Dialogue-based knowledge transfer
Dialogue-based knowledge transfer: User acceptance of internal corporate chatbots
Master thesis by Paul Heß
3. Semester, 2024
Supervisor: Prof. Dr. Sebastian Pranz
Field of research
“We need our own Chatbot!”. This might be a statement that people working in different office jobs may have heard from their superior since the launch of ChatGPT in November 2022 and the hype that came with it (Angwin, 2024). But are corporate chatbots and their dialogue-form useful for every application? In this research this topic is looked into, especially regarding internal bots.
Together with robots and automation, having been a wish of humankind since long before chatbots, like with Leonardo Da Vinci’s robot construction plans (Mainzer, 2019, p. 7), managing knowledge has been relevant for humans at least since Egyptian hieroglyphs (Spender, 2015, p. 4). For examining the combination of these two topics in relation to internal corporate chatbots, some newer fields of research are relevant.
Organizational Theories by Max Weber or Herbert A. Simon, give insight into organizations with Weber describing organizations as a goal-oriented system (Weber, 1947, p. 95) and Simon referencing the need for communication and technologies transmitting it, to provide a basis for decisions (Simon, 2000, p. 208). In the field of knowledge management, the utilization of knowledge to achieve company goals is examined (Greiner et al., 2007, p. 4). Another relevant field is the one of internal communication, communicating to all employees (Welch & Jackson, 2007, p. 184) of an organization as part of knowledge management (O’Leary, 1998, p. 59). For this research, the channels, knowledge elements, goals & metrics and challenges of internal communication were examined. Lastly, research on the technology itself, chatbots can give insight into the research topic. From the first chatbot ELIZA (Bruns & Kowald, 2023, p. 6), to exploring a linguistic perspective of talking to computers (Lotze, 2018) and existing use-cases of corporate chatbots in HR (Kohne et al., 2020, p. 37) or IT (Fiore et al., 2020, p. 86). This research focused on rule-based chatbots (Hussain et al., 2019, p. 947), examining Artificial Intelligence in varying relevance.
Method
To provide a productive view, based on a literature review, expert interviews are conducted with internal communication experts. Also, users are confronted with internal corporate chatbot technology in comparison to the widely employed (Hudcova, 2014, p. 56) intranet. This is done in a laboratory quasi-experiment with a follow-up panel discussion.
The tasks of the quasi-experiment were conducted with prototypes, accessible at https://intranet.paulhess.de/.
Afterwards, the transcripts of the expert interviews and laboratory were transcribed and coded qualitatively.
As seen in Figure 2, the expert interviews, together with the literature provided the basis for the laboratory tasks and questions, also answering the research question 2, while the laboratory aimed to answer the research question 1.
Results
The expert perspective provided perspective on internal communication channels, knowledge elements, goals & challenges and potential and challenges for a chatbot. For the channels, no clear clustering was found but approaches for clustering in static/simple/lean vs. interactive/advanced/media-rich channels. Regarding knowledge elements, no clear clustering was found as well, bringing the need to assess every element on its own. The goals were mostly seen as informing employees or improving perception of the company, while challenges were the diversity, informing employees, managing topics or consulting stakeholders. Potential for chatbots was seen in managing knowledge, providing engagement and motivation. No potential was seen with networking. Challenges with chatbots were seen with output, efficiency, adoption, but not unanimous with GDPR or ethical problems.
Users also looked at knowledge elements as well as the experience, goals, fears or concerns as well as future potential. Knowledge elements fitting for bots were seen by the user as documents or multimedia elements. Booking tools were seen mixed, while news and social elements were seen better fitting with the intranet. Regarding the user experience, potential for bots were seen with giving the right information and speed. Overview was seen better in the intranet, and humour was not wished for. In achieving the internal communication goals, the goal of informing employees was seen fitting for bots with users. Motivation was seen working better on the intranet. Users mostly had fears regarding functionality, while privacy or GDPR was seen ambivalent. Ethical fears were not voiced. Future potential was seen, especially with AI powered chatbots.
