An Overview of Conversational Agent: Applications, Challenges and

Future Directions

Ahlam Alnefaie

, Sonika Singh

, Baki Kocaballi

and Mukesh Prasad

School of Computer Science, FEIT, University of Technology Sydney, Australia

Marketing Discipline Group, Business School, University of Technology Sydney, Australia

Keywords: Chatbot, Conversational Agent, Artificial Intelligence, Real-world Application.

Abstract: Recent years have seen the increased use of artificial intelligence technologies such as conversational agents.

Conversational agents, also referred to as chatbots, are used to interact with users using natural language.

Thus, various fields have started to adopt conversational agents such as education, healthcare, marketing,

customer service, and entertainment. However, determine the motivations that drive the use of conversational

agents and clarify their usefulness are challenging. This paper presents an overview of the evolution of

conversational agents from an initial model to an advanced intelligent system and their deployment in various

real-world applications. Moreover, this paper contributes to information system literature by comparing the

different types of conversational agents based on their roles and interaction styles. This paper also highlights

the current challenges of conversational applications along with recommendations for future research.

1 INTRODUCTION

In recent years, a significant number of artificial

intelligence (AI) applications have been developed

for organizations to automatically manage users'

inquiries. A conversational agent or a chatbot is a

smart program that simulates human language

through adopting machine learning and AI

techniques. The modes of communication with

conversational AI agents involve text, voice, emoji,

and other types of inputs as interaction techniques

with users. In the multichannel environment,

conversational agents can reduce the time users spent

seeking the right information. Consequently, the

pervasive use of conversational AI technology has

created a critical dependency on researchers that call

for a specific focus on conversational AI application

evaluation (Venkatesh et al. 2018).

Conversational systems can enhance the

experience of digital users in various domains such as

education, e-commerce, healthcare, finance,

marketing, and business. Each conversational agent

has access to specific knowledge of its domain to

converse effectively. For instance, education

conversational agents help tutor content and

university-related information (Hiremath et al. 2018).

The conversational applications in healthcare assist

patients with answers to specific health-related

queries (Kadariya et al. 2019). Business conversational

agents act as a customer service tool to improve

customer experience (Nuruzzaman and Hussain,

2018). Other conversational applications in a general

domain are developed to conduct conversations on

open topics and support users' needs.

Despite the rapid adoption of conversational AI in

the industry, conversational AI and its applications

have received limited attention in the academic

literature. This paper synthesizes conversational AI

applications and their evolution in various industries.

Therefore, a literature review has been conducted to

comprehensively compare conversational agents in

different domains. This study also identifies the

current challenges of conversational applications and

provides future research directions towards the

effectiveness of conversational AI usage.

The paper is organized as follows. The first

section briefly describes the technological concepts

of conversational agent applications; the second

section discusses the history of conversational agents

and the growing interest of the research studies; the

third section presents a classification of

conversational agents based on their roles and

interaction styles; the fourth section highlights the

conversational applications in various domains. The

last section discusses the current challenges of

conversational applications and highlights directions

that need further research.

388

Alnefaie, A., Singh, S., Kocaballi, B. and Prasad, M.

An Overview of Conversational Agent: Applications, Challenges and Future Directions.

DOI: 10.5220/0010708600003058

In Proceedings of the 17th International Conference on Web Information Systems and Technologies (WEBIST 2021), pages 388-396

ISBN: 978-989-758-536-4; ISSN: 2184-3252

2 RESEARCH BACKGROUND

2.1 Understanding Conversational

Technology

Conversational agents or chatbots are considered as a

type of dialogue system in the field of human-

computer interaction (Følstad and Brandtzæg, 2017).

As the name suggests, chatbots refer to an Internet

robot that can chat with humans. Chatbots are not

usually stand-alone applications that can be installed,

but rather, they work in an integrated way within

websites or messenger platforms (Parthornratt et al.

2018). Chatbots often use AI algorithms to analyse

the users' input before sending an appropriate

response (Rahman et al. 2017). In addition, speech

recognition technology is critical here to understand

the users' responses (Haridas et al. 2018).

A conversational user interface (CUI) bridges the

interaction of humans with dialogue systems to mimic

social chatting. CUI term referred to the collective

term for a variety of assistants that mimic human

conversation (Lister et al. 2020). CUI has usually two

styles of interaction: voice-based and typing-based.

Voice User Interfaces (VUI) is what users interact

with when communicating with spoken language

applications (Cohen et al. 2004). For example, Apple

Siri, Amazon Alexa, Windows Cortana, and Google

Assistant (aka voice assistants) are typical examples of

CUIs using voice-based interaction. Typing-based

chatbots rely on textual input and output and are

usually integrated with social media sites such as

Facebook Messenger bots. Furthermore, the chatbot

applications enhance the CUI capability using natural

language processing (NLP) and natural language

understanding (NLU) techniques to smartly understand

the intentions of each user input (Braun et al. 2017).

