Chatbots: Can They Satisfy Customers in the Banking Sector?

Chatbots, digital assistants, voice-activated services, concierge robots, and virtual assistants are all different terminologies used for similar type of AI-enabled systems (Prentice & Nguyen 2020). In this research, the focus is on the chatbots which are utilised by the banks, as a customer service representative.

Various authors and researchers have defined AI in different ways. From the time it was first discovered to the present time, the definitions have evolved as well, keeping in mind the various perspectives. To start with, in 1950, Turing (P. 435) defined it as “such a human interaction between another human and the machine where an individual is unable to differentiate between the human and the machine, hence the machine is termed to be intelligent”. Haenlein and Kaplan (2019) stated that “such an ability of the system that can explain the external data accurately, that can learn from the same source, and by using and incorporating that data, specific tasks and goals can be achieved by being flexible”.

Chatbot technology is being applied in various businesses and industries like banking, hospitality, tourism, and healthcare. With the help of these conversational chatbots, different businesses can collect and store larger sets of data in terms of variety, while assisting them to become more cost effective and sustainable (Campbell et al. 2020; Um, Kim & Chung 2020).

With the help of appropriate coding, programming, and algorithms, chatbots are trained to identify similar customer queries and respond to them in a specific pattern (Campbell et al. 2020). Belanche et al. (2020) highlighted that robots (chatbots) are being considered as frontline employees for most of the businesses, as they can easily communicate with the customers, receive and store larger data of information, and are cost effective. Chatbots are well-equipped with intuitive and empathetic skills, which is helping them in replacing all kinds of jobs performed by humans in various industries, particularly those of customer service representatives (Belanche et al. 2020; Huang & Rust 2020a, b). However, Um, Kim and Chung (2020) contended albeit chatbots make work easier for organisations to utilise them, they still have drawbacks; failure to understand and provide appropriate service to the customer, incur additional costs; maintenance, installation and training.

John McCarthy; the father of “artificial intelligence” coined this term first in 1956 during a summer research project (Haenlein & Kaplan 2019). Since the research in the field of AI-enabled systems (chatbots) is still in its infancy stage, the following Table 1 is devised based on the key information provided by various journal articles.

The world has witnessed substantial developments in AI-based innovations across the global banking sector, which serves customers that are using diverse banking networks (Kumar, Sujit & Charles 2018). There is still an impending need for research when it comes to the implementation of AI-based technologies in the banking sector of UAE. Hence, it was necessary to present a concise synopsis of UAE’s banking sector.

Over the years, UAE has become a strong proponent and fore-bearer in introducing the latest technologies to its various sectors. Talking about the banking sector, nearly all the banks based in UAE have now implemented AI-enabled systems (chatbots and virtual assistants) (Mehta & Bhavani 2017). Al-Marri, Ahmed and Zairi (2007) signified the fact that when it comes to the perception of customer service quality, UAE’s banking sector is large enough to cover this area with vast outreach and better outcome. Digitalisation of the banking sector backed by AI-enabled systems has given the UAE’s banking sector a growth of 13.9% in terms of profitability (KPMG 2020).

While banks in UAE are making extensive efforts to retain and satisfy their customers, it is imperative to primarily satisfy their needs employing the cutting-edge technological innovations that are in place (Kumar, Sujit & Charles 2018). Alhosani et al. (2019) discussed the benefits and drawbacks of implementing the latest technologies. One of the major drawbacks faced by UAE’s banking sector in implementing the latest innovations is that they have been the target of cybercrime attacks, resulting in data compromise of approximately 14 million records in the year 2018, whereas, UAE lost roughly $1.1 billion to cybercrime attacks in 2017 (Rosberg 2018; Alhosani et al. 2019). Another major reservation faced by the banks in UAE is the customer satisfaction, which is not earned easily in this part of the world (Sleimi, Musleh & Qubbaj 2020).

