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Conversational AI – How Intelligent Chatbots Disrupt Firms and Industries

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Chatbots are a digital and disruptive technology with implications on industry- and firm-level dynamics. But what exactly is a chatbot? Which characteristics define it as a digital technology? And how does it disrupt industries and firms? These questions will be answered in the following sections.

Chatbot

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A chatbot is a text-based dialog system, which uses natural-language processes to generate smart conversations with a user[1]. Currently a wide variety of chatbots exists, mainly divisible in rules-driven, less intelligent and AI-driven, intelligent bots[2]. However, this wiki-page solely refers to the intelligent ones, which are based on a combination of technologies[3]. To understand the design of a bot, a short explanation of the integrable technologies is necessary. Chatbots use machine learning (ML), which is a collective term in computer science for learning from data through the research and development of mathematical models or algorithms[4]. ML includes techniques like natural language processing, which is used to analyse and interpret human and programming language[5] or deep learning, which applies neuronal networks to discover complex structures in large datasets by using algorithms[6]. All these technologies can be combined in intelligent chatbots and thus enable them to be smart and work autonomous.

Characteristics of Chatbots

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In contrast to traditional technologies, digital technologies like chatbots are convergent and generative. Convergence describes the process of connecting previously separated user experiences and generativity focuses on the technology’s dynamic capacity and malleability[7].

Convergence
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Since all digital information can be processed by the same technologies[8], a companies』 chatbot can get seamlessly integrated in social media platforms like Facebook Messenger or Skype, which have opened to developers via APIs[9]. APIs thereby decouple the chatbots from its origin and homogenize it with another platform or program. Since this became relatively simple, there are various platform interactions in the field of Chatbots[10]. Platforms like Luis.ai offer possibilities for the implementation of machine learning[11], to include artificial intelligence without programming it oneself. Luis.ai thereby shows a good example of positive, direct network effects, because the benefit of Luis.ai grows, when the number of participants increases[12]. With every participant joining the platform the dataset gets enriched by collecting big quantities of real-world data without any costs[13]. Having up-to-date data increases the ML capabilities and thereby the chatbot performance which thus is beneficial for developers again.

Decoupling also takes place when a company uses cloud-computing for building up an chatbot infrastructure[14]. This is due to the fact that the original information is not stored at the company itself anymore. Outsourcing the infrastructure reduces the setup costs of the chatbot[15]. However, even if a company does not use cloud-computing, the establishment of chatbots becomes increasingly cheap. Nowadays the build of spacious commodity-computer data centres is possible, due to decreasing costs of hardware, software and network bandwidth[16], as explained in Moore’s law. Since there are little additional costs, when the infrastructure is created, the marginal costs for chatbots are low and make the technology available relatively cheap.

Another feature of a chatbot is its connectivity: a chatbot connects several parties. On one side it connects customers with companies as a virtual assistant providing information about products and services, answering requests and taking orders[17]. On the other side chatbots are connectable to various additional applications and infrastructures[18]. An example of this is Amazon’s Echo 「Alexa」, which is linkable to video doorbells, smart thermostats, or other smart home devices connected to the same Wi-Fi[19]. By connecting several smart home devices within one application, 「Alexa」 is able to create a convergent user experience (UX). Next to the IoT integration developers also start to connect chatbots among each other in systems like swarm bots, bot farms or bot networks[20], what can even increase the convergent UX. We will elaborate on Alexa in the section IT industry.

Generativity
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A chatbot can train itself. Intelligent bots do not only improve their knowledge through answers and reactions of the users, but also by autonomous online search[21]. When connecting all this information with techniques like machine learning or natural language processing[22] the chatbot creates new content and is able to constantly improve itself. This smartness also helps to develop new products and functionalities on a regular manner. Next to smartness, the modularity of a chatbot can also be an important aspect for emerging functionalities. Modularity describes the decomposition of a domain into modules, where each module has a standardized interface to interact with the others[23]. By having the possibility to add extra modules to a chatbot the technology can be more flexible towards change. An example of the use of modularity is Apple’s SiriKit. SiriKit enables developers to create apps which can get used by Siri (Apple, n.d.). That means whenever a new trend occurs, and developers subsequently create new apps with SiriKit, the bot is immediately able to include those new apps in its services by only including further modules in the chatbot, without influencing the systems completeness or functionality[24]. During all the time a chatbot is running, connecting or improving itself digital information is generated, as a result digital traces occur[25], documenting every action and interaction from and with the bot.