User Experience (UX) is a major term in the discipline

of human-computer interaction (HCI). UX concept has

a variety of different definitions in the relevant

literature. The ISO 9241-210 definition of user

experience is "A person's perceptions and responses

resulting from the use and/or anticipated use of a

product, system or service" (Mirnig et al. 2015). A

critical factor that influences user experience with new

technology is a user onboarding design. The user

onboarding design is the introduction process that

gives users a guided message to the new product to

enhance the experience of using the product (Cascaes

Cardoso 2017). A common type of onboarding method

used in conversational interface is using greeting

statements or menu-based questions. Further studies

need to investigate the type of onboarding design that

influences first-time interaction with users.

2.2 History

The interest in conversational technology has

increased considerably since the 1950s when the

initial studies focused on the interaction between

humans and computers in accordance with the

communication theory. Table 1 shows a summary of

the conversational applications development

timeline. In 1950, Alan Mathison published an article

titled "Computing Machinery and Intelligence" to test

computers' ability to think like humans. Alan

Mathison's famous research establishes the

foundations of AI conversational agents (Pinsky

1951). ELIZA was the first chatbot developed in1966

by Joseph Weizenbaum, which the main purpose is to

act as a Rogerian psychotherapist based on the model

of mirroring the previous user prompt. However, it

was unable to keep the flow of conversation ongoing

and had limited capability of recognizing human-like

feelings (Weizenbaum 1966). PARRY was another

historic chatbot, developed in 1972 by Kenneth

Colby, at Stanford University, which simulates a

person with paranoid schizophrenia. PARRY with its

embodied conversational interface has more features

than ELIZA (AbuShawar and Atwell, 2015).

Artificial linguistic Internet computer entity

(ALICE) was developed in 1995 by Richard Wallace,

which utilizes XML platform called AI mark-up

language to determine the heuristic conversation rules

(Tabet et al. 2000). In 2001, SmarterChild chatbot was

developed, and integrated within windows messenger

to communicate with users as a personalized

conversational interface. Apple launched its voice

assistant, Siri, in 2010, which communicates with users

through a natural language interface employing both

voice and textual interaction modalities (Aron 2011;

Guzman 2017). In 2015, Amazon introduced a voice-

based chatbot designed as a smart speaker called

Alexa. Then, inspired by Alexa, Google released a

smart speaker in 2016: Google Assistant. In 2017,

Samsung, started developing Bixby, to have an

intelligent voice-based assistant similar to Siri.

Recently, Google has launched the most state-of-

the-art AI chatbot called Meena, a 2.6 billion

parameter end-to-end trained neural conversational

model. Meena can hold sensible conversations that

are more specific than existing best-performing

chatbots (Adiwardana et al. 2020). Facebook has also

announced a new AI chatbot, Blender that has more

human features than Meena. However, Blender is

limited in filtering Reddit datasets that may have

harmful languages that influence the responses

(Gjurković and Šnajder, 2018). The following section

proposes a classification of conversational agents

An Overview of Conversational Agent: Applications, Challenges and Future Directions

389

Table 1: Timeline of conversational agents’ development.

Agent Year Creator Interaction mode Role

ELIZA 1966 Joseph Weizenbaum Text Rogerian psychotherapist

PARRY 1972 Kenneth Colby Text Simulate a person with paranoid schizophrenia

ALICE 1995 Richard Wallace Text Practice human-like conversation

SmarterChild 2001 Robert Hoffer Text

Personal assistant

Siri 2011 Apple Text/Voice

Alexa 2015 Amazon Voice

Bixby 2017 Samsung Text/Voice

Meena 2020 Google Text

Blender 2020 Facebook Text

according to two categories based on their role and

interaction styles. While the first one depends on

showing the different roles conversational agents can

take, the second classification focuses on technical

aspects characterizing user interaction styles.

3 METHODOLOGY

This review aims to present an overview of the

evolution of conversational agents and their

deployment in various real-world applications. To

review existing studies on conversational agents in

various domains, we follow the literature review

process based on the standard approaches (Nakano

and Muniz, 2018; Webster and Watson, 2002). All

searches were limited by the English language and

publication date (2010– 2021). The review included

two phases. The first stage was collecting studies

regarding conversational agents from relevant

journals and conferences. The second phase was

analysing the literature based on the specific domains

to present the potential role of conversational agents

and derive directions for future studies. Furthermore,

titles and abstracts were scanned to remove irrelevant

articles. In addition, we conducted a forward

snowballing search method by examining the

citations to the included articles.

4 RESULTS

4.1 Types of Conversational Agents

Conversational applications may consist of a dialog

system, an avatar, and an expert framework to process

queries efficiently. Broadly, the conversational

agents can be classified based on the role/tasks

accomplished: general-purpose and task-specific.

