Introduction

Microsoft introduced the concept of conversations as a platform in early spring 2016, where artificial intelligence (AI) and natural language interaction enables new ways to communicate with interactive technology (Folstad & Brandtzaeg, 2017). Many commercial and private domains have been conquered by domain specific assistance in the shape of chatbots (Janssen et.al, 2020). Facebook offered tools for creating chatbots for messenger, its messaging app (Folstad & Brandtzaeg, 2017).

The “chatbots” come out from two words “chat” and “robot”. Chatbot is a computer program have a message-text and output that lets consumer to connect with personal assistant to know the queries (Wang & Petrina, 2013) Chatbots, which are linguistic bots, have been designed for a variety of purposes. Artificial intelligence (AI) and natural language processing (NLP) advancements are changing way virtual assistance cooperate for people (Ngugen and Sidorova, 2018; Jain et.al, 2018). Chatbots have become increasingly convenient and frequent change the text into speech and speech into text (Bittner et al, 2019). Some of the personal assistants such as Siri, Google Now, Cortane, Fcebook M, Blackberry Assistant, Braina, Tenco, Speaktoit Assistant, Hound, Amazon Echo (Alexa) were created with the goal of assisting people in their daily lives as voice – activated intelligent personal assistants. Chatbot have grown expontially as a result of the emergence of these services (Janssen et al, 2017).

In this analysis, the word “chatbot” refer to an autonomous conversational entity that engages purpose or task – oriented discourse via a text- based environment (Chaves et al, 2021) . Simultaneously, the virtual assistant created for the purpose of changing human views and their behaviour (Fogg 2002; Mirsch et al, 2017; Oinas-Kukkonen & Harjumaa, 2009; Weinmann et.al, 2016). People are consideredin this pretty severe perspective “artifacts shaped and used by the (system of) technology rather than vice-versa” (Demetis & Lee, 2018). As a result, in addition to differing degrees are engagement, intellect and individual agency.

A chatbot is a software which communicates with humans by using natural language and analysis to understand a queries and respond accordingly (Mittal et al, 2016). Chatbots have become increasingly popular in real-world applications due to their ability to precisely mimic human representatives during conversations. Chatbots are available 24 hours a day, seven days a week and cost less than humans (Mittal et al, 2016).Virtual Assistant (VA) is defined as & quotas system that uses information such as a person’s voice, inputs, and logical knowledge to send information by analysing inquiries such as dialect, current options, and executing tasks (Zaidi et al. 2021). In computer encounters, man continues to supplant human contact, while expanding gross sales and distribution on-line gain quickly. In the technical market, their virtual assistants make it simple for people to work. Chatbot, similar Facebook and Google is largely dependent on android and intelligent home networks and therefore is capable of multiple communication, Although computer interactions is possible , may prefer to engage with Google’s digital using Speaking Voice. Google’s assistants may do web searches, create reminders and reminders, change drivers software upon that mobile screen , even visualize data first from recipients Gmail account. These features include a continuous improvement of the gateway with unique features such as autonomy, liveliness, and readability, as well as “information and change prospects in use time consuming activities of gathering knowledge.” When VA, hospitality service kind, and satisfaction variables are combined, the scarcity is exacerbated. With users &39; communication preferences shifting to instant messaging, the move from face-to-face, high-touch low-tech to highly virtual engagement, as described by (De Keyser et.al. 2019), becomes eminent. This forces firms to satisfy customer expectations by putting their existing services on messaging platforms. As Debecker stated in a Ubisend webinar in January 2020, a chatbot is a logical evolution to support traditional engagement methods (Benchhiba et al, 2020). He argued that chatbots are simple to set up, cost-effective, and improve customer service. VA is also said to improve the availability, stability, and social interactions of enterprises. As a result, more space is available for sales leads and conversions (Ukpabi et al, 2019). According to Accenture (2019), pressure on organisations to dramatically improve their customer service levels will define future customer service models. The new models are distinguished by more customised service and a broader range of integration capabilities. Traditional inefficient service models must be replaced with intelligent ones that provide experiences that are tailored to the needs of increasingly impatient clients, resolving issues quickly and with minimal effort. According to Accenture (2019), firms who continue to use rigid, costly service models struggle to resolve customer complaints quickly and lack visibility into the causes of retention and revenue issues (Ikumoro & Jawad, 2019).