The word “satisfaction” is derived from Latin “satis” meaning “enough”, and “facere” meaning “make or to do”. Hence, the combined meaning of these words mean to provide the products and services which have the capacity to be “enough” (Oliver 2010). The meaning of the word satisfaction is also dependent on the context. For instance, if “satisfaction” is used in a marketing context, its meaning becomes more specific (Parker & Mathews 2001).

Söderlund (1998) defined customer satisfaction as, “the value one receives after purchasing or using a product or service”. It is worth mentioning here that the context in which customer satisfaction is mentioned is very important, since the meaning changes from one context to another.

When someone mentions customer satisfaction, they cannot leave repurchase intention out of it, as they both go hand in hand. It will not be wrong to say that it is the customer satisfaction that leads to repurchase intention (Hellier et al. 2003). Repurchase intention is defined as, “the willingness to purchase something again” Dictionary (2021a).

Suhaily and Soelasih (2017) mentioned that customer satisfaction is a compulsory factor when it comes to the repurchase intention towards a product or service, based on the overall quality provided to the customer. Furthermore, Mittal and Kamakura (2001) elaborated that once the customer reaches threshold or the tolerance level towards the repurchase, it may vary from person to person to display the repurchase intention, as not everyone have similar characteristics when it comes to repurchasing.

Word of mouth (WOM) is another important factor that plays a vital role when it comes to customer satisfaction and repurchase intention. According to the Cambridge Dictionary (2021), WOM is defined as “such a process in which the information is being passed on from one person to another through the medium of verbal communication”. WOM has developed into an important means of communication for the field of marketing, which is defined as “the conveyance of message from person to person using oral communication” (Matute, Polo-Redondo & Utrillas 2016).

Communication of information between customers about the products/services, or the company is considered crucial when it comes to the repurchase intention, as customer needs to be completely satisfied before making an informed decision to purchase a certain product/service (Litvin, Goldsmith & Pan 2006). WOM has also transformed over time into electronic word of mouth (eWOM) and algorithmic word of mouth (aWOM), where eWOM is electronic conveyance of message or information, and aWOM is the AI enabled word of mouth using all the sophisticated technology to communicate among people (Williams, Ferdinand & Bustard 2019).

Despite the ease and convenience provided by chatbots to the organisations, more attention needs to be paid to customer satisfaction, since chatbots are AI-enabled systems, programmed by humans, lacking flexibility (Prentice & Nguyen 2020). Various researchers have come up with different technology acceptance theories, where the most relevant ones are Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology Model (UTAUT), Unified Theory of Acceptance and Use of Technology Model 2(UTAUT 2), and Technology Acceptance model 2 (TAM 2).

Technology Acceptance Model 2 or TAM 2 is an extension of the Technology Acceptance model, developed by Venkatesh and Davis (2000). TAM 2 integrated additional external constructs to the original TAM model (Venkatesh & Davis 2000). The objective of this theory was not only to contemplate on user acceptance of technology, but also to understand the user adoption behaviour through social influence and cognitive instrumental processes (Venkatesh & Davis 2000).

One such study published by Ali et al. (2021) argued that the extension of TAM can also be adopted when it comes to the use of cardless banking system. They further suggested that perceived risk plays a big role when it comes to consumers’ intention of use, based on the extended TAM model (Ali et al. 2021). Hence, this theory was the perfect fit to be used and implemented for the purpose of this research.

For the data collection of this research, a face-to-face, semi-structured expert interview with open-ended questions was implemented. Non-probability, purposeful stratified sampling technique was used to collect the required number of participants for this research (until data saturation was achieved). The participants were contacted through emails, WhatsApp, and over the phone. Due to Covid-19 pandemic, online platforms; Zoom and Google meets were used to interview the participants, in lieu of interviewing them in-person. Moreover, interview guide was established to stay on track with the purpose of this research, along with the questions for this interview, which were guided by the theory and literature (Kvale 2007; Given & Saumure 2008; Creswell 2009a, b). For collecting audio records, two separate high-quality instruments were used; iPhone 12 Max Pro, and HP Pavillion laptop. The researchers used iPhone’s “Notes” and Google Doc’s “Dictation” function to transcribe the data.