The second classification is based on interaction

styles with users that involve two types of interaction

styles: menu-based and text/voice-based. Figure 1

presents the broad classification of conversational

agents. These criteria may influence the core design

philosophy of conversational agents or which

principle needs to be considered in understanding the

communication or the tasks of the conversation for

which the conversational agent needs to be designed

(Braun and Matthes, 2019). The general-purpose

conversational agent is a multi-tasking agent and

plays the role of a personal virtual assistant. It is

usually integrated as a virtual assistant on platforms

such as mobile, desktop, and smart speaker (Siebra

et al. 2018). These applications perform general tasks,

and users can ask general questions. For example,

users ask information about the weather, the nearest

restaurants, opening email applications, adjusting the

calendar, and any other personal inquiries. Siri, Bixby,

Cortana, Alexa, and Google Home are examples of

virtual assistants that play a general-purpose role

(López et al. 2017). The task-specific conversational

agent performs specific tasks for users, working as an

assistant agent for a particular domain such as online

tutor, therapist, and customer service (Kalia et al. 2017;

Ko and Lin, 2018; Ranoliya et al. 2017).

The integrated platforms for this type of agent

include websites and social media applications. For

example, the Facebook messenger is a popular

chatbot used by firms to target consumers (Pereira

and Díaz, 2018). Social media platforms such as

Twitter, Kik messenger, WhatsApp, and WeChat

application also embed the chatbot platform (Xie et

al. 2019; Yamaguchi et al. 2018). The menu/screen-

based conversational agent uses pre-defined rules to

produce limited numbers of answers. The users, in

turn, can only ask pre-defined questions, and the

subsequent responses are generated from the chatbots'

knowledge base. The user interface of a menu-based

conversational agent has a limited number of end-

WEBIST 2021 - 17th International Conference on Web Information Systems and Technologies

390

users' prompts. The implementation of this type of

conversational agent is straightforward and does not

require any machine learning algorithm (Dahiya 2017).

A limitation of this conversational application is unable

to answer the different kinds of questions not included

in the pre-defined list of options in the dataset. The

benefits of a menu-based agent are enhancing the ease

of use factor and navigating the conventional flow of

information (Hornbæk and Hertzum, 2017). However,

the limited number of response options constrain the

users' expressive capacity. This type of conversational

assistance is popularly used in the retailing industry.

For example, Domino's Facebook Messenger bot

offers buttons as communication modes with

consumers (Sotolongo and Copulsky, 2018).

Figure 1: Classification of conversational agents.

The text/voice-based type of conversational

agent is more advanced and utilizes machine learning

algorithms to generate an appropriate response. This

type of agent interacts with end-users allowing them

to unconstrained input through typing text or voice by

speech and long sentences during the conversation.

For example, If the user asks, “Where is the nearest

store to my location?” the agent uses the keywords

“nearest”, “store”, and “location”, to determine the

best answer to reply to the end-user. The text and

voice-based agents apply deep learning algorithms to

develop the ability to detect and recognise keywords

(Khanpour et al. 2016). A dialogue manager is the

main part of the design, which collects keywords

from the conversational interface and sends it to the

knowledge engine. Then, the knowledge engine

classifies the type of questions and searches for the

answers from the knowledge database (Setiaji and

Wibowo, 2016). Conversation datasets are available

through many open source platforms (Serban et al.

2015). Compared to the menu-based ones, text/voice-

based conversational agents provide higher levels of

flexibility in the ways in which users can express their

prompts. However, there is also a higher possibility

of misrecognized prompts that may negatively affect

user experience.

The next section discusses the application of

conversational

in various domains such as education,

finance, banking, travel, healthcare, and E-commerce.

4.2 Applications in Various Domains

Conversational systems play an important role in

many sectors such as education, healthcare, finance,

travel, and business. Table 2 presents the role of

chatbots in various domains. Figure 2 shows that

chatbots' potential usage in the industry (Suhel et al.

2020). In education, a conversational application is

useful for learning with the right conversation

scenario design, leading to less complex knowledge

structures. Prior research has focused on the role of a

chatbot that can serve for teaching and learning

purposes. Sánchez-Díaz et al. (2018) developed a

formal methodology for implementing an intelligent

chatbot as a tutor for a university-level course.

Clarizia et al. (2018) proposed an ontology-based

chatbot in the educational domain, which uses NLP

techniques to develop keyword detection skills to

provide the correct answers to students. Therefore,

chatbots can work as assistants for teachers and

students during the learning activities such as

identifying grammatical and spelling mistakes,

assigning projects, and checking homework.

Figure 2: Chatbots usage in the industry (Suhel et al. 2020).

Kerlyl et al. (2006) focused on a negotiated open

learner model to develop conversational agents and

intelligent tutoring systems to support student

reflection on their learning. A chatbot's ability to

negotiate and incorporate small talk capabilities can

positively influence the enjoyment of the interaction

and engagement with a chatbot, thereby improving

the learning experience for students. Hien et al.

(2018) developed a chatbot that can provide services

for students and academic staff with high accuracy of

user intent identification and context extraction. Both

user intent identification and context information

have quite promising results i.e., a high score of the

context information extraction indicates the ability of

An Overview of Conversational Agent: Applications, Challenges and Future Directions

391

a chatbot to provide the correct answers, as shown in

Figure 3.