Review of Literature

(Stoeckli et al, 2019), in their study focuses on online and flexible organizations that hire robots in the manner of connections with corporate communicators like Slack and Microsoft Teams, and it follows a three-step procedure. With 29 respondents from 17 organisations, a qualitative and preliminary study was undertaken. The finding showed trends between how employees estimate the perception of these recognised capabilities against the interpretation of constrictions, and how accomplishing these opportunities contribute to greater category capabilities of virtual assistants that greatly enhance information with abilities of conventional business applications.

(Yorita, 2019) in his study proposed chatbot psychology concept and technology which allows the robot can alter its attitude in actual environments since it engages with both the customer in conversations. The research is based on the Big Five personality paradigm and focuses on two important personal attributes: assertiveness and extroversion. A chatbots should provide supportive care has also been developed as part of the research. (Gnewuch et al, 2018) in their study stated n interpersonal interaction, the importance of distinct input indications. The study's findings revealed that the association among input indications and robots' relative social presence is influenced by the indications' design as well as the customer's expertise with virtual assistants. The study's findings provide to factual findings and conceptual information that help academicians and practitioners quickly identify as well as build better realistic social communication. (Chaves & Gerosa, 2020), investigated in their study an analysis on nonhuman text-based robots to answer the question, “What chatbots social characteristics benefit human interactions and what are the challenges and strategies associated with them?”. In this paper, it is stated that robots must be enhanced using attributes that align with customers’ assumptions, hence minimising oscillations and discontent, and is also explained how these traits interact. (Zumstein & Humdertmark , 2017 ) in their study examined Virtual assistants in public transit: more higher usage, significance, and limitations. The analysis was divided into different categories. Data was obtained from 134 clients in the public transportation sector, and the respondents of an analysed quasi were impressively adaptable to different mobile networks that swiftly accept them.

(MC Tear, 2018), the paper examined the conversational models of the main chatbot frameworks and assesses the extent to which the modal important conversational phenomenon such as follow up questions, change of the topic, out of scope utterances, and
other conversations phenomenon. The paper concluded that the conversation was a complex
system and we can’t rely on common sense nations to design conversational interferences
and modelling the sequential mechanics of conversation is essential to make interactions with
chatbots intuitive and natural.

(Chen Wei et.al, 2018) in their objective of the analysis was on interpersonal as well as helpful chatbots. The study went into the development of robots and their abilities in terms of technology. The study also demonstrates how it frames, which is built on another existing theories, can be effectively applied to meet the sector's requirements.

(Lasek & Jessa, 2013) in their study analyzed the implementations of a chatbot and challenges of chatbots in public transport. The research study divided into four phrases. Data
was collected from 134 customers in the public transport sector and the questioned of an investigated proto-type are remarkably open to new mobile services and they quickly adopt to
these technology.

(MC Tear, 2018), the paper examined the conversational models of the main chatbot frameworks and assesses the extent to which the modal important conversational phenomenon such as follow up questions, change of the topic, out of scope utterances, and other conversations phenomenon. The paper concluded that the conversation was a complex system and we can’t rely on common sense nations to design conversational interferences and modelling the sequential mechanics of conversation is essential to make interactions with chatbots intuitive and natural.

(Chen Wei et.al , 2018 ) in their the study focused on chatbots that are both social and helpful. The analysis goes into the development of chatbots and their potential in terms of technology. The study also demonstrates how the structure, which is founded on another existing theories, may be effectively applied to meet the sector's requirements.

(Lasek & Jessa, 2013) in their study analyzed the implementations of a chatbot and a program with stimulates and intelligent with web page visitors, dedicated to hotels and guest houses. The study obtained the data from 17413 user’s statement in 4165 conversations. The result of the study indicated the implementations of the speech synthesis increase the percentage of users that book rooms online.

(Shawar, 2007) in his numerous conversational systems were presented in the study that were successful in actual areas like education, knowledge discovery, e-commerce, and entertainment. The research concluded that the goal of robot developers was to create methods to assist users, enhance the job and interact with such a machine utilizing speech recognition, rather than to completely replace or fully duplicate social interaction.

