PhD study in 2012-2015 focused on how managerial mental models affected the use of new digital technologies with focus on social media. Back then, I came across a number of articles describing the use of social media, i.e. the use of different platforms (e.g. Oestreicher-Singer & Zalmanson 2013), in different contexts by different people (e.g. Kozinets et al. 2010), in different ways (e.g. Van Iddekinge & Thatcher 2013), and for different reasons (e.g. Seraj 2013). These studies showed how social media affect the way we live, work, and interact in the digital age. At that time, only a few studies (e.g. Fischer & Reuber 2011) investigated the ways social media platforms affect our thinking, however with focus on the single platforms.
Relating this to my present studies on human-AI engagement, we see a specific development taking place with AI, namely the growth of AI tools based on relational, visual, and creative thinking. May the use of such tools trigger a more varied thinking repertoire for human co-creators? Perhaps the wholegrain model can shed light on this question.
A New Whole Brain Thinknology?
So what do I mean with ‘whole brain thinking’? Back in the antique Greece, Hippocrates discovered that people had distinct preferences in their approaches to problem solving and explained this through the dual brain. Up till the 20the century a detailed knowledge of the complexity and specialized functions of our brain was unfolded by scientists as Roger W, Sperry, Paul MacLean, Joseph Bogen and Michael Gazzanaga still clinging to the concepts of the dual or triune brain. However, this often populist notion is outdated, as Stansbury (2014) explains, “Although the left-brain/right-brain distinction has persisted in popular culture, there is little evidence to suggest that individual differences in cognitive processing can be linked to anatomical differences in the two hemispheres of the brain. A recent study (Overton et al., 2025) indicates that brains work as a distributed interactive ecosystem, which underscores the relevance of understanding thinking as the distributed nature of neural activity underlying choices in the human brain.
However, the notion of the dual brain may still be useful as a metaphor for different thinking preferences. Moreover, the simple description does not capture radically different ways of thinking (Leonard and Straus 1997: 113). According to Leonard and Straus (1997), one of the paradoxes of modern management is that in the midst of technical and social change, so pervasive and rapid that it seems out of pace with the rhythms of nature, human personality has not altered throughout recorded history.
A Brain Metaphor
In 2010, I was associate professor in Management at the Technical University of Denmark. Here, I became certified in administrating and interpreting the HBDI concept. In contrast to other psychological personality identification concepts, this method did not look specifically at who you are, but how you prefer to learn, communicate, and make decisions.
My first publication in Journal of Interactive Marketing showed how individual and collective mental models are closely liked with managers social media preferences. So what? If our preferences steers our choice and use of new technologies in an unreflected way, we might not learn much from using them. That is why it can be more constructive to understand our preferences and expand our way of thinking by using new technologies in a way that challenges our existing preferences. Unlike personalities, preferences are not rigid; most people can draw on a mixture of approaches and do not live their lives within cognitive boundaries (Leonard and Straus (1997: 112). Preferences are neither inherently good nor bad, but their assets and liabilities depend on the situation (Leonard and Straus 1997:113).
Ned Herrmann, physician, musician, and artist, developed and instrumentalized the whole brain metaphor. He worked as manager at General Electrics (1976) and wondered how our cognitive styles/preferences shape the leadership styles and communication patterns. In this context he developed the HBDI model (Hermann Brain Dominance Instrument) as a frame of reference to expand thinking preferences and create learning that synchronized with the learners’ preferred learning modes:
”Preferred ways of knowing are crucial factors to take into account in management education or any teaching education because they filter out certain data and allow other information through. For people whose preferred mode of knowing is visual, what is presented in pictures will get through to them better than a lecture or a book with text only. Thus, preferred modes of knowing shape our perceptions of the world around us and incline us to think about our experience in specific ways”.
By splitting ‘the brain’ into four quadrants, it is possible to identify the dominant preferences – or cognitive styles. The left-brain hemisphere represents logical, sequential, factual, and analytical thinking whereas the right-brain hemisphere represents non-linear, intuitive, value-based, and holistic thinking. Left hemisphere thinks in numbers and letters as opposed to the right hemisphere that thinks in images and symbols. Please note that despite the neuro-biological references, the model is a metaphor and not a depiction of the brain.
HDBI: AI and Social Media
Artificial Intelligence and Social Media are defined by human traits (intelligent and social), but are rather oxymorons as technology are not human (not to mention the anti-social effects of tech use).
If we apply the four quadrants to Artificial Intelligence, its function draws on the left hemisphere, but displays characteristics of the right hemisphere to mimic human traits. For Social Media, the platforms organize (lower left quadrant) interpersonal interaction (lower right quadrant) and the upper right quadrant (integrating) whereas ‘media’ associate to the process (lower left quadrant) of conveying content (upper left quadrant). Moreover, most these technologies contain text and image functions. Texting appeals to the left-sided functions whereas images (including videos) appeal to right side of the model.