Chatbots applications

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Service industry
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Chatbots combined with Artificial Intelligence have, since their introduction, made waves across various industries. The most notable one of these is the customer service industry. The customer service industry entails almost every other industry where consumers are involved. ... Where customer service used to be an industry that needed friendly, adequate and responsible employees to talk and support their customer base, the landscape of the playing field here has been changing more towards getting adequate answers in as timely a manner as possible[26] , [27], [28]. For a normal human working behind a computer it is almost impossible to supply five customers with adequate and fast answers at the same time[29]. Chatbots makes their jobs easier, being able to recognize text and basing answers of the input given by the customers. These days chatbots disrupting, by slowly taking over entire parts of the customer agents』 job[30], merely because they are getting much more intelligent, as previously illustrated. Chatbots, with the implementation of AI, now have the skillset to adequately, and swiftly, respond to a customer and even learn from what he or she is saying. An invaluable tool for an industry which is in dire need of a method to respond to the customers』 increasingly easy way to fire any question at will.

The industry itself is reacting with an attacking, offensive response. They face the challenge head-on, and implement the technology themselves in order not to stay behind. Business Insider[31] showed that, by 2020, 80% of senior c-level executives investigated in Europe, want to have implemented chatbots in their firm. The following examples will illustrate the implementation, disruption and use of chatbots in the service industry;

A company that has already implemented the use of chatbots is Instalocate. Instalocate, working in the airline industry, has implemented a chatbot that uses AI and Big Data to scan the web for information about flights from any carrier. They can then inform consumers immediately about issues like flight delays, or whether the flight has WiFi. It is disrupting the industry in such a way that customer agents are no longer necessary for different airlines, since it provided the option to get you your information without having to talk to humans. Its modular nature has even provided it with the possibility to be used via Facebook Messenger. The information the company provides is not new, but the way it is supplying it has the potential to radically change service in the airline industry[32].

Another example is the chatbot Nina. Nina, a chatbot manufactured by Nuance Communcations to provide AI based service, is being used in several industries. Nuance Communications Inc. is an American, stock listed company specialized in voice recognition and scanning software. Nuance communications has a turnover of 1.934 billion and 11.600 employees worldwide[33]. Their chatbot assistant for enterprise customer service, 『Nina』 is ranked number one among the most significant chatbot providers. Nina has been adopted globally by leading brands like Coca Cola, ING Netherlands and more[34]. Nina has particularly gained ground in the incumbent banking sector, where for instance Swedbank and the Commonwealth Bank of Australia (CBA) have started using its smart, machine learning technologies to answer all kinds of questions from consumers. For instance, the bot can help customers find the banking products most suited for their needs, as well as guide them towards the perfect human assistent for the job to help with more complex problems. Its modular nature makes it possible to access Nina on all kinds of communication channels, such as Amazon Echo, Facebook Messenger, or even through SMS.

Other players in the banking industry have shown to implement chatbots as well. Nordea Bank Abp is a financial services group that wants to change the entire way their associated banks tackle service. They manufactured their own chatbot, Nova, which uses machine learning and Data scanning, to reach the goal of ultimately cutting around 6,000 jobs[35]. Nova is sure to shake up the service side of the banking industry rather soon than later.

A final example can be found within the hospitality industry. Marriot, one of the leading companies in this field, have taken to developing their own chatbot: ChatBotIr. It was done to provide their clients quicker check-in and check-out options. Two of every three hotel clients did interact with ChatBotIr[36]. The average response time was five seconds, even quicker than a well-trained agent. Through machine learning the chatbot can, in time, grow even quicker, improve its smartness and maybe in the future reduce the need for customer service agents in the hospitality industry.

IT industry
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IT platforms have gained considerable traction lately. They are an information technology industry with components and rules that facilitate interactions among a network of users[37] such as Facebook and Weibo. Besides IT companies which have their own platforms, more IT companies are also users of platforms, they might use platforms to connect with others for marketing or public relationship activities. Consequently, platforms have important implications both inside and outside the IT industry. With the entering of chatbot technology into platforms, the IT industry is disrupted. By developing chatbot platforms, chatbot, this innovative and advanced technology is accessible for almost every IT companies and even every user. In other word, the development of chatbot platforms enables the popularization of chatbot.

