Original Title: Towards Understanding Organizing Visions through Conceptual Blending: on the Example of Artificial Intelligence

Towards Understanding Organizing Visions through Conceptual Blending: on the Example of Artificial Intelligence

Type:
  • Master Thesis Business Information Systems
Status:
offered
Tutor:

Abstract

Motivation

In an organizing vision, defined as a “ […] focal community idea for the application of IT in organizations" (Swanson & Ramiller, 1997, p. 460), a communal discourse takes the center stage in understanding the adoption and diffusion of a novel IS phenomenon. Specifically, for a given IS notion an organizing vision essentially exists, as Swanson and Ramiller (1997) argue, to “talk the walk": a discourse, or “talk", in an inter-organizational community, aimed at an understanding and indeed legitimation of the IS phenomenon at hand (Ramiller & Swanson, 2003; Swanson & Ramiller, 1997; Wang & Ramiller, 2009).

However, as pointed out by Miranda et al. (2015), understanding how organization-level discourse informs community-level discourse remains underdeveloped, especially when it comes to the cognitive aspect - roughly speaking the “idea formation" reflected in the language of the discourse at hand. Specifically, if we consider an organizing vision as “[…] the product of ongoing community discussion" (Ramiller & Swanson, 2003, p. 16), which “[...] is developed by many different storytellers, who modify and embellish it to suit their own and their audiences' tastes and interests" (Swanson & Ramiller, 1997, p. 463), beyond the broad categories understanding, legitimation and mobilization, we lack an understanding how the discourse of individual storytellers (i.e., the organization-level discourse) can shape the organizing vision (i.e., the community-level discourse).

In this respect Conceptual Blending (CB) can play a notable role. CB, an approach from cognitive linguistics, builds on the basic notions of (i) conceptual mapping, whereby conceptual structure is projected from a source frame to a target frame, and (ii) framing devices, which are used in CB to integrate features into a novel blended space. Especially, given that many notions in Information Systems are inherently based on conceptual mapping (e.g., a “desktop” user interface borrows features from an everyday office environment, like “document” and “trashcan”), like done in de Kinderen et al (2020) it makes sense treat discourse around these concepts as if it is based on a selective projection and integration of conceptual structure from source concepts. In so doing, one has the potential to systematically analyze the core associations a community has with a given notion, as well as the frames the used to position the claimed capabilities of the notion at hand.

Objective

The objective of this master thesis is to use conceptual blending for analyzing the organizing vision of a particular notion in IS. Initially, the focus will be on the notion of ”Artificial Intelligence”, since (1) there exists already a considerable amount of AI discourse, (2) the AI discourse can be traced over time, thus one can nicely reconstruct the trajectory of the organizing vision’s career of AI. Of course, the analysis of another notion is also welcome. This can be discussed with the supervisor.

Einstiegsliteratur:

Swanson, E. B., & Ramiller, N. C. (1997). The organizing vision in information systems

innovation. Organization science, 8 (5), 458{474.

Ramiller, N. C., & Swanson, E. B. (2003). Organizing visions for information technology and

the information systems executive response. Journal of Management Information Systems,

20 (1), 13-50.

de Kinderen, S., Kaczmarek-Heß, M., & Nolte, M. (2020). Conceptual Blending for Analyzing Possible Interpretations of Information Systems Concepts-The Case of Smart Contracts. In ECIS.

Wang, P., & Ramiller, N. C. (2009). Community learning in information technology innovation. MIS Quarterly, 33 (4), 709{734.

Evans, V., Green, M. (2006). Cognitive linguistics –an introduction. Edinburgh University Press.

Sweetser, E. (2017a). Conceptual mappings. In The Cambridge Handbook of Cognitive Lin-

guistics (p. 377{490). Cambridge University Press.

Chilton, P. (2005, 01). Manipulation, memes and metaphors. In L. De Saussure & P. Schulz (Eds.), Manipulation and ideologies in the twentieth century: Discourse, language, mind (p. 15-43). Amsterdam and Philadelphia: J. Benjamins Publishing Company.

Nilsson, N. J. (2009). The quest for artificial intelligence. Cambridge University Press.

Multi-level Reference Modeling ---NISTIR 7628 (Master)