Conclusion
As a basis for the conclusion, it is seen that due to the differentiation into static and complex / one-sided and two-sided channels, chatbots are eligible to be compared to the intranet due to both being complex, two-sided channels. Knowledge elements however must be assessed on their own while goals and challenges can be a measurement to assess a chatbots usefulness.
Assessing the potential for dialogue-based knowledge transfer with the example of internal corporate chatbots, it is seen that documents are seen most fitting for bots, with multimedia elements, people and external links. Agreement is also seen that news and social elements work better on other channels. Positive assessment is seen for bots with handling knowledge and information as well as their speed. The prerequisite for accepting a bot is, that it has to work while there is no clear position on ethical or privacy concerns. Limits are seen with providing overview or humour. Also, an onboarding is needed for the bots. A connection with Artificial intelligence is seen as fitting as well.
From this, the following hypothesis can be derived:
Employees feel more informed when the internal communication uses chatbots compared to when they do not.
Internal corporate chatbots improve the productivity of companies compared to when internal communication uses no chatbots.
Users expect artificial intelligence to be implemented when talking to chatbots.
These could be further explored in the future.
Overall, potential for Dialogue-based knowledge transfer of internal corporate chatbots is seen.
Sources
Angwin, J. (2024, May 15). Press Pause on the Silicon Valley Hype Machine. New York Times. https://www.nytimes.com/2024/05/15/opinion/artificial-intelligence-ai openai-chatgpt-overrated-hype.html
Bruns, B., & Kowald, C. (2023). Praxisleitfaden Chatbots. Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-39645-9
Fiore, D., Thiel, C., & Baldauf, M. (2020). Potenziale von Chatbots für den innerbetrieblichen IT-Support. HMD Praxis Der Wirtschaftsinformatik, 57(1), 77–88. https://doi.org/10.1365/s40702-019-00578-7
Greiner, M. E., Böhmann, T., & Krcmar, H. (2007). A strategy for knowledge management. Journal of Knowledge Management, 11(6), 3–15. https://doi.org/10.1108/13673270710832127
Hudcova, S. (2014). Tools of Internal Communication from Knowledge Transfer Perspective. Journal of Competitiveness, 6(4), 50–62. https://doi.org/10.7441/joc.2014.04.04
Hussain, S., Ameri Sianaki, O., & Ababneh, N. (2019). A Survey on Conversational Agents/Chatbots Classification and Design Techniques. In L. Barolli, M. Takizawa, F. Xhafa, & T. Enokido (Eds.), Advances in Intelligent Systems and Computing. Web, Artificial Intelligence and Network Applications: Proceedings of the Workshops of the 33rd International Conference on Advanced Information Networking and Applications (WAINA-2019) (Vol. 927, pp. 946–956). Springer Cham. https://doi.org/10.1007/978-3-030-15035-8_93
Kohne, A., Kleinmanns, P., Rolf, C., & Beck, M. (2020). Chatbots. Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-28849-5
Lotze, N. (2018). Zur sprachlichen Interaktion mit Chatbots – Eine linguistische Perspektive. Innsbruck University Press. https://doi.org/10.25969/MEDIAREP/19874
Mainzer, K. (2019). Künstliche Intelligenz – Wann übernehmen die Maschinen? Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-58046-2
O'Leary, D. E. (1998). Enterprise knowledge management. Computer, 31(3), 54–61. https://doi.org/10.1109/2.660190
Simon, H. A. (2000). Administrative behavior: A study of decision-making processes in administrative organizations (4. ed.). The Free Press.
Spender, J. C. (2015). Knowledge Management: Origins, History, and Development. In E. Bolisani & M. Handzic (Eds.), Knowledge Management and Organizational Learning. Advances in Knowledge Management (Vol. 1, pp. 3–23). Springer International Publishing. doi.org/10.1007/978-3-319-09501-1_1
Weber, M. (1947). The Theory of Social and Economic Organization. The Free Press of Glencoe. 68 https://search.alexanderstreet.com/view/work/bibliographic_entity%7Cbibliographic_details%7C4708644
Welch, M., & Jackson, P. R. (2007). Rethinking internal communication: a stakeholder approach. Corporate Communications: An International Journal, 12(2), 177–198. https://doi.org/10.1108/13563280710744847