In the finance industry, chatbots are deployed as

customer service agents in large-scale inquiries for

financial industry clients to provide the information

and features of services like car loans, home loans,

and FAQ for customers who already have a car loan

contract. Okuda and Shoda (2018) examined the

features of ‘Sony bank' chatbot and developed the

user stream function to visualize how many users

have passed through different contexts. Visualization

of the user stream function can provide insights

regarding which script locations require more

detailed responses to develop the conversation

suitability of the chatbot. Altinok (2018) proposed a

framework for the finance-banking domain to build

German language banking through the finance

chatbot to keep the state of the conversation between

the chatbot and customers. The achievement of

Altinok (2018) is promising, and the project is still in

progress to introduce the success metrics and evaluate

the dialogue manager module. Duijst (2017) proposed

a chatbot for banks, which investigates the factor of

personalization for improving the user experience of

chatbots in the finance section and found that

personalization has no significant effect on the user

experience of chatbots for the finance industry, as

shown in Figure 4 (Duijst 2017).

Figure 3: The F-score results (Hien et al.2018).

Figure 4: Plot of the effect of Task & Personalisation with

the UX of chatbots (Duijst 2017).

Table 2: Chatbots’ role in various domains.

Stud

Chatbots’ Role Domain

Kerlyl et al. 2006;

Sánchez-Díaz et al. 2018;

Clarizia et al. 2018; Hien

et al. 2018; Prasad and

Ran

ith, 2020

 An intelligent tutor for a university level course.

 Providing education system information and services on behalf of the

academic staff.

 Improving the security and automation of a lab by the voice-based agent.

Education

Okuda and Shoda 2018;

Altinok 2018, Duijst 2017;

Milhorat et al. 2019; Suhel

et al. 2020; Khan and

Rabbani, 2021

 Providing financial-product sales.

 Online customer support in banking industry.

 Answering a question about customers account, bill payment, credit card

payments, and schedule meetings

Bank/Finance

Kowatsch et al. 2017; Oh

et al. 2017; Huang et al.

2018; Martin et al. 2020;

Espinoza et al. (2020)

 Providing guidance for consumers or their carers when they have

medical problems.

 Providing diabetics with diets and information regarding foods to be

avoided.

 Provi

information for

reventin

COVID-19

andemic

Healthcare

Argal et al. 2018; Sano et

al. (2018), Alotaibi et al.

2020

 Providing information or services through conversation-like interactions

for tourism and travel.

 Consumers use travel chatbots to book a trip, plan a vacation, discover

new experiences, and make reservations at hotels.

Travel/Tourism

Gupta et al. 2015;

Brynjolfsson and Mcafee,

2017; Chung et al. 2018;

Zarouali et al. 2018;

Rakhra et al. 2021;

 Offering features to brands, such as sending advertisement messages,

asking for customer feedback, and collecting customers' preferences.

 Providing an online experience and customer service through social

media sites.

E-commerce

WEBIST 2021 - 17th International Conference on Web Information Systems and Technologies

392

In the banking sector, a conversational agent can

play several roles to help end-users, such as

answering a question about users account, bill

payment, make a transaction, credit card payments,

and schedule meetings. Milhorat et al. (2019)

investigated the power of chatbots in bank services by

developing a dialogue management system to provide

a suitable answer and avoid making generic fallback

utterances. They experimented with 226 user

interactions and provided 187 correct answers and 39

fallback utterances. Their result showed that user

utterances could be handled if the system had the

addition of a coherent statement response component.

Conversational agents are widely used in the

healthcare industry with the primary aim to provide

guidance for consumers or their carers when they

have medical problems (Kowatsch et al. 2017; Oh et

al. 2017). Huang et al. (2018) utilized user data to

develop the AI healthcare chatbot that can provide

people with diabetes with diets and information

regarding foods to be avoided. AI medical

applications can offer useful personalised

information to individuals in efficient ways

(Kocaballi et al. 2019a). However, further studies are

needed to assess patient safety (Laranjo et al. 2018).

Recent research investigates the power of AI

technology to provide information for preventing

COVID-19 pandemic (Miner et al. 2020). Developing

AI-based chatbot applications can have a role in

defeating COVID-19 and increase the efficiency of

healthcare management (Martin et al. 2020).

Espinoza et al. (2020) proposed a web-based chatbot

for COVID-19 screening and redirecting users via

links distributed through multiple channels (social

media, email, text messages) to handle the screening

questions to assign each question into a risk category

with a specific set of actions and triage patients to the

right health care option.

Chatbots can also provide information or services

through conversation-like interactions for tourism and

travel. Consumers use travel chatbots to book a trip,

plan a vacation, discover new experiences, and make

reservations at hotels with higher ratings. Argal et al.

(2018) developed a chatbot to improve user-machine

interactions in the travel domain through collective

user preferences to provide better user-centric

recommendations and accurate travel information to

the user. Sano et al. (2018) implemented a tourism

chatbot that is based on hierarchical cluster analysis

and agglomerative nesting algorithm to give users a

balance between time allocations versus the quality of

their tour for tourism sites.