(Slater & Burden, 2009) in their incorporation of feelings and emotional conduct to artificial simulations having virtual life is the topic of a paper. The study will also serve as a better experimental setup with the in review expected in Virtual Worlds, which will involve a variety of users commenting on actual immersive activities.

(Nuruzzaman & Hussain, 2018) examined an overview of current robots as well as the strategies used to create them, as well as a discussion of their commonalities, distinctions, and limits. According to the report, 75% of customers have had crap service, and coming up with relevant, comprehensive, and insightful solutions still a difficult challenge. The research also discussed that why existing robot architecture entirely ignores context while creating replies, and whether this influences conversational efficiency.

References

Benchhiba, S. M. (2020). Customer Satisfaction with Virtual Assistance in a Hospitality Context.

Bittner, E., Oeste-Reiß, S., & Leimeister, J. M. (2019). Where is the bot in our team? Toward a taxonomy of design option combinations for conversational agents in collaborative work. In Proceedings of the 52nd Hawaii international conference on system sciences.

Chaves, A.P., & Gerosa, M.A. (2021). How should my chatbot interact? A survey on social characteristics in human–chatbot interaction design. International Journal of Human–Computer Interaction, 37(8), 729-758.

De Keyser, A., Köcher, S., Alkire, L., Verbeeck, C., & Kandampully, J. (2019). Frontline Service Technology infusion: conceptual archetypes and future research directions. Journal of Service Management.

Demetis, D., & Lee, A.S. (2018). When humans using the IT artifact becomes IT using the human artifact. Journal of the Association for Information Systems, 19(10), 5.

Fogg, B.J. (2002). Persuasive technology: using computers to change what we think and do. Ubiquity, 2002(December), 2.

Følstad, A., & Brandtzæg, P. B. (2017). Chatbots and the new world of HCI. interactions, 24(4), 38-42.

Gnewuch, U., Morana, S., Adam, M.T., & Maedche, A. (2018, December). “The Chatbot is typing…”–The Role of Typing Indicators in Human-Chatbot Interaction. In Proceedings of the 17th Annual Pre-ICIS Workshop on HCI Research in MIS (pp. 0-5).

Ikumoro, A.O., & Jawad, M.S. (2019). Intention to use intelligent conversational agents in e-commerce among Malaysian SMEs: an integrated conceptual framework based on tri-theories including unified theory of acceptance, use of technology (UTAUT), and TOE. International Journal of Academic Research in Business and Social Sciences, 9(11), 205-235.

Jain, M., Kumar, P., Kota, R., & Patel, S.N. (2018, June). Evaluating and informing the design of chatbots. In Proceedings of the 2018 Designing Interactive Systems Conference (pp. 895-906).

Janssen, A., Passlick, J., Cardona, D. R., & Breitner, M. H. (2020). Virtual Assistance in Any Context. Business & Information Systems Engineering, 62(3), 211-225.

Lasek, M., & Jessa, S. (2013). CHATBOTS FOR CUSTOMER SERVICE ON HOTELS’WEBSITES. Information Systems in Management, 2(2), 146-158.

McTear, M. (2018). Conversation modelling for chatbots: current approaches and future directions. Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2018, 175-185.

Mirsch, T., Lehrer, C., & Jung, R. (2017). Digital nudging: Altering user behavior in digital environments. Proceedings der 13. Internationalen Tagung Wirtschaftsinformatik (WI 2017), 634-648.

Mittal, P., & Singh, Y. (2016). Development of intelligent transportation system for improving average moving and waiting time with artificial intelligence. Indian Journal of Science and Technology, 9(3), 1-7.

Nguyen, Q.N., & Sidorova, A. (2018). Understanding user interactions with a chatbot: A self-determination theory approach.

Nuruzzaman, M., & Hussain, O.K. (2018, October). A survey on chatbot implementation in customer service industry through deep neural networks. In 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE) (pp. 54-61). IEEE.

Oinas-Kukkonen, H., & Harjumaa, M. (2009). Persuasive systems design: Key issues, process model, and system features. Communications of the Association for Information Systems, 24(1), 28.

Shawar, B. A., & Atwell, E. (2007, April). Different measurement metrics to evaluate a chatbot system. In Proceedings of the workshop on bridging the gap: Academic and industrial research in dialog technologies (pp. 89-96).