The chatbot ecosystem include deployment channels, third-party companies, chatbot technology companies and native companies[38]. Most chatbot enabling technology companies are building their own chatbot platforms, and providing interfaces to the deployment channels and the third-party companies, while the native companies are developing their own chatbots through their own platforms.

chatbot ecosystem

In this case, the chatbot technology is not monopolized by the industry giants any more. As chatbot modules are accessible through platforms, more and more middle and small-sized enterprises could also enjoy the convenience and efficiency brought by the chatbots.

It shows that chatbot platform could have impacts on the whole ecosystem. An example is passage.ai, a company founded in 2016. It is devoting itself to help other companies to develop chatbots. Building its own chatbot platform and providing companies with DIY chatbots modules, passage.ai makes the chatbot technology more accessible and reduces the technical threshold as well as initial investment for normal companies. So far, passage.ai has already raised 7.3 million dollars for further development[39]. As the chatbot technology is getting gradually mature, an increasing number of companies want to have their own chatbots without spending too many resources on developing them. Passage.ai successfully seize this opportunity and has a bright future. Furthermore, as the chatbot technology is accessible through different kinds of platforms, it becomes common among different IT companies. It reflects that the development of chatbot platforms disrupting and pushing the whole industry forward by providing low-cost modularized chatbots, increasing the efficiency and offering convenience to both other companies and guests.

Another example is the company Gupshup, Gupshup is the leading smart messaging platform that handles over 4 billion messages per month. It offers API’s for developers to build and program their own messaging bots[40] by implementing the modularity, its customers can DIY their own chatbots by adding their preferred complementary peripheral components to the core functions of the chatbots. Thus, the variety of gupshup’s products are significantly increased and resources of the company are saved as well. In addition, the company Chatbotpack offers 『top-tuned Artificial Intelligence’. People can buy the chatbot 『Laura, which is according to Chatbotpack (2018) [41]; 「the most advanced modular chatbot platform to which people can import new modules and integrations」. As the examples indicate, nowadays, everybody could have their own chatbot due to the development of chatbot platforms. If the chatbot is widely adopted, people’s lifestyle could be further disrupted.

However, besides platform-building companies, more native firms are developing and integrating chatbot platforms in the company.

As Amazon has already connected devices to the Internet platform and realized the IoT integration, developers are also working on bring chatbot inside the existed platform to increase the direct network. As the platform integrates all chatbots』 functions within the platform, to a great extent reduce the cross-functional complexity. It could bring the user disruptive using experience. In addition, useful data such as frequent asked questions, system bugs and common errors could be collected and stored together, the research crews could get access to the big data collected through the chatbot platform and develop correspondent patch and update. It takes much less time to work from the aspect of the whole project and the change could be applied simultaneously. In 2014, Amazon’s Echo 「Alexa」 was born. As the initiator of bringing listening and responding systems to the IoT[42], Amazon is taking the 「racing」 response towards the development of disruptive chatbot technology. The Amazon Echo allows the chatbot to process inputs via its cloud-based web server, making it better identify the request and response to them. During the initial stage of implementing, Amazon lower the price of the chatbots to finish the first stage of information collection ($199, or $99 for Prime members). Besides, due to its numerous users, Amazon, by keep updating, seize the opportunity provided by the chatbot technology by quickly taking the market lead position, increasing the thresholds for other entrants[43].

Social media giant Facebook expanded its official communication software Facebook Messager’s functions by allowing users to adopt and adapt their Facebook chatbots to their own preferences. Facebook users can use existed modules to design their own chatbots.

Flowchart

With the user-friendly interfaces and Facebook’s periodical updates, users can easily adapt the chatbot to the style which suits themselves best. Each one can be used to deal with different things such as marketing, selling and tour-guiding. For those people who do their businesses through Facebook, the chatbot service provides them incomparable convenience among resource allocation and they could also be more loyal users of Facebook. In this case, Facebook is using "transition" response, based on the incumbent disruptive technology response theory[44], to separate from its existing business and adopting ambidexterity.