The main business objectives of the firms are to

achieve sales, enhance customer service and

engagements (Solem 2016), and these can be achieved

using AI technologies (Brynjolfsson and Mcafee

2017). The researchers have investigated the

effectiveness of the Facebook chatbot applications for

brand engagement, and they found that the consumer

engagement of brands on Facebook results in positive

user-generated content and consumer involvement

(Leong et al. 2018; Shareef et al. 2018). Lee and Ko

(2019) reported that chatbots with customizing

functionality, sociality, creativity, and hedonic value

all influence perceived brand relationships and brand

loyalty. Bhawiyuga et al. (2017) designed a chatbot

system that can communicate with customers through

the Telegram service and provide automatic answers to

the customer-to-seller questions in less than 5 seconds.

Conversational agents can offer many features to

brands, such as sending advertisement messages,

asking for customer feedback, and collecting

customers' preferences that often drive consumers'

brand engagement. Luxury brands such as Burberry

and Tommy Hilfiger started to adopt chatbots to

communicate with their customers and provide an

online experience through social media sites. At least

two reviews of the literature found that chatbots can

be effective for customer satisfaction and marketing

strategies of brands (Chung et al. 2018; Zarouali et al.

2018). However, more studies are needed to

investigate the impact of using conversational agents

as a marketing channel with careful consideration of

customers' perceptions. Conversational applications

can enhance online shopping by providing

recommendations and information for online

customers, leading to improving browsing the

products that can be challenging and time-consuming

through websites given the variety of features a

product can have. For example, Gupta et al. (2015)

developed a website-based chatbot as an online

automated assistant that is able to help customers to

make a decision about which product is suitable for

them and provide product suggestions.

5 CHALLENGES

Today most brands rely on social media sites to

develop the customer-brand relationship and provide

information to the customer. However, social media

sites with a large amount of content and poorly

structured posts can make it difficult for customers to

find products and information easily and quickly. In

this scenario, a conversational agent to make it easier

for a customer to contact with a brand and find

information. Making conversations relevant and

designing and evaluating satisfactory user

An Overview of Conversational Agent: Applications, Challenges and Future Directions

393

experiences are still challenges for conversational

applications design (Kocaballi et al. 2019b).

However, conversational agents are a promising

alternative as compared to using other marketing

channels and customer service technologies. Existing

chatbot applications in the customer service industry

have many drawbacks such as poor interactive user

interface, not multilingual, do not support third-party

integration, and cannot detect customers' emotions

(Nuruzzaman and Hussain, 2018).

6 CONCLUSION AND FUTURE

DIRECTIONS

Conversational applications have received increasing

attention in numerous fields like virtual assistance,

education, finance, healthcare, and e-commerce due

to their advantage of supporting the use of natural

language interfaces. This paper describes the

technological concepts of conversational agent

applications, and the history of conversational agent

development, and implementation of conversational

applications in various domains. This paper compares

the different types of conversational agents based on

their roles and interaction modes. This paper

highlights the areas for future research directions

towards conversational agents for brands, especially

for marketing and customer service toolkits, as the

conversational applications may prove effective in

improving customer-brand engagement and lead to

the success of marketing strategies.

A few recent studies have focused on the

effectiveness of conversational agents for online

marketing and brand strategies (Chung et al. 2018;

Zarouali et al. 2018). A promising area of research is

to investigate the potential of online conversational

agents as an online marketing tool and how

conversational agents can enhance customer

engagement. Future research can focus on developing

a methodology to investigate the consumers’ attitudes

of using conversational applications and the factors

that influence user satisfaction of using

conversational agents. The UX quality assessment is

a current research topic regarding the HCI discipline

(Kocaballi et al. 2019b). Further improvement in

conversational application design can be achieved by

evaluating the UX of conversational agents for a

specific domain and exploring the characteristics of

conversational agents that need to be improved based

on the users' perspectives to enhance the conversation

outcomes and capabilities.

REFERENCES

AbuShawar, B., & Atwell, E. (2015). "ALICE chatbot:

Trials and outputs", Computación y Sistemas, 19(4),

625-632.

Adiwardana, D., Luong, M.-T., So, D. R., Hall, J., Fiedel,

N., Thoppilan, R., Yang, Z., Kulshreshtha, A., Nemade,

G., and Lu, Y. 2020. "Towards a Human-Like Open-

Domain Chatbot," p. 2001.09977.

Altinok, D. 2018. "An Ontology-Based Dialogue

Management System for Banking and Finance

Dialogue Systems."

Alotaibi, R., Ali, A., Alharthi, H., Almehamdi, R. 2020. AI

Chatbot for Tourist Recommendations: A Case Study

in the City of Jeddah, Saudi Arabia.

Argal, A., Gupta, S., Modi, A., Pandey, P., Shim, S., and

Choo, C. 2018. "Intelligent Travel Chatbot for

Predictive Recommendation in Echo Platform," 2018

IEEE 8th annual computing and communication

workshop and conference (CCWC): IEEE, pp. 176-183.

Aron, J. 2011. "How Innovative Is Apple's New Voice

Assistant, Siri?" 212:2836, pp. 24-24.

Bhawiyuga, A., Fauzi, M. A., Pramukantoro, E. S., and

Yahya, W. 2017. "Design of E-Commerce Chat Robot

for Automatically Answering Customer Question,"

2017 International Conference on Sustainable

Information Engineering and Technology (SIET):

IEEE, pp. 159-162.