Slater, S., & Burden, D. (2009). Emotionally responsive robotic avatars as characters in virtual worlds. In 2009 Conference in Games and Virtual Worlds for Serious Applications (pp. 12-19). IEEE.

Stoeckli, E., Dremel, C., Uebernickel, F., & Brenner, W. (2020). How affordances of chatbots cross the chasm between social and traditional enterprise systems. Electronic Markets, 30(2), 369-403.

Ukpabi, D.C., Aslam, B., & Karjaluoto, H. (2019). Chatbot adoption in tourism services: A conceptual exploration. In Robots, artificial intelligence, and service automation in travel, tourism and hospitality. Emerald Publishing Limited.

Wang, Y.F., & Petrina, S. (2013). Using learning analytics to understand the design of an intelligent language tutor–Chatbot lucy. Editorial Preface, 4(11), 124-131.

Wei, C., Yu, Z., & Fong, S. (2018). How to build a chatbot: chatbot framework and its capabilities. In Proceedings of the 2018 10th International Conference on Machine Learning and Computing (pp. 369-373).

Weinmann, M., Schneider, C., & Vom Brocke, J. (2016). Digital nudging. Business & Information Systems Engineering, 58(6), 433-436.

Yorita, A., Egerton, S., Oakman, J., Chan, C., & Kubota, N. (2019, October). Self-adapting Chatbot personalities for better peer support. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (pp. 4094-4100). IEEE.

Zaidi, M.H., Tyagi, A., & Narayani, N. (2021). VIRTUAL ASSISTANCE: A STUDY ON USER APPLICATION AND USER EXPERIENCE OF CUSTOMER SERVICE SYSTEMS. Annals of the Romanian Society for Cell Biology, 20500-20509

Zumstein, D., & Hundertmark, S. (2017). CHATBOTS--AN INTERACTIVE TECHNOLOGY FOR PERSONALIZED COMMUNICATION, TRANSACTIONS AND SERVICES. IADIS International Journal on WWW/Internet, 15(1).

Introduction

Microsoft introduced the concept of conversations as a platform in early spring 2016, where artificial intelligence (AI) and natural language interaction enables new ways to communicate with interactive technology (Folstad & Brandtzaeg, 2017). Many commercial and private domains have been conquered by domain specific assistance in the shape of chatbots (Janssen et.al, 2020). Facebook offered tools for creating chatbots for messenger, its messaging app (Folstad & Brandtzaeg, 2017).

The “chatbots” come out from two words “chat” and “robot”. Chatbot is a computer program have a message-text and output that lets consumer to connect with personal assistant to know the queries (Wang & Petrina, 2013) Chatbots, which are linguistic bots, have been designed for a variety of purposes. Artificial intelligence (AI) and natural language processing (NLP) advancements are changing way virtual assistance cooperate for people (Ngugen and Sidorova, 2018; Jain et.al, 2018). Chatbots have become increasingly convenient and frequent change the text into speech and speech into text (Bittner et al, 2019). Some of the personal assistants such as Siri, Google Now, Cortane, Fcebook M, Blackberry Assistant, Braina, Tenco, Speaktoit Assistant, Hound, Amazon Echo (Alexa) were created with the goal of assisting people in their daily lives as voice – activated intelligent personal assistants. Chatbot have grown expontially as a result of the emergence of these services (Janssen et al, 2017).

In this analysis, the word “chatbot” refer to an autonomous conversational entity that engages purpose or task – oriented discourse via a text- based environment (Chaves et al, 2021) . Simultaneously, the virtual assistant created for the purpose of changing human views and their behaviour (Fogg 2002; Mirsch et al, 2017; Oinas-Kukkonen & Harjumaa, 2009; Weinmann et.al, 2016). People are consideredin this pretty severe perspective “artifacts shaped and used by the (system of) technology rather than vice-versa” (Demetis & Lee, 2018). As a result, in addition to differing degrees are engagement, intellect and individual agency.