While Amazon and Facebook are actively responding to the disruptive chatbot technology, another IT giant, Apple, has already taken its own market. As Siri was introduced in 2011 as the most impressed function of iPhone 4S, Apple stepped into the conversational world in the early stage and took the leadership[45]. During the following years, Apple also takes the racing response to the development of chatbot technology since it frequently launches update among the whole system. Moreover, Apple developed SiriKit, a chatbot platform in the original IOS system. As the development of modularized chatbots is inside Apple’s system and could be directly used by Siri[46], users could extend the function of siri with the whole system’s completeness undisturbed[47]. Besides, because of the development of technology, Apple makes it possible for the intent extension. Customer could choose to launch their own extensions and make the chatbot more personal. Apple also seize the opportunity by developing the chatbot among its well-founded platforms.

Interorganizational
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Another application that is worth considering is that of chatbot use in internal communications. Where the customer service industry is disrupted in a heavy way due to less need for actual human employees, in the internal communication industry chatbots provide a way to make everything run smoother[48], therefore sustaining it rather than disrupting it. It practically adds on to existing ERP systems, smoothing over the processes of getting answer within a company through its modular and connective nature. This facilitates the way employees may gather all types of information from one chatbot rather than a dozen systems or colleagues that all need to be addressed[49]. The system can make an uniform user interface, creating a better experience, as well as making it homogenised. AI can even help the chatbot anticipate questions when by for instance paid leave information is requested, the form to apply for it is actively supplied. This reduces the time any employee needs to actively gather the answers that he needs for any question within a company, giving that employee more time to do the work he is supposed to. This in turn makes for more profit for the implementing company[50].

To give an example, Danone has already successfully implemented a chatbot inside their company, with the use of Workplace by Facebook and Clevy[51]. The internal success that it has gathered there, with lots of happy employees, shows that there is great potential for businesses to implement this technology. Especially larger conglomerates can create an advantage here, by eliminating internal ruse and getting employees a tool to help them answer all their internal questions swiftly. While chatbots inside an industry are still only sustaining, there is no denying that it is an invaluable tool. The industry right now is still ignoring it for the most part, but this will transition towards adoption in less time than you would think[52].

Healthcare
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The healthcare industry, someone might not expect chatbots to be a viable technology, is expected to have great potential for product enhancement and disruption due to intelligent bots[53]. Looking at the technology improvement s-curve (see figure 3), chatbots are still at the very beginning in this industry, but they already overcame major technical obstacles of implementation. They for instance are already implemented for smarter scheduling of operations[54], even if data security may have impeded this realisation. On the long term there is potential, that robots will carry out diagnosis or even treat patients[55].

An innovator within this field is the company Woebot. Woebot offers Behavioral Therapy with artificial intelligence and has opened ways for people feeling anxiety, depression or other mental illnesses to express their concerns and talk about them without needing another person to listen. They aim to design chatbots that can accurately test whether the respondent has symptoms related to mental diseases, capture the emotion change and response in the right ways to improve the patients』 status. Even if the Woebot is still inferior than a psychiatrist, there is already proof, that talking to an AI could be as useful as talking to an expert[56] and that patients do get better after talking to the Woebot for more than 2 weeks[57]. This speaks for a great potential of Woebot, to sooner or later disrupt the area of depression therapy. In contrary to the typical feature of a disruptive innovation, which would be to only attract fring customer groups[58], Woebot does not aim to be only attractive for patients who do not receive any treatment at all, but also also wants to attract patients which receive therapies to support them in their progress[59].

The Future of Chatbots

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Above examples show that chatbots have the potential to massively disrupt industries, such as in the example of Nova, where Nordea Bank IBP uses it to ultimately cut around 6,000 jobs or bot Nina, which has been adopted globally by leading brands. However, even though chatbots have the potential to disrupt, they will not replace customer service agents completely, according to Alex Galert. Galert, CEO of Brn.ai (a major player in the chatbot industry) has found out that only 20% of implementers of chatbot technology want to implement it with disruptive intentions[60]. The rest wanted to use the technology to sustain their industry, relieving stress of customer agents』 jobs.

