The Creation of Chatbots at Work: An Organizational Perspective
The Creation of Chatbots at Work: An Organizational Perspective
Extended Abstract submitted to AI@Work 2020,
Lorentsa Gkinko, PhD Candidate, School of Business and Management, Royal Holloway University of London, UK
Dr Amany Elbanna, Reader in Information Systems, School of Business and Management, Royal Holloway University of London, UK
A recent report from Gartner suggests that one in five workers set to have a machine as their ‘co-worker’ by 2022 (Gartner, 2017). Latterly, advances in Natural Language Processing and Machine Learning have facilitated an increase of chatbots in several domains and the establishment of partnerships in which computers and humans collaborate to augment each other’s activities. They enhance collaboration and influence changes in the workplace (Wessel et al., 2018). However, the changes they introduce affect how people work and might raise challenges that need to be conquered. This paper reports on a case study of the use of a chatbot in the workplace. It aims to understand employees’ perception of chatbots at work and the organizational effort to improve its operations.
Artificial Intelligence (AI) is a broad term that accommodates the endeavour of humans to understand and create intelligent agents. AI refers to the ability of machines to understand, think and learn in a similar way to human beings, representing the opportunity of using computers to simulate human intelligence (Mat Rahim, Mohamad, Abu Bakar, Mohsin, & Md Isa, 2018). AI is a widespread field with techniques being applied in many industries. The adoption of AI may have profound effects on the workplace, value creation, and competitive advantage. In the modern era of technology, “chatbots” or “virtual assistants” or “bots” are commonly used AI applications that interact with the users in a conversational level. The term ‘chatbot’ refers to any software application that participates in a dialog with a human using Natural Language (Dale, 2019).
Chatbots are usually used in dialog systems for various practical purposes including customer service or information acquisition (Vandana et al., 2019). Some chatbot examples include the IBM Watson, Microsoft bot, AWS Lambda, Heroku and many others (Rahman, Al Mamun, & Islam, 2018). Chatbot applications have been around for a long time. One of the earliest Natural Language Processing (NLP) applications, Joseph Weizenbaum’s Eliza, was a chatbot. Eliza was developed in the early 1960s to emulate the conversational style of a nondirectional psychotherapist (Dale, 2019). A chatbot uses Machine Learning and Natural Language Processing to answer questions and make recommendations.
A chatbot is often described as one of the most advanced and promising expressions of interaction between humans and machines (Vandana et al., 2019). Chatbot technology has evolved over time, nevertheless the intention and added value that chatbots offer has not been clearly defined. In order to design a chatbot that provides a meaningful experience, we must firstly understand what expectations people have for this technology, and what opportunities there are for chatbots based on user needs. The functionality of these chatbots ranges from utilitarian to entertainment, but the value is often not clearly defined (Zamora, 2017).
The current attention in chatbots is stimulated by recent developments in Artificial Intelligence and Machine Learning. Designing a new interactive technology such as a chatbot requires in-depth knowledge of users’ motivations for using the technology, which allows the designer to overcome challenges regarding the adoption of the technology (Brandtzaeg & Følstad, 2017). The increasing use of chatbots is expanding the digitalization of work processes (Yoo, Boland, Lyytinen, & Majchrzak, 2012) throughout organizations. As a result, the influences of integrating such digital innovations into the workplace are important areas of inquiry (Barrett, Oborn, Orlikowski, & Yates, 2012). However, we currently know little about how chatbots are being developed and integrated in work practices.
This paper explores a case study of developing a chatbot in a large European Bank. It questions how the chatbot is developed and integrated in work practices. Qualitative research methods including interviews, participant observation and documents review are adopted. The research findings show that for the chatbots to be integrated in the organization, different changes in the features of the chatbot design are incorporated and different organizational changes take place. Together, these changes bring about new work practices where the chatbot becomes a member of the organization. We adopt the theoretical lens of institutional work to explain the effort of designing the chatbot and establish it as a new member of the organization. An institutional work perspective concentrates more on practice and process than on outcome (Lawrence, Suddaby, & Leca, 2011). The concept of institutional work emphasizes the understanding of how intentional actions affect institutions (Lawrence, Suddaby, & Leca, 2009).
Barrett, M., Oborn, E., Orlikowski, W. J., & Yates, J. (2012). Reconfiguring Boundary Relations: Robotic Innovations in Pharmacy Work. Organization Science, 23(5), 1448–1466.
Brandtzaeg, P. B., & Følstad, A. (2017). Why people use chatbots. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10673 LNCS, 377–392.
Dale, R. (2019). Industry Watch The return of the chatbots. Natural Language Engineering, 22(5), 811–817.
Gartner. (2017). Gartner Says By 2020, Artificial Intelligence Will Create More Jobs Than It Eliminates. Retrieved October 16, 2019, from Press Release website: https://www.gartner.com/en/newsroom/press-releases/2017-12-13-gartner-says-by-2020-artificial-intelligence-will-create-more-jobs-than-it-eliminates
Lawrence, T. B., Suddaby, R., & Leca, B. (2009). Institutional work: Actors and agency in institutional studies of organizations. Cambridge University Press.
Lawrence, T., Suddaby, R., & Leca, B. (2011). Institutional work: Refocusing institutional studies of organization. Journal of Management Inquiry, 20(1), 52–58.
Mat Rahim, S. R., Mohamad, Z. Z., Abu Bakar, J., Mohsin, F. H., & Md Isa, N. (2018). Artificial Intelligence, Smart Contract and Islamic Finance. Asian Social Science, 14(2), 145.
Rahman, A. M., Al Mamun, A., & Islam, A. (2018). Programming challenges of chatbot: Current and future prospective. 5th IEEE Region 10 Humanitarian Technology Conference 2017, R10-HTC 2017, 2018–Janua, 75–78. IEEE.
Vandana, A., Rani, J., Goud, V. A., Kishor, M. C., Reddy, K., & Murthy, R. (2019). A study of chatbots through artificial intelligence. Journal of Applied Science and Computational Science, VI(I), 341–354.
Wessel, M., de Souza, B. M., Steinmacher, I., Wiese, I. S., Polato, I., Chaves, A. P., & Gerosa, M. A. (2018). The Power of Bots. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 1–19.
Yoo, Y., Boland, R. J., Lyytinen, K., & Majchrzak, A. (2012). Organizing for innovation in the digitized world. Organization Science, 23(5), 1398–1408.
Zamora, J. (2017). I’m Sorry, Dave, I’m afraid I can’t do that: Chatbot perception and expectations. HAI 2017 – Proceedings of the 5th International Conference on Human Agent Interaction, 253–260.