A chatbot is a software which communicates with humans by using natural language and analysis to understand a queries and respond accordingly (Mittal et al, 2016). Chatbots have become increasingly popular in real-world applications due to their ability to precisely mimic human representatives during conversations. Chatbots are available 24 hours a day, seven days a week and cost less than humans (Mittal et al, 2016).Virtual Assistant (VA) is defined as & quotas system that uses information such as a person’s voice, inputs, and logical knowledge to send information by analysing inquiries such as dialect, current options, and executing tasks (Zaidi et al. 2021). In computer encounters, man continues to supplant human contact, while expanding gross sales and distribution on-line gain quickly. In the technical market, their virtual assistants make it simple for people to work. Chatbot, similar Facebook and Google is largely dependent on android and intelligent home networks and therefore is capable of multiple communication, Although computer interactions is possible , may prefer to engage with Google’s digital using Speaking Voice. Google’s assistants may do web searches, create reminders and reminders, change drivers software upon that mobile screen , even visualize data first from recipients Gmail account. These features include a continuous improvement of the gateway with unique features such as autonomy, liveliness, and readability, as well as “information and change prospects in use time consuming activities of gathering knowledge.” When VA, hospitality service kind, and satisfaction variables are combined, the scarcity is exacerbated. With users &39; communication preferences shifting to instant messaging, the move from face-to-face, high-touch low-tech to highly virtual engagement, as described by (De Keyser et.al. 2019), becomes eminent. This forces firms to satisfy customer expectations by putting their existing services on messaging platforms. As Debecker stated in a Ubisend webinar in January 2020, a chatbot is a logical evolution to support traditional engagement methods (Benchhiba et al, 2020). He argued that chatbots are simple to set up, cost-effective, and improve customer service. VA is also said to improve the availability, stability, and social interactions of enterprises. As a result, more space is available for sales leads and conversions (Ukpabi et al, 2019). According to Accenture (2019), pressure on organisations to dramatically improve their customer service levels will define future customer service models. The new models are distinguished by more customised service and a broader range of integration capabilities. Traditional inefficient service models must be replaced with intelligent ones that provide experiences that are tailored to the needs of increasingly impatient clients, resolving issues quickly and with minimal effort. According to Accenture (2019), firms who continue to use rigid, costly service models struggle to resolve customer complaints quickly and lack visibility into the causes of retention and revenue issues (Ikumoro & Jawad, 2019).

Review of Literature

(Stoeckli et al, 2019), in their study focuses on online and flexible organizations that hire robots in the manner of connections with corporate communicators like Slack and Microsoft Teams, and it follows a three-step procedure. With 29 respondents from 17 organisations, a qualitative and preliminary study was undertaken. The finding showed trends between how employees estimate the perception of these recognised capabilities against the interpretation of constrictions, and how accomplishing these opportunities contribute to greater category capabilities of virtual assistants that greatly enhance information with abilities of conventional business applications.

(Yorita, 2019) in his study proposed chatbot psychology concept and technology which allows the robot can alter its attitude in actual environments since it engages with both the customer in conversations. The research is based on the Big Five personality paradigm and focuses on two important personal attributes: assertiveness and extroversion. A chatbots should provide supportive care has also been developed as part of the research. (Gnewuch et al, 2018) in their study stated n interpersonal interaction, the importance of distinct input indications. The study's findings revealed that the association among input indications and robots' relative social presence is influenced by the indications' design as well as the customer's expertise with virtual assistants. The study's findings provide to factual findings and conceptual information that help academicians and practitioners quickly identify as well as build better realistic social communication. (Chaves & Gerosa, 2020), investigated in their study an analysis on nonhuman text-based robots to answer the question, “What chatbots social characteristics benefit human interactions and what are the challenges and strategies associated with them?”. In this paper, it is stated that robots must be enhanced using attributes that align with customers’ assumptions, hence minimising oscillations and discontent, and is also explained how these traits interact. (Zumstein & Humdertmark , 2017 ) in their study examined Virtual assistants in public transit: more higher usage, significance, and limitations. The analysis was divided into different categories. Data was obtained from 134 clients in the public transportation sector, and the respondents of an analysed quasi were impressively adaptable to different mobile networks that swiftly accept them.

(MC Tear, 2018), the paper examined the conversational models of the main chatbot frameworks and assesses the extent to which the modal important conversational phenomenon such as follow up questions, change of the topic, out of scope utterances, and
other conversations phenomenon. The paper concluded that the conversation was a complex
system and we can’t rely on common sense nations to design conversational interferences
and modelling the sequential mechanics of conversation is essential to make interactions with
chatbots intuitive and natural.