A reason for this could be that the product still has its limitations in the customer service industry. According to Lee, Chatbots are still not able to understand human emotions[61], which is a crucial skill in customer service. This would support the idea, that chatbots are in the pioneering science phase within the technology improvement s-curve, because they only make small progresses in understanding emotions. Nevertheless, chatbots like Nina or Nova certainly represent experienced chatbots, where learning already took place. That would lead to the assumption, that the customer service industry is already sustaining mastery over chatbots. This comparison shows, that one is not able to pinpoint the improvement status of a chatbot within the service industry by the model and thus predictions of future improvement can be difficult.

S-curve

A further uncertainty for the future of chatbots is the regulatory issue. A certain technology can outperform any comparative employee or technology, but still be held back by regulations. The GDPR has restricted companies worldwide in storing customer data. This could mean that big data that fuels chatbot performance declines, resulting in a machine learning algorithm that has no data to learn from. Closely connected to regulations are general privacy security issues. A recent news story about privacy violations is covering the hack of Ticketmaster, to get to customers card payment details[62]. For firms the issue in this case is: Who to blame, the chatbot developers or the own company? And how can they make sure that the digital traces, produced when customers use the chatbot, are securely stored and protected from hackings?

Another trend to be observed, will be how the incumbent firms further react to the chatbot disruption. The theory of incumbent’s responses[63] states that firms can react in three ways: defensive, offensive or they can retreat. An interesting determination is visible when looking at the IT industry described above. All incumbents described before, like Facebook, Amazon or Apple, are reacting either defensive or offensive, but no incumbent retreats. It might be, that the theory of incumbent’s responses differs between traditional and fully digital businesses, because it seems to be no option at all for the big IT firms to retreat. Instead, they want to be ahead of the trend. That the urge of rushing, visible by the actions of the big players, can have far-reaching consequences, shows the example of Microsoft’s chatbot Tay. Twitterbot Tay was launched in 2016 by Microsoft, with the aim to pick up human habits of speech and improve the conversational understanding[64]. It shut down again less than 24 hours after its release, because it generated inappropriate tweets including racist, sexist and anti-semitic language[65]. Microsoft tried to win the race of chatbots in Twitter but heavely failed, because the bot was not yet able to distinguish normal speaking patterns form intentional user abuse. This sample might be an alert for incumbent IT firms to rethink their race into a response on chatbots in future.