NVivo12 software was used to describe and classify data to interpret into codes and themes. With the help of NVivo12 software, a large set of data gathered from interviews was analysed in a systematic way. Initial coding was carried out manually, and then all the coded data and themes were transferred to NVivo12 for further organisation and analysis. It also assisted in describing the essence of phenomenology for this research. Analysing the data using NVivo12 further assisted in enhancing the consistency, and validity of this research. According to Creswell and Poth (2018), it is duty of the researcher to write a detailed description of the themes, that are developed from the study. Once all the data was transcribed into Word document, it was then run through NVivo12 for further analysis into themes, and sub-themes. For this research, the data was coded using open coding, axial coding, and selective coding.

Perceived chatbot service quality was labelled as the first aggregate dimension. This dimension revealed how the customers perceived chatbot service quality of their bank. It comprised of the 1st major theme “chatbot efficiency” and its sub-theme “service information and understanding”, 2nd major theme “chatbot interaction” and its sub-theme “limited options and emotional aspects”, respectively. Almost all the participants agreed that chatbots are not very efficient when it comes to assisting or solving their problems, as depicted in the following excerpt from the interview of participant no. 12:

“I don’t know how it would be better, or how people would get used to it, but it needs a lot of time to be more efficient. It is not that efficient nowadays unfortunately, with most of the places that I experienced.” (P12)

Another participant number 10 mentioned that chatbots require lengthy security information before it can verify the customer and proceed further with their request:

“…Some entities tend to make it as a basic and an easy system for the customers, while some provide a very complicated system. Like in the banking sector, they tend to put some restrictions on securities, and this makes it difficult for the client to get what they want as easily as possible. I think that is the downside of using a chatbot in bank. It further needs to consider the type of service. Some services are easy to use with the chatbot, while some of them like banks is a bit difficult because of the security issues.” (P10)

The second aggregate dimension was labelled as Customer satisfaction. This dimension revealed how satisfied the customers felt while interacting with the chatbot of their bank. It comprised of 3rd major theme “experience” and its sub-theme “urgent actions and problem solving”, 4th major theme “participant’s feedback”, and the 5th major theme “human interaction” and its sub-theme “human representative and assistance”, respectively. A few examples are portrayed below, from the excerpts of participant’s interviews:

“…if you are in a hurry and you have some other work to do, it is extremely necessary for you to finish with the bank, but it takes 5 to 10 minutes, then that is very annoying. For me, I have been at both the ends, I mean I have experienced both; being satisfied with its service and annoyed with it at times.” (P22)

“…it was very interesting because this is something all of us have experienced at some point, happily and unhappily. So, I also had mixed kind of reaction while interacting with a chatbot. However, I distinctly remember my first interaction. I was intrigued that how this was going to work. I wouldn’t say it was too bad, but it was not the best experience that I had. I still remember my ATM card had gotten stuck in the ATM machine; it was not coming out. I was trying to reach the helpline, but I couldn’t reach any helpline. So, that was the first time I interacted with a chatbot. Now the thing with the chatbot is that it gives you almost the perfect information that one could possibly be looking for, and it gives you all the details that you might find necessary for your need of the hour, but also it is limited in a way that it is a programmed machine.” (P3)

Responses and Intentions was labelled as the third aggregate dimension. This dimension revealed about the participants responses and intentions once they interacted with the chatbot of their bank. It comprised of 6th major theme “suggestions from experience” and 7th major theme “Word of Mouth (WOM)”, respectively. The following examples illustrate participant’s responses related to the aggregate dimension three:

“…the other one which I think is about the age factor especially the old people, I don’t think that they are very technical people. Maybe out of 100, 2 or maximum 3 people will know or are familiar with chatbot. Other than that, I am sure they are avoiding it and prefer to interact directly with a human representative or visit the branch itself.” (P1)

“I am a technology savvy person, but everyone is different than the other human, and let us not forget who hardly understand technology like aged people, labour class and uneducated people, for example. So, banks need to consider their situation as well to try and create a way for their easy access as well, because honestly, not everyone is willing to spend money on costly calls and end up with no result at the end.” (P20)