(Chen Wei et.al, 2018) in their objective of the analysis was on interpersonal as well as helpful chatbots. The study went into the development of robots and their abilities in terms of technology. The study also demonstrates how it frames, which is built on another existing theories, can be effectively applied to meet the sector's requirements.

(Lasek & Jessa, 2013) in their study analyzed the implementations of a chatbot and challenges of chatbots in public transport. The research study divided into four phrases. Data
was collected from 134 customers in the public transport sector and the questioned of an investigated proto-type are remarkably open to new mobile services and they quickly adopt to
these technology.

(MC Tear, 2018), the paper examined the conversational models of the main chatbot frameworks and assesses the extent to which the modal important conversational phenomenon such as follow up questions, change of the topic, out of scope utterances, and other conversations phenomenon. The paper concluded that the conversation was a complex system and we can’t rely on common sense nations to design conversational interferences and modelling the sequential mechanics of conversation is essential to make interactions with chatbots intuitive and natural.

(Chen Wei et.al , 2018 ) in their the study focused on chatbots that are both social and helpful. The analysis goes into the development of chatbots and their potential in terms of technology. The study also demonstrates how the structure, which is founded on another existing theories, may be effectively applied to meet the sector's requirements.

(Lasek & Jessa, 2013) in their study analyzed the implementations of a chatbot and a program with stimulates and intelligent with web page visitors, dedicated to hotels and guest houses. The study obtained the data from 17413 user’s statement in 4165 conversations. The result of the study indicated the implementations of the speech synthesis increase the percentage of users that book rooms online.

(Shawar, 2007) in his numerous conversational systems were presented in the study that were successful in actual areas like education, knowledge discovery, e-commerce, and entertainment. The research concluded that the goal of robot developers was to create methods to assist users, enhance the job and interact with such a machine utilizing speech recognition, rather than to completely replace or fully duplicate social interaction.

(Slater & Burden, 2009) in their incorporation of feelings and emotional conduct to artificial simulations having virtual life is the topic of a paper. The study will also serve as a better experimental setup with the in review expected in Virtual Worlds, which will involve a variety of users commenting on actual immersive activities.

(Nuruzzaman & Hussain, 2018) examined an overview of current robots as well as the strategies used to create them, as well as a discussion of their commonalities, distinctions, and limits. According to the report, 75% of customers have had crap service, and coming up with relevant, comprehensive, and insightful solutions still a difficult challenge. The research also discussed that why existing robot architecture entirely ignores context while creating replies, and whether this influences conversational efficiency.