Citations

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  1. ^ Khan, R. & Das, A. (2018). Build Better Chatbots. A Complete Guide to Getting Started with Chatbots. New York: Apress.
  2. ^ Lee, J. (2018, May 6). Chatbots were the next big thing: what happened? Retrieved September 22, 2018, from https://blog.growthbot.org/chatbots-were-the-next-big-thing-what-happened
  3. ^ Arthur, B. (2009). The nature of technology: What it is and how it evolves. New York: Free Press.
  4. ^ Suthaharan, S. (2016). Machine Learning Models and Algorithms for Big Data Classification. Boston: Springer US.
  5. ^ Gentsch, P. (2018). Künstliche Intelligenz für Sales, Marketing und Service. Mit AI und Bots zu einem Algorithmic Business - Konzepte, Technologien und Best Practices. Wiesbaden: Springer Fachmedien.
  6. ^ LeCun,Y., Bengio, Y. & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
  7. ^ Yoo, Y., Boland Jr, R.J., Lyytinen, K. & Majchrzak, A. (2012). Organizing for innovation in the digitized world. Organization Science, 23(5), 1398-1408.
  8. ^ Tilson, D., Lyytinen, K. & Sørensen, C. (2010). Research Commentary: Digital Infrastructures: The Missing IS Research Agenda. Information Systems Research, 21(4), 748–759.
  9. ^ Khan, R. & Das, A. (2018). Build Better Chatbots. A Complete Guide to Getting Started with Chatbots. New York: Apress.
  10. ^ Khan, R. & Das, A. (2018). Build Better Chatbots. A Complete Guide to Getting Started with Chatbots. New York: Apress.
  11. ^ Microsoft. (n.d.). LUIS. Retrieved from https://www.luis.ai/home
  12. ^ Gawer, a. (2014). Bridging differing perspectives on technological platforms: Toward an integrative framework. Research Policy, 43 (2014), 1239-1249.
  13. ^ Gilmartin, E. & Campbell, N. (2014). More Than Just Words: Building a Chatty Robot. In Mariani, J., Rosset, S., Garnier-Rizet, M., Devillers, L., (Eds.), Natural Interaction with Robots, Knowbots and Smartphones. Putting Spoken Dialog Systems into Practice. New York: Springer. Science+Business Media.
  14. ^ Khan, R. & Das, A. (2018). Build Better Chatbots. A Complete Guide to Getting Started with Chatbots. New York: Apress.
  15. ^ Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., . . . Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.
  16. ^ Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., . . . Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.
  17. ^ Gentsch, P. (2018). Künstliche Intelligenz für Sales, Marketing und Service. Mit AI und Bots zu einem Algorithmic Business - Konzepte, Technologien und Best Practices. Wiesbaden: Springer Fachmedien.
  18. ^ Goossenaerts, J. B. M. (2006). A Domain Model for the IST Infrastructure. In Konstantas, D., Bourrières, J.-P., Léonard, M., Boudjlida, N., (Eds.), Interoperability of Enterprise Software and Applications. London: Springer.
  19. ^ Deagon, B. (2018, Feb 28). Amazon fulfilling smart home dream with ring buy, alexa growth. Investor's Business Daily. Retrieved from https://search-proquest-com.vu-nl.idm.oclc.org/docview/2008991652?accountid=10978
  20. ^ Gentsch, P. (2018). Künstliche Intelligenz für Sales, Marketing und Service. Mit AI und Bots zu einem Algorithmic Business - Konzepte, Technologien und Best Practices. Wiesbaden: Springer Fachmedien.
  21. ^ Gentsch, P. (2018). Künstliche Intelligenz für Sales, Marketing und Service. Mit AI und Bots zu einem Algorithmic Business - Konzepte, Technologien und Best Practices. Wiesbaden: Springer Fachmedien.
  22. ^ Khan, R. & Das, A. (2018). Build Better Chatbots. A Complete Guide to Getting Started with Chatbots. New York: Apress.
  23. ^ Friedman, R., Kermarrec, A.-M. & Raynal, M. (2008). Modularity: a First Call Concept to Address Distributed Systems. ACM SIGACT News, 39 (2), 91-110.
  24. ^ Friedman, R., Kermarrec, A.-M. & Raynal, M. (2008). Modularity: a First Call Concept to Address Distributed Systems. ACM SIGACT News, 39 (2), 91-110.
  25. ^ Kneidinger-Müller, B. (2018). Self-Tracking Data as Digital Traces of Identity: A Theoretical Analysis of Contextual Factors of Self-Observation Practices. International Journal of Communication 12(2018), 629–646.
  26. ^ Johnson, N. (n.d.). 69% of Consumers Prefer Chatbots For Quick Communication with Brands. Retrieved September 24, 2018, from https://www.salesforce.com/blog/2018/01/why-consumers-prefer-chatbots.html