Braun, D., and Matthes, F. 2019. "Towards a Framework

for Classifying Chatbots," ICEIS (1), pp. 496-501.

Braun, D., Mendez, A. H., Matthes, F., and Langen, M.

2017. "Evaluating Natural Language Understanding

Services for Conversational Question Answering

Systems," Proceedings of the 18th Annual SIGdial

Meeting on Discourse and Dialogue, pp. 174-185.

Brynjolfsson, E., and Mcafee, A. 2017. "The Business of

Artificial Intelligence,” 7, pp. 3-11.

Cascaes Cardoso, M. 2017. "The Onboarding Effect:

Leveraging User Engagement and Retention in

Crowdsourcing Platforms," Proceedings of the 2017

CHI Conference Extended Abstracts on Human Factors

in Computing Systems, pp. 263-267.

Chung, M., Ko, E., Joung, H., and Kim, S. J. 2018. "Chatbot

E-Service and Customer Satisfaction Regarding Luxury

Brands," pp. 1-9.

Clarizia, F., Colace, F., Lombardi, M., Pascale, F., and

Santaniello, D. 2018. "Chatbot: An Education Support

System for Student," International Symposium on

Cyberspace Safety and Security: Springer, pp. 291-302.

Cohen, M. H., Cohen, M. H., Giangola, J. P., & Balogh, J.

(2004). Voice user interface design. Addison-Wesley

Professional.

Dahiya, M. 2017. "A Tool of Conversation: Chatbot,” 5:5,

pp. 158-161.

Duijst, D. 2017. "Can We Improve the User Experience of

Chatbots with Personalisation," pp. 1-22.

Espinoza, J., Crown, K., and Kulkarni, O. 2020. "A Guide

to Chatbots for Covid-19 Screening at Pediatric Health

Care Facilities,” 6:2, p. e18808.

WEBIST 2021 - 17th International Conference on Web Information Systems and Technologies

394

Følstad, A., and Brandtzæg, P. B. 2017. "Chatbots and the

New World of Hci,” 24:4, pp. 38-42.

Gjurković, M., and Šnajder, J. (2018). Reddit: "A gold mine

for personality prediction". In Proceedings of the

Second Workshop on Computational Modeling of

People’s Opinions, Personality, and Emotions in Social

Media. pp. 87-97.

Gupta, S., Borkar, D., De Mello, C., and Patil, S. 2015. "An

E-Commerce Website Based Chatbot,” 6:2, pp. 1483-

1485.

Guzman, A. L. 2017. "Making Ai Safe for Humans: A

Conversation with Siri," pp. 85-101.

Haridas, A. V., Marimuthu, R., and Sivakumar, V. G. 2018.

"A Critical Review and Analysis on Techniques of

Speech Recognition: The Road Ahead,” 22:1, pp. 39-

57.

Hien, H. T., Cuong, P.-N., Nam, L. N. H., Nhung, H. L. T.

K., and Thang, L. D. 2018. "Intelligent Assistants in

Higher-Education Environments: The Fit-Ebot, a

Chatbot for Administrative and Learning Support,"

Proceedings of the ninth international symposium on

information and communication technology, pp. 69-76.

Hiremath, G., Hajare, A., Bhosale, P., Nanaware, R., and

Wagh, K. 2018. "Chatbot for Education System,” 4:3,

pp. 37-43.

Hornbæk, K., and Hertzum, M. 2017. "Technology

Acceptance and User Experience: A Review of the

Experiential Component in Hci,” 24:5, pp. 1-30.

Huang, C. Y., Yang, M. C., Huang, C. Y., Chen, Y. J., Wu,

M. L., & Chen, K. W. 2018. A Chatbot-supported smart

wireless interactive healthcare system for weight

control and health promotion. In 2018 IEEE

international conference on industrial engineering and

engineering management (IEEM) (pp. 1791-1795).

IEEE.

Kadariya, D., Venkataramanan, R., Yip, H. Y., Kalra, M.,

Thirunarayanan, K., and Sheth, A. 2019. "Kbot:

Knowledge-Enabled Personalized Chatbot for Asthma

Self-Management,", IEEE International Conference on

Smart Computing (SMARTCOMP): IEEE, pp. 138-

143.

Kalia, A. K., Telang, P. R., Xiao, J., and Vukovic, M. 2017.

"Quark: A Methodology to Transform People-Driven

Processes to Chatbot Services," International

Conference on Service-Oriented Computing: Springer,

pp. 53-61.

Kerlyl, A., Hall, P., and Bull, S. 2006. "Bringing Chatbots

into Education: Towards Natural Language Negotiation

of Open Learner Models," International Conference on

Innovative Techniques and Applications of Artificial

Intelligence: Springer, pp. 179-192.

Khan, S., and Rabbani, M. R. 2021. Artificial Intelligence

and NLP-Based Chatbot for Islamic Banking and

Finance. International Journal of Information Retrieval

Research (IJIRR), 11(3), 65-77.

Khanpour, H., Guntakandla, N., and Nielsen, R. 2016.