References

1. Benchhiba, S. M. (2020). Customer Satisfaction with Virtual Assistance in a Hospitality Context.
2. Bittner, E., Oeste-Reiß, S., & Leimeister, J. M. (2019). Where is the bot in our team? Toward a taxonomy of design option combinations for conversational agents in collaborative work. In Proceedings of the 52nd Hawaii international conference on system sciences.
3. Chaves, A.P., & Gerosa, M.A. (2021). How should my chatbot interact? A survey on social characteristics in human–chatbot interaction design. International Journal of Human–Computer Interaction, 37(8), 729-758.
4. De Keyser, A., Köcher, S., Alkire, L., Verbeeck, C., & Kandampully, J. (2019). Frontline Service Technology infusion: conceptual archetypes and future research directions. Journal of Service Management.
5. Demetis, D., & Lee, A.S. (2018). When humans using the IT artifact becomes IT using the human artifact. Journal of the Association for Information Systems, 19(10), 5.
6. Fogg, B.J. (2002). Persuasive technology: using computers to change what we think and do. Ubiquity, 2002(December), 2.
7. Følstad, A., & Brandtzæg, P. B. (2017). Chatbots and the new world of HCI. interactions, 24(4), 38-42.
8. Gnewuch, U., Morana, S., Adam, M.T., & Maedche, A. (2018, December). “The Chatbot is typing…”–The Role of Typing Indicators in Human-Chatbot Interaction. In Proceedings of the 17th Annual Pre-ICIS Workshop on HCI Research in MIS (pp. 0-5).
9. Ikumoro, A.O., & Jawad, M.S. (2019). Intention to use intelligent conversational agents in e-commerce among Malaysian SMEs: an integrated conceptual framework based on tri-theories including unified theory of acceptance, use of technology (UTAUT), and TOE. International Journal of Academic Research in Business and Social Sciences, 9(11), 205-235.
10. Jain, M., Kumar, P., Kota, R., & Patel, S.N. (2018, June). Evaluating and informing the design of chatbots. In Proceedings of the 2018 Designing Interactive Systems Conference (pp. 895-906).
11. Janssen, A., Passlick, J., Cardona, D. R., & Breitner, M. H. (2020). Virtual Assistance in Any Context. Business & Information Systems Engineering, 62(3), 211-225.
12. Lasek, M., & Jessa, S. (2013). CHATBOTS FOR CUSTOMER SERVICE ON HOTELS’WEBSITES. Information Systems in Management, 2(2), 146-158.
13. McTear, M. (2018). Conversation modelling for chatbots: current approaches and future directions. Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2018, 175-185.
14. Mirsch, T., Lehrer, C., & Jung, R. (2017). Digital nudging: Altering user behavior in digital environments. Proceedings der 13. Internationalen Tagung Wirtschaftsinformatik (WI 2017), 634-648.
15. Mittal, P., & Singh, Y. (2016). Development of intelligent transportation system for improving average moving and waiting time with artificial intelligence. Indian Journal of Science and Technology, 9(3), 1-7.
16. Nguyen, Q.N., & Sidorova, A. (2018). Understanding user interactions with a chatbot: A self-determination theory approach.
17. Nuruzzaman, M., & Hussain, O.K. (2018, October). A survey on chatbot implementation in customer service industry through deep neural networks. In 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE) (pp. 54-61). IEEE.
18. Oinas-Kukkonen, H., & Harjumaa, M. (2009). Persuasive systems design: Key issues, process model, and system features. Communications of the Association for Information Systems, 24(1), 28.
19. Shawar, B. A., & Atwell, E. (2007, April). Different measurement metrics to evaluate a chatbot system. In Proceedings of the workshop on bridging the gap: Academic and industrial research in dialog technologies (pp. 89-96).
20. Slater, S., & Burden, D. (2009). Emotionally responsive robotic avatars as characters in virtual worlds. In 2009 Conference in Games and Virtual Worlds for Serious Applications (pp. 12-19). IEEE.
21. Stoeckli, E., Dremel, C., Uebernickel, F., & Brenner, W. (2020). How affordances of chatbots cross the chasm between social and traditional enterprise systems. Electronic Markets, 30(2), 369-403.
22. Ukpabi, D.C., Aslam, B., & Karjaluoto, H. (2019). Chatbot adoption in tourism services: A conceptual exploration. In Robots, artificial intelligence, and service automation in travel, tourism and hospitality. Emerald Publishing Limited.
23. Wang, Y.F., & Petrina, S. (2013). Using learning analytics to understand the design of an intelligent language tutor–Chatbot lucy. Editorial Preface, 4(11), 124-131.
24. Wei, C., Yu, Z., & Fong, S. (2018). How to build a chatbot: chatbot framework and its capabilities. In Proceedings of the 2018 10th International Conference on Machine Learning and Computing (pp. 369-373).
25. Weinmann, M., Schneider, C., & Vom Brocke, J. (2016). Digital nudging. Business & Information Systems Engineering, 58(6), 433-436.
26. Yorita, A., Egerton, S., Oakman, J., Chan, C., & Kubota, N. (2019, October). Self-adapting Chatbot personalities for better peer support. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (pp. 4094-4100). IEEE.
27. Zaidi, M.H., Tyagi, A., & Narayani, N. (2021). VIRTUAL ASSISTANCE: A STUDY ON USER APPLICATION AND USER EXPERIENCE OF CUSTOMER SERVICE SYSTEMS. Annals of the Romanian Society for Cell Biology, 20500-20509
28. Zumstein, D., & Hundertmark, S. (2017). CHATBOTS--AN INTERACTIVE TECHNOLOGY FOR PERSONALIZED COMMUNICATION, TRANSACTIONS AND SERVICES. IADIS International Journal on WWW/Internet, 15(1).