positive word-of-mouth publicity.

Limitations and future research avenues

This study has included a perceived interactivity dimension, namely an ‘interactive engagement’ construct within an information adoption model. The findings revealed that the respondents believed that the websites’ engaging content was a significant antecedent of their perceptions about the usefulness of interactive websites. This study also reported that the interactive engagement construct indirectly affected the individuals’ intentions to revisit them again.

In conclusion, the authors recommend that future researchers validate this study’s measures in other contexts, to determine the effects of interactive engagement on information adoption and/or on the acceptance and usage of online technologies. Further research is required to better understand which attributes and features of interactive websites are appreciated by online users. Recent contributions suggest that there are many benefits for service businesses to use conversational chatbots to respond to online customer services. These interactive technologies can offer increased convenience to consumers and prospects (Thomaz et al., 2020), improved operational efficiencies (Pantano and Pizzi, 2020), reduced labor costs (Belanche et al., 2020), as well as time-saving opportunities for customers and service providers (Adam et al., 2021).

Prospective empirical research may consider different constructs from other theoretical frameworks to examine the individuals’ perceptions and/or attitudes toward interactive websites and their service technologies. Academic researchers are increasingly relying on the expectancy theory/expectancy violation theory (Crolic et al., 2021), the human computer interaction theory/human machine communication theory (Wilkinson et al., 2021), the social presence theory (Tsai et al., 2021), and/or the social response theory (Adam et al., 2021), among others, to investigate the customers’ engagement with service technologies.

Notwithstanding, different methodologies and sampling frames could be used to capture and analyze primary data. For instance, inductive studies may investigate the consumers’ in-depth opinions and beliefs on this topic. Interpretative studies may reveal important insights on how to improve the efficacy and/or the perceived usefulness of interactive service technologies.

The full paper is available here: https://www.researchgate.net/publication/366055918_Utilitarian_motivations_to_engage_with_travel_websites_An_interactive_technology_adoption_model

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Live support by chatbots with artificial intelligence: A future research agenda

This is an excerpt from one of my latest 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

(Credit: Chatbots Magazine)

The benefits of using chatbots for online customer services

Frequently, consumers are engaging with chatbot systems without even knowing, as machines (rather than human agents) are responding to online queries (Li et al. 2021; Pantano and Pizzi 2020; Seering et al. 2018; Stoeckli et al. 2020). Whilst 13% of online consumer queries require human intervention (as they may involve complex queries and complaints), more than 87 % of online consumer queries are handled by chatbots (Ngai et al., 2021).

Several studies reported that there are many advantages of using conversational chatbots for customer services. Their functional benefits include increased convenience to customers, enhanced operational efficiencies, reduced labor costs, and time-saving opportunities.

Consumers are increasingly availing themselves of these interactive technologies to retrieve detailed information from their product recommendation systems and/or to request their assistance to help them resolve technical issues. Alternatively, they use them to scrutinize their personal data. Hence, in many cases, customers are willing to share their sensitive information in exchange for a better service.

Although, these interactive technologies are less engaging than human agents, they can possibly elicit more disclosures from consumers. They are in a position to process the consumers’ personal data and to compare it with prior knowledge, without any human instruction. Chatbots can learn in a proactive manner from new sources of information to enrich their database.

Whilst human customer service agents may usually handle complex queries including complaints, service chatbots can improve the handling of routine consumer queries. They are capable of interacting with online users in two-way communications (to a certain extent). Their interactions may result in significant effects on consumer trust, satisfaction, and repurchase intentions, as well as on positive word-of-mouth publicity.

Many researchers reported that consumers are intrigued to communicate with anthropomorphized technologies as they invoke social responses and norms of reciprocity. Such conversational agents are programed with certain cues, features and attributes that are normally associated with humans.

The findings from this review clearly indicate that individuals feel comfortable using chatbots that simulate human interactions, particularly with those that have enhanced anthropomorphic designs. Many authors noted that the more chatbots respond to users in a natural, humanlike way, the easier it is for the business to convert visitors into customers, particularly if they improve their online experiences. This research indicates that there is scope for businesses to use conversational technologies to personalize interactions with online users, to build better relationships with them, to enhance consumer satisfaction levels, to generate leads as well as sales conversions.

The costs of using chatbots for online customer services

Despite the latest advances in the delivery of electronic services, there are still individuals who hold negative perceptions and attitudes towards the use of interactive technologies. Although AI technologies have been specifically created to foster co-creation between the service provider and the customer,

There are a number of challenges (like authenticity issues, cognition challenges, affective issues, functionality issues and integration conflicts) that may result in a failed service interaction and in dissatisfied customers. There are consumers, particularly the older ones, who do not feel comfortable interacting with artificially intelligent technologies like chatbots, or who may not want to comply with their requests, for different reasons. For example, they could be wary about cyber-security issues and/or may simply refuse to engage in conversations with a robot.