"Dialogue Act Classification in Domain-Independent

Conversations Using a Deep Recurrent Neural

Network," Proceedings of COLING 2016, the 26th

International Conference on Computational

Linguistics: Technical Papers, pp. 2012-2021.

Ko, M.-C., and Lin, Z.-H. 2018. "Cardbot: A Chatbot for

Business Card Management," Proceedings of the 23rd

International Conference on Intelligent User Interfaces

Companion, pp. 1-2.

Kocaballi, A. B., Berkovsky, S., Quiroz, J. C., Laranjo, L.,

Tong, H. L., Rezazadegan, D., and Coiera, E. 2019a.

The personalization of conversational agents in health

care: systematic review. Journal of medical Internet

research, 21(11), e15360.

Kocaballi, A. B., Laranjo, L., and Coiera, E. 2019b.

Understanding and measuring user experience in

conversational interfaces. Interacting with

Computers, 31(2), 192-207.

Kowatsch, T., Nißen, M., Shih, C.-H. I., Rüegger, D.,

Volland, D., Filler, A., Künzler, F., Barata, F., Büchter,

D., and Brogle, B. 2017. "Text-Based Healthcare

Chatbots Supporting Patient and Health Professional

Teams: Preliminary Results of a Randomized

Controlled Trial on Childhood Obesity," pp. 1-10.

Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B.,

Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi,

F., and Lau, A. Y. 2018. "Conversational Agents in

Healthcare: A Systematic Review,” 25:9, pp. 1248-

1258.

Lee, M., and Ko, E. 2019. "The Effect of Customized

Chatbot Services on Brand Loyalty toward Luxury

Brands," 2019 Global Fashion Management

Conference at Paris, pp. 828-828.

Leong, L.-Y., Jaafar, N. I., and Ainin, S. 2018. "The Effects

of Facebook Browsing and Usage Intensity on Impulse

Purchase in F-Commerce,” 78, pp. 160-173.

Lister, K., Coughlan, T., Iniesto, F., Freear, N., & Devine,

P. (2020). Accessible Conversational User Interfaces.

López, G., Quesada, L., and Guerrero, L. A. 2017. "Alexa

Vs. Siri Vs. Cortana Vs. Google Assistant: A

Comparison of Speech-Based Natural User Interfaces,"

International Conference on Applied Human Factors

and Ergonomics: Springer, pp. 241-250.

Martin, A., Nateqi, J., Gruarin, S., Munsch, N.,

Abdarahmane, I., Zobel, M., and Knapp, B. 2020. "An

Artificial Intelligence-Based First-Line Defence

against Covid-19: Digitally Screening Citizens for

Risks Via a Chatbot,” 10:1, pp. 1-7.

Milhorat, P., Lala, D., Inoue, K., Zhao, T., Ishida, M.,

Takanashi, K., Nakamura, S., and Kawahara, T. 2019.

"A Conversational Dialogue Manager for the

Humanoid Robot Erica," in Advanced Social

Interaction with Agents. Springer, pp. 119-131.

Miner, A. S., Laranjo, L., and Kocaballi, A. B. 2020.

"Chatbots in the Fight against the Covid-19 Pandemic,"

3:1, pp. 1-4.

Mirnig, A. G., Meschtscherjakov, A., Wurhofer, D.,

Meneweger, T., & Tscheligi, M. 2015. A formal

analysis of the ISO 9241-210 definition of user

experience. In Proceedings of the 33rd annual ACM

conference extended abstracts on human factors in

computing systems (pp. 437-450).

An Overview of Conversational Agent: Applications, Challenges and Future Directions

395

Nakano, D., and Muniz, J. (2018). Writing the literature

review for empirical papers. Production, 28.

Nuruzzaman, M., and Hussain, O. K. 2018. "A Survey on

Chatbot Implementation in Customer Service Industry

through Deep Neural Networks," 2018 IEEE 15th

International Conference on e-Business Engineering

(ICEBE): IEEE, pp. 54-61.

Oh, K.-J., Lee, D., Ko, B., and Choi, H.-J. 2017. "A Chatbot

for Psychiatric Counseling in Mental Healthcare

Service Based on Emotional Dialogue Analysis and

Sentence Generation," 2017 18th IEEE international

conference on mobile data management (MDM): IEEE,

pp. 371-375.

Okuda, T., and Shoda, S. 2018. "Ai-Based Chatbot Service

for Financial Industry,” 54:2, pp. 4-8.

Parthornratt, T., Kitsawat, D., Putthapipat, P., and

Koronjaruwat, P. 2018. "A Smart Home Automation

via Facebook Chatbot and Raspberry Pi," 2018 2nd

International Conference on Engineering Innovation

(ICEI): IEEE, pp. 52-56.

Pereira, J., and Díaz, O. 2018. "A Quality Analysis of

Facebook Messenger's Most Popular Chatbots,"

Proceedings of the 33rd annual ACM symposium on

applied computing, pp. 2144-2150.

Pinsky, L. 1951. "Do Machines Think About Machines

Thinking," pp. 8-20.