In that sense, AI and social media use carries the potential to expand our thinking preferences. As mentioned, the shift from an economy and society build on logical, linear, and computer-like capabilities of the information age to an economy and society built on the inventive, empathic, big-picture capabilities of the conceptual age may even be influenced by of image-based technologies.
Just take a look at our schools and work places. 20 years ago, text-based materials such as books and reports were considered predominantly the ‘right’ source of information. Today, many lectures are image-based and students allowed to produce videos and image materials instead of text-based essays and reports. AI is used to support digital learning environments and when students or conference participants are allowed to tweet questions, comments, and pictures during a session, it indicates new forms of human interaction and tech use.
Now, let’s assume that people have specific approaches to perceiving data (text/images), making decisions and relating to other people. Peter, who prefers to gather, absorb, and process information on his own tend to evaluate evidence and make decisions in structured, logical processes. Liza, on the other hand, usually prefers to work together with other people. She relies on values and emotions to guide her on her decisions. Using social media for assessing where to go on holiday, the former would probably prefer to visit Wikipedia whereas the latter will tweet her friends or check out on Yelp.
People’s work experience reinforces the original preferences and deepens associated skills (Leonard and Straus 1997: 113). This may rub off on the way we approach AI or social media at work. Moreover, if we go along with the premise that biased and categorical thinking hinders learning and innovation, the managerial challenge is to use the insights of the whole brain approach to create new processes and encourage new behaviors that will help innovation efforts succeed (Leonard and Straus 1997:116). Expanding the variety of AI tools and platforms to a whole-brained portfolio in the company may both lead to development of managers’ and employees’ thinking preferences, but also expand the modes of external interaction to target a broader range of stakeholders.
I simply posit that we can learn to expand our repertoire of behaviors and act outside preferred styles (thinking out of the box) by using AI and social media in such considered way. Research suggests that style flexibility – being able to select among styles, monitor their effectiveness, and switch styles if necessary – may actually be more important than style rigidity (Stansbury 2014). If not appreciated, or understood, cognitive preferences tend to personalize conflict or avoid it (Leonard and Straus 1997: 121). A positive effect of a whole-brain approach is that we learn to appreciate the differences as each approach brings a unique valuable perspective to the process of learning and innovation.
References:
- Fischer, Eileen and A. Rebecca Reuber (2011) Social interaction via new social media: (How) can interactions on Twitter affect effectual thinking and behavior? Journal of Business Venturing, 26(1), pp. 1-18.
- Herrmann, Ned (1988) The Creative Brain , North Carolina, Brain Books.
- Herrmann, Ned (1996) The Brain Business Book, McGraw-Hill.
- Herrmann, Ned (1991), The Creative brain, The Journal of Creative Behavior, 25(4) pp. 275–295.
- Kozinets, Robert V; de Valck, Kristine; Wojnicki, Andrea C; Wilner, Sarah J.S. (2010) Networked Narratives: Understanding Word-of-Mouth Marketing in Online Communities. Journal of Marketing 74(2), p. 71-89.
- Leonard, Dorothy and Straus, Susaan (1997), Putting Your Company’s Whole Brain to Work, Harvard Business Review, July-August.
- Oestreicher-Singer, Gal and Lior Zalmanson (2013), Content or Community? A digital Business Strategy for Content Providers in the Social Age, MIS Quarterly 37(2), 591-616.
- Overton, J.A Moxon, K.A., Stickle, M.P., Peters, L.M., Lin, J.J., Chang, E.F., Knight, R.T., Hsu, M., Saez, I. (2025). Distributed Intracranial Activity Underlying Human Decision-making Behavior, Journal of Neuroscience 45 (15). DOI: 10.1523/JNEUROSCI.0572-24.2024
- Rydén, P. Ringberg, T. and Wilke, R. (2015). How Managers’ Shared Mental models of Business-Customer Interaction Influence Managers’ Sensemaking of Social Media, , Journal of Interactive Marketing, 31, 1-16.
- Rydén, P., Ringberg, T, El Sawy, O. A. (2023). The Leader’s Strategic Mindset: A Key Factor for AI Success, Management and Business Review Special Issue, 3(1-2), 54-63.
- Stansbury, Merry (2014) Groundbreaking: We can predict cognitive styles, and here’s how. http://www.eschoolnews.com/2014/04/23/cognitive-styles-learning-670/2/
- Van Iddekinge, Chad H. and Thatcher, Jason B. (2013), Social Media in Employee-Selection-Related Decisions A Research Agenda for Uncharted Territory, Journal of Management, Oct. (in press).
Links:
http://www.hbdi.com/WholeBrainProductsAndServices/programs/thehbdi.php