  27. ^ Hemmah, C. (2013, April 8). The impact of customer service on customer lifetime value. Retrieved September 24, 2018, from https://www.zendesk.com/resources/customer-service-and-lifetime-customer-value/
  28. ^ Astute Solutions. (2017, May 27). The 5 Most Important Trends in Customer Self-Service. Retrieved September 24, 2018, from https://www.astutesolutions.com/blog/articles/5-trends-in-consumer-demand-for-digital-self-service
  29. ^ Astute Solutions. (2017, May 27). The 5 Most Important Trends in Customer Self-Service. Retrieved September 24, 2018, from https://www.astutesolutions.com/blog/articles/5-trends-in-consumer-demand-for-digital-self-service
  30. ^ Peterson, B. (2017, September 27). How chatbots could change customer service over the next 5 years. Retrieved 24 September 2018 from https://www.businessinsider.nl/how-chatbots-could-change-customer-service-over-the-next-5-years-2017-9/?international=true
  31. ^ Business Insider. (2016, December 14). 80% of businesses want chatbots by 2020. Retrieved October 5, 2018, from https://www.businessinsider.com/80-of-businesses-want-chatbots-by-2020-2016-12?international=true&r=US&IR=T
  32. ^ Business Insider. (2016, December 14). 80% of businesses want chatbots by 2020. Retrieved October 5, 2018, from https://www.businessinsider.com/80-of-businesses-want-chatbots-by-2020-2016-12?international=true&r=US&IR=T
  33. ^ Singh, R. (2017, October 15). How Instalocate helps aggrieved flyers get compensation from airlines. Retrieved October 2, 2018, from https://economictimes.indiatimes.com/small-biz/startups/how-instalocate-helps-aggrieved-flyers-get-compensation-from-airlines/articleshow/61085741.cms
  34. ^ Singh, R. (2017, October 15). How Instalocate helps aggrieved flyers get compensation from airlines. Retrieved October 2, 2018, from https://economictimes.indiatimes.com/small-biz/startups/how-instalocate-helps-aggrieved-flyers-get-compensation-from-airlines/articleshow/61085741.cms
  35. ^ Hoikkala, H., & Schwartzkopff, F. (2017). Nordea Bank’s 6,000 Job Cuts Are Just the Beginning, Union Says. Retrieved from https://www.bloomberg.com/news/articles/2017-12-01/nordea-bank-s-6-000-job-cuts-are-just-the-beginning-union-says
  36. ^ Russon, M. (2014). Meet Botlr: World's First Robot Bellhop Being Trialled at Silicon Valley Hotel. Retrieved from https://www.ibtimes.co.uk/meet-botlr-worlds-first-robot-bellhop-being-trialled-silicon-valley-hotel-1460973
  37. ^ Kneidinger-Müller, B. (2018). Self-Tracking Data as Digital Traces of Identity: A Theoretical Analysis of Contextual Factors of Self-Observation Practices. International Journal of Communication 12(2018), 629–646.
  38. ^ Nguyen, MAIHANH (2017, October 20). The latest market research, trends & landscape in the growing AI chatbot industry. Consulted on October 4, 2018, from https://www.businessinsider.com/chatbot-market-stats-trends-size-ecosystem-research-2017-10?international=true&r=US&IR=T
  39. ^ Azevedo, M. (2018). Passage AI Raises $ 7.3M to Turn Conversational AI into A Global Business. Retrieved from https://news.crunchbase.com/news/passage-ai-raises-7-3m-turn-conversational-ai-global-business/
  40. ^ Maruti Techlabs. (n.d.). 14 MOST POWERFUL PLATFORMS TO BUILD A CHATBOT. Retrieved from https://www.amdocs.com/media-room/consumers-want-female-and-funny-not-youthful-chatbots
  41. ^ Chatbotpack. (n.d.). Technology - ChatBot Pack. Retrieved September 22, 2018, from https://www.chatbotpack.com/technology/
  42. ^ Etherington, D. (2014, Nov 6). "Amazon Echo Is A $199 Connected Speaker Packing An Always-On Siri-Style Assistant". TechCrunch. Retrieved from https://techcrunch.com/2014/11/06/amazon-echo/
  43. ^ Parkhurst, E (2015, Juni 25). "Amazon makes $100M available to fund voice-control tech". Retrieved from https://www.bizjournals.com/seattle/blog/techflash/2015/06/amazon-makes-100m-available-to-fund-for-voice.html
  44. ^ Adner, R., & Snow, D. (2010). Old technology responses to new technology threats: demand heterogeneity and technology retreats. Industrial and Corporate Change, 19(5), 1655-1675.
  45. ^ Spectrm, SINDRI (2018, May 5). Apple's road to chatbots. Retrieved October 7, 2018, from https://chatbotslife.com/apples-road-to-chatbots-85489cb7959e?gi=562e9e382af0