Prasad, V. A., and Ranjith, R. 2020. Intelligent Chatbot for

Lab Security and Automation. In 2020 11th

International Conference on Computing,

Communication and Networking Technologies

(ICCCNT) (pp. 1-4). IEEE.

Rahman, A., Al Mamun, A., and Islam, A. 2017.

"Programming Challenges of Chatbot: Current and

Future Prospective," 2017 IEEE Region 10

Humanitarian Technology Conference (R10-HTC):

IEEE, pp. 75-78.

Rakhra, M., Gopinadh, G., Addepalli, N. S., Singh, G.,

Aliraja, S., Reddy, V. S. G., & Reddy, M. N. 2021. E-

Commerce Assistance with a Smart Chatbot using

Artificial Intelligence. In 2021 2nd International

Conference on Intelligent Engineering and

Management (ICIEM) (pp. 144-148). IEEE.

Ranoliya, B. R., Raghuwanshi, N., and Singh, S. 2017.

"Chatbot for University Related Faqs," 2017

International Conference on Advances in Computing,

Communications and Informatics (ICACCI): IEEE, pp.

1525-1530.

Sánchez-Díaz, X., Ayala-Bastidas, G., Fonseca-Ortiz, P.,

and Garrido, L. 2018. "A Knowledge-Based

Methodology for Building a Conversational Chatbot as

an Intelligent Tutor," Mexican International

Conference on Artificial Intelligence: Springer, pp.

165-175.

Sano, A. V. D., Imanuel, T. D., Calista, M. I., Nindito, H.,

and Condrobimo, A. R. 2018. "The Application of

Agnes Algorithm to Optimize Knowledge Base for

Tourism Chatbot," 2018 International Conference on

Information Management and Technology

(ICIMTech): IEEE, pp. 65-68.

Serban, I. V., Lowe, R., Henderson, P., Charlin, L., and

Pineau, J. 2015. "A Survey of Available Corpora for

Building Data-Driven Dialogue Systems."

Setiaji, B., and Wibowo, F. W. 2016. "Chatbot Using a

Knowledge in Database: Human-to-Machine

Conversation Modeling," 2016 7th International

Conference on Intelligent Systems, Modelling and

Simulation (ISMS): IEEE, pp. 72-77.

Shareef, M. A., Mukerji, B., Alryalat, M. A. A., Wright, A.,

and Dwivedi, Y. K. 2018. "Advertisements on

Facebook: Identifying the Persuasive Elements in the

Development of Positive Attitudes in Consumers," 43,

pp. 258-268.

Siebra, C., Correia, W., Penha, M., Macedo, J., Quintino,

J., Anjos, M., Florentin, F., da Silva, F. Q., and Santos,

A. L. 2018. "Virtual Assistants for Mobile Interaction:

A Review from the Accessibility Perspective,"

Proceedings of the 30th Australian Conference on

Computer-Human Interaction, pp. 568-571.

Solem, B. A. A. 2016. "Influences of Customer

Participation and Customer Brand Engagement on

Brand Loyalty."

Sotolongo, N., and Copulsky, J. (2018). "Conversational

marketing: Creating compelling customer

connections". Applied Marketing Analytics, 4(1), 6-21.

Suhel, S. F., Shukla, V. K., Vyas, S., & Mishra, V. P. 2020,

June). Conversation to Automation in Banking through

Chatbot Using Artificial Machine Intelligence

Language. In 2020 8th International Conference on

Reliability, Infocom Technologies and Optimization

(Trends and Future Directions) (ICRITO) (pp. 611-

618). IEEE.

Tabet, S., Bhogaraju, P., and Ash, D. 2000. "Using Xml as

a Language Interface for Ai Applications," Pacific Rim

International Conference on Artificial Intelligence:

Springer, pp. 103-110.

Venkatesh, A., Khatri, C., Ram, A., Guo, F., Gabriel, R.,

Nagar, A., Prasad, R., Cheng, M., Hedayatnia, B., and

Metallinou, A. 2018. "On Evaluating and Comparing

Conversational Agents," 4, pp. 60-68.

Webster, J., and Watson, R. T. (2002). Analyzing the past

to prepare for the future: Writing a literature review.

MIS quarterly.

Weizenbaum, J. 1966. "Eliza—a Computer Program for the

Study of Natural Language Communication between

Man and Machine," 9:1, pp. 36-45.

Xie, Q., Tan, D., Zhu, T., Zhang, Q., Xiao, S., Wang, J., Li,

B., Sun, L., and Yi, P. 2019. "Chatbot Application on

Cryptocurrency," 2019 IEEE Conference on

Computational Intelligence for Financial Engineering

& Economics (CIFEr): IEEE, pp. 1-8.

Yamaguchi, H., Mozgovoy, M., and Danielewicz-Betz, A.

2018. "A Chatbot Based on Aiml Rules Extracted from

Twitter Dialogues," FedCSIS (Communication Papers),

pp. 37-42.

Zarouali, B., Van den Broeck, E., Walrave, M., and Poels,

K. 2018. "Predicting Consumer Responses to a Chatbot

on Facebook," 21:8, pp. 491-497.

WEBIST 2021 - 17th International Conference on Web Information Systems and Technologies

396