A few commentators contended that consumers should be informed when they are interacting with a machine. In many cases, online users may not be aware that they are engaging with elaborate AI systems that use cues such as names, avatars, and typing indicators that are intended to mimic human traits. Many researchers pointed out that consumers may or may not want to be serviced by chatbots.

A number of researchers argued that some chatbots are still not capable of communicative behaviors that are intended to enhance relational outcomes. For the time being, there are chatbot technologies that are not programed to answer to all of their customers’ queries (if they do not recognize the keywords that are used by the customers), or may not be quick enough to deal with multiple questions at the same time. Therefore, the quality of their conversations may be limited. Such automated technologies may not always be in a position to engage in non-linear conversations, especially when they have to go back and forth on a topic with online users.

Theoretical and practical implications

This contribution confirms that recently there is a growing interest among academia as well as by practitioners on research that is focused on the use of chatbots that can improve the businesses’ customer-centric services. It clarifies that various academic researchers have often relied on different theories including on the expectancy theory, or on the expectancy violation theory, the human computer interaction theory/human machine communication theory, the social presence theory, and/or on the social response theory, among others.

Currently, there are limited publications that integrated well-established conceptual bases (like those featured in the literature review), or that presented discursive contributions on this topic. Moreover, there are just a few review articles that capture, scrutinize and interpret the findings from previous theoretical underpinnings, about the use of responsive chatbots in service business settings. Therefore, this systematic review paper addresses this knowledge gap in the academic literature.

It clearly differentiates itself from mainstream research as it scrutinizes and synthesizes the findings from recent, high impact articles on this topic. It clearly identifies the most popular articles from Scopus and Web of Science, and advances a definition about anthropomorphic chatbots, artificial intelligence chatbots (or AI chatbots), conversational chatbot agents (or conversational entities, conversational interfaces, conversational recommender systems or dialogue systems), customer experience with chatbots, chatbot customer service, customer satisfaction with chatbots, customer value (or the customers’ perceived value) of chatbots, and on service robots (robot advisors). It discusses about the different attributes of conversational chatbots and sheds light on the benefits and costs of using interactive technologies to respond to online users’ queries.

In sum, the findings from this research reveal that there is a business case for online service providers to utilize AI chatbots. These conversational technologies could offer technical support to consumers and prospects, on various aspects, in real time, round the clock. Hence, service businesses could be in a position to reduce their labor costs as they would require fewer human agents to respond to their customers. Moreover, the use of interactive chatbot technologies could improve the efficiency and responsiveness of service delivery. Businesses could utilize AI dialogue systems to enhance their customer-centric services and to improve online experiences.  These service technologies can reduce the workload of human agents. The latter ones can dedicate their energies to resolve serious matters, including the handling of complaints and time-consuming cases.

On the other hand, this paper also discusses potential pitfalls. Currently, there are consumers who for some reason or another, are not comfortable interacting with automated chatbots. They may be reluctant to engage with advanced anthropomorphic systems that use avatars, even though, at times, they can mimic human communications relatively well.  Such individuals may still appreciate a human presence to resolve their service issues. They may perceive that interactive service technologies are emotionless and lack a sense of empathy.

Presently, chatbots can only respond to questions, keywords and phrases that they were programed to answer. Although they are useful in solving basic queries, their interactions with consumers are still limited. Their dialogue systems require periodic maintenance. Unlike human agents they cannot engage in in-depth conversations or deal with multiple queries, particularly if they are expected to go back and forth on a topic.

Most probably, these technical issues will be dealt with over time, as more advanced chatbots will be entering the market in the foreseeable future. It is likely that these AI technologies would possess improved capabilities and will be programmed with up-to-date information, to better serve future customers, to exceed their expectations.

Limitations and future research avenues

This research suggests that this area of study is gaining traction in academic circles, particularly in the last few years. In fact, it clarifies that there were four hundred twenty-one 421 publications on chatbots in business-related journals, up to December 2021. Four hundred fifteen (415) of them were published in the last 5 years. 

The systematic analysis that was presented in this research was focused on “chatbot(s)” or “chatterbot(s)”. Other academics may refer to them by using different synonyms like “artificial conversational entity (entities)”, “bot(s)”, “conversational avatar(s)”, “conversational interface agent”, “interactive agent(s)”, “talkbot(s)”, “virtual agent(s)”, and/or “virtual assistant(s)”, among others. Therefore, future researchers may also consider using these keywords when they are other exploring the academic and nonacademic literature on conversational chatbots that are being used for customer-centric services.

Nevertheless, this bibliographic study has identified some of the most popular research areas relating to the use of responsive chatbots in online customer service settings. The findings confirmed that many authors are focusing on the chatbots’ anthropomorphic designs, AI capabilities and on their dialogue systems. This research suggests that there are still knowledge gaps in the academic literature. The following table clearly specifies that there are untapped opportunities for further empirical research in this promising field of study.

The full article is forthcoming. A prepublication version will be available through Researchgate.

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