Language Processing as Organizational Cognition

Introduction

Language is arguably one of the most fascinating features of the human brain and a key topic in cognitive psychology. The cognitive processes making up the foundations of human language processing are key to the uniquely human capability for labor division, task distribution and sensemaking in organizations. Leadership, work motivation, planning and organizational learning are all dependent on language processing. Increasingly, computerized text algorithms are available to sample, analyze and map language usage in organizations. The interface between neuropsychological processes and natural language processing is already becoming the next frontier in organizational psychology. In earlier years, statistical modelling for psychological theory building and managerial applications have needed to rely on rating scales and other mapping techniques that come in addition to the linguistic interaction in organization. The topic of this course is to introduce students to modern text modeling approaches that render more direct access to cognitive and emotional psychological information. As an aspect of this, the course also introduces, explains and applies text algorithmic tools like intelligent chatbots and other software that mimics human cognition. It is important that organizational psychologists understand how uniquely human language and cognition is related to the growing use of machine-based technologies in the workplace.

This course takes the students directly to the questions of how the core topics of organizational behavior such as leadership, motivation, personality, learning and organized sensemaking can be understood and in conjunction with neurobiology and natural language processing. The course brings together foundational theory in these fields with easily accessible tools such as the chatbot ChatGPT, websites for text analysis and packages for data analysis in programming languages such as R and Python. The course focuses on how data used in organizational decision making can now be obtained through the direct processing of existing text materials instead of going methodological detours like collecting survey data with Likert scales.

The rapid advancement of these techniques has raised a number of issues from the role of human-machine interaction in advanced work, through the role of text algorithms in learning and education to questions about scientific philosophy in generating theories with predictive value in social science. The applications span increasingly wide domains in management and need to be addressed from an organizational psychological perspective, because they change work processes in the interface between humans and machines.

Examples of such organizational domains are:

1. Automated mapping of organizational constructs such as motivation, leadership, engagement etc. that so far have only been available through survey research data.
2. Personality profiling of applicants and others through free text.
3. Understanding sensemaking in organizations through topic identification and extraction from texts.
4. Understanding how human cognitive psychology can be enhanced and challenged by cognitive automatization such as Chatbot interfaces, for example between students, professors, customers, employees, and managers.
5. Sentiment analyses assessing the attitudes of customers, employees and other stakeholders towards products, organizations and departments.
6. Sustainability issues involved, such as decision making, diversity and technology acceptance and literacy in the workplace.
7. Ethical issues involved in digital constructions of social realities, such as biases, privacy and power distribution.

This development of low-cost cognitive tools is changing the way we understand language as maps of organizations and emphasizes the need to understand the psychology of language. The three most important theoretical underpinnings are: 1) Modeling of human cognition in the interface between neuropsychology and the digital technologies for natural language processing (e.g., Landauer), 2) Sensemaking in organizations as a way of understanding continuous organizing as an ongoing language game (e.g., Weick), and 3) Social constructionism as a foundation for how our social realities are constituted and upheld by linguistic practices (e.g., Berger & Luckmann).

The aim of this course is to unify these three strains of organizational psychology with state-of-the-art computational tools in text analysis and management. Given the rapid advancement of technologies, the course is also building on contributions from complex adaptive systems theory. The course addresses specifically the psychological interface between human cognitive psychology, computational tools and organizational theory and practice. It does not cover organizational communication or strategic aspects of digitization, nor does it give the students any detailed instruction in writing computer code.

Course content

This course starts out with a direct introduction to some up-to-date applications of algorithmic text analysis, such as ChatGPT and other open source tools and research findings. The course then takes the students along a path to understand how and why these technologies are possible, keeping a focus on their possible uses and limitations. The course is a continuous mixture of lectures, discussions and student exercises.

The progress of the course contents is structured as follows:

1) Organizations as talking machines: ChatGPT as a window to organized work, and understanding language as a map of shared human realities.

2) Psycholinguistcs: What is actually human, natural language and how does the brain interact with culture to produce it?

3) Information theory: Introduction to the physical rules that unify brains, language and computers.

4) The genuinely human side to language: Pragmatics of human communication, reasoning theory and why natural language works better for lawyers than for scientists, including biases and power.

5) The role of language for change and stability in organizations: social construction, sensemaking, change poetry and the multi-lingual work environment with implications for diversity.

6) Digital language processing: Topics, wordclouds, sentiments and semantic analyses. Easy access to text analysis and the possible advanced options.

7) Statistical semantics: Modelling constructs such as leadership, motivation and personality. Predicting human behavior using available algorithmic tools. Constructs as intersection of social practice and information theory.

8) A touring of contemporary text analysis in organizations: Updated applications from the corporate landscape.