  46. ^ Apple. (n.d.). Apple Developer. SiriKit. Retrieved from https://developer.apple.com/sirikit/
  47. ^ Friedman, R., Kermarrec, A.-M. & Raynal, M. (2008). Modularity: a First Call Concept to Address Distributed Systems. ACM SIGACT News, 39 (2), 91-110.
  48. ^ Falala-Sechet, F. (2018, June 8). How a 100k+ Employee Company Launched an internal Workplace by Facebook Chatbot in 1 Week. Retrieved September 24, 2018, from https://medium.com/clevyio/how-a-100k-employee-company-launched-an-internal-workplace-by-facebook-chatbot-in-1-week-a1108a8865ce
  49. ^ O'Dea, S. (2017, July 7). Chatbots for work – how they could make a big impact inside organizations ‹ Poppulo. Retrieved September 24, 2018, from https://www.poppulo.com/blog/chatbots-for-employees-in-internal-communication/
  50. ^ O'Dea, S. (2017, July 7). Chatbots for work – how they could make a big impact inside organizations ‹ Poppulo. Retrieved September 24, 2018, from https://www.poppulo.com/blog/chatbots-for-employees-in-internal-communication/
  51. ^ Falala-Sechet, F. (2018, June 8). How a 100k+ Employee Company Launched an internal Workplace by Facebook Chatbot in 1 Week. Retrieved September 24, 2018, from https://medium.com/clevyio/how-a-100k-employee-company-launched-an-internal-workplace-by-facebook-chatbot-in-1-week-a1108a8865ce
  52. ^ Newman, D. (2016, May 24). Chatbots And The Future Of Conversation-Based Interfaces. Retrieved September 22, 2018, from https://www.forbes.com/consent/?toURL=https://www.forbes.com/sites/danielnewman/2016/05/24/chatbots-and-the-future-of-conversation-based-interfaces/
  53. ^ PwC (2017). Sizing the prize. What’s the real value of AI for your business and how canyou capitalise? Retrieved 04 October 2018 from http://preview.thenewsmarket.com/Previews/PWC/DocumentAssets/476830.pdf
  54. ^ PwC (2017). Sizing the prize. What’s the real value of AI for your business and how canyou capitalise? Retrieved 04 October 2018 from http://preview.thenewsmarket.com/Previews/PWC/DocumentAssets/476830.pdf
  55. ^ PwC (2017). Sizing the prize. What’s the real value of AI for your business and how canyou capitalise? Retrieved 04 October 2018 from http://preview.thenewsmarket.com/Previews/PWC/DocumentAssets/476830.pdf
  56. ^ Andersson, G., Cuijpers, P., Carlbring, P., Riper, H., & Hedman, E. (2014). Guided Internet‐based vs. face‐to‐face cognitive behavior therapy for psychiatric and somatic disorders: a systematic review and meta‐analysis. World Psychiatry, 13(3), 288-295.
  57. ^ Woebot. (n.d.). Woebot - Your charming robot friend who is here for you, 24/7. Retrieved October 2, 2018, from https://woebot.io/
  58. ^ Christensen, C., McDonald, R., Altman, E., & Palmer, J. (2018). Disruptive innovation: An intellectual history and directions for future research. Journal of Management Studies, (20180829). doi:10.1111/joms.12349
  59. ^ Wolf, M.J., Miller, K. & Grodzinsky, F.S. (2017). Why we should have seen that coming: Comments on microsoft's tay "experiment," and wider implications. Acm Sigcas Computers and Society, 47(3), 54-64. doi:10.1145/3144592.3144598
  60. ^ Galert, A. (2018, March 27). Chatbot 2018 — Is It My Competitor or Colleague? Retrieved September 22, 2018, from https://chatbotsmagazine.com/chatbot-2018-is-it-my-competitor-or-colleague-8e98ba8cd473?gi=126de97bd85b
  61. ^ Lee, J. (2018, May 6). Chatbots were the next big thing: what happened? Retrieved September 22, 2018, from https://blog.growthbot.org/chatbots-were-the-next-big-thing-what-happened
  62. ^ Schwartz, M. J. (2018, June 28). Ticketmaster Breach Traces to Embedded Chatbot Software. Retrieved September 22, 2018, from https://www.bankinfosecurity.com/ticketmaster-breach-traces-to-embedded-chatbot-software-a-11144
  63. ^ Adner, R., & Snow, D. (2010). Old technology responses to new technology threats: demand heterogeneity and technology retreats. Industrial and Corporate Change, 19(5), 1655-1675.
  64. ^ Khan, R. & Das, A. (2018). Build Better Chatbots. A Complete Guide to Getting Started with Chatbots. New York: Apress.
  65. ^ Khan, R. & Das, A. (2018). Build Better Chatbots. A Complete Guide to Getting Started with Chatbots. New York: Apress.
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