Month: March 2017

The “4H” Model for inferring information and knowledge culture from search technology artefacts

It is still a work-in-progress, however I have blended more elements of the ‘modality model for search’ into some of my recent thinking on how search technology artefacts could be used to infer aspects of information and knowledge culture.

information culture

A focus on search to check for information compliance of various aspects is termed ‘HOLD TO ACCOUNT‘ and will likely lead to a preponderance of dashboard metrics and reports of the information asset. A focus on social connections (between people and their information) termed ‘HARNESS‘ will likely yield a personalized approach (like popular social media sites) using search driven algorithms to show people what is going on in their network. This may lead to unexpected, insightful and valuable connections.

A focus on using what is known to exist termed ‘HARVEST‘ is likely to lead to the deployment of a corporate ‘Google like’ general purpose search engine. The focus is on Information Retrieval (IR). This is likely to infer aspects of Knowledge Management (KM) culture as this relates to exploiting information, rather than managing information which is arguably the focus of an Information Management (IM) culture. More extreme forms of harvesting may see domain specific search applications deployed, tuned for very specific work tasks and goals.

A focus on ‘what might exist’ or ‘what could be’ is termed ‘HUNT and HYPOTHESIZE’. This may likely lead to a focus on rich visual exploratory search interfaces of various media and analytics. The focus is on the search for patterns rather than just retrieving information. This is also likely to lead to unexpected, insightful and valuable information encounters.

Machine Learning techniques can be present at all parts of the model in some form and will likely be necessary within all quadrants as information volumes are too large for people to practically read. However, the sense-making of staff will be crucial as is information literacy in general. Noticing what is useful and valuable and generating new theories is never ‘in the data’. Enterprise search & discovery capability is likely to be a system of which technology is just one part.

Most organizations will contain the 4H’s to various degrees, however the presence or absence of certain technology artefacts or features within search applications, may be at odds with overall organizational culture.

There are a few other angles I am considering, its a work-in-progress!

Some references that shaped the thinking:

ARNOLD, S.E., 2014a. Redefining Search: Enterprise Search and Big Data. Information Today, June 2014, pp. 22-23.
CHOO, C.W., 2013. Information culture and organizational effectiveness. International Journal of Information Management, 33, pp. 775-779.
CURRY, A. and MOORE, C., 2003. Assessing information culture – an exploratory model. International Journal of Information Management, 23, pp. 91-110.
DAVIES, A., FIDLER, D. and GORBIS, M., 2011. Future Work Skills 2020. [online]. University of Phoenix Research Institute. Available from:
EASTWOOD, G., 2005. Enterprise Search tools move from luxury item to business essential as data builds up. [online]. Computerworld. Available from: [accessed January 2016].
GINMAN, M., 1987. Information culture and business performance. International Association of Technological University Libraries (IATUL) Quarterly, 2(2), pp. 93-106.
GRANT, S. and SCHYMIK, G., 2014. Using Work System Theory to Explain Enterprise Search Dissatisfaction. Proceedings of the Information Systems Educators Conference (ISECON). 6-9 November 2014: Baltimore, Maryland, USA, pp. 1-11.
GREFENSTETTE, G. and WILBER, L., 2011. Search-Based Applications: At the Confluence of Search and Database Technologies. In: MARCHIONINI, G., Ed. Synthesis Lectures on Information Concepts, Retrieval, and Services. USA: Morgan & Claypool Publishers.
HEILBRONER, R.L., 1967. Do Machines Make History? Technology and Culture, 8(3), pp. 335-345.
HILLIS, K., PETIT, M. and JARRETT, K., 2013. Google and the Culture of Search. UK: Routledge.
HOFSTEDE, G. et al., 1990. Measuring Organizational Cultures: A Qualitative and Quantitative Study across Twenty Cases. Administrative Science Quarterly, 35(2), pp. 286-316.
JACKSON, S., 2011. Organizational culture and information systems adoption: A three-perspective approach. Information and Organization, 21, pp. 57-83.
LEIDNER, D.E. and KAYWORTH, T., 2006. A review of culture in information systems research: Towards a Theory of Information Technology Culture Conflict. Management Information Systems (MIS) Quarterly, 30(2), pp. 357-399.
MARTIN, J., 2002. Organizational culture: Mapping the terrain. Thousand Oaks, CA, USA: Sage Publications.
MOLNAR, A., 2015. The 5 C’s of Enterprise Search. [online]. Available from:
PETTIGREW, A. M., 1979. On Studying Organizational Cultures. Administrative Science Quarterly, 24(4), pp. 570-581.
POSTMAN, N., 1993. Technopoly: The surrender of culture to technology. New York, USA: Vintage Books.
SCHEIN, E.H., 2004. Organizational Culture and Leadership. 3rd ed. San Francisco, USA: Jossey-Bass, pp. 3-23.
TURNER, F., 2006. From Counterculture to Cyberculture: Steward Brand, the Whole Earth Network and the rise of Digital Utopianism. Chicago, USA: University of Chicago Press.
VAN DER SPEK, R. and SPIJKERVET, A., 1997. Knowledge management: Dealing intelligently with knowledge. In: LIEBOWITZ, J. and WILCOX, L.C., Eds. Knowledge management and its integrative elements: Boca Raton, USA: CRC Press, pp. 31-59.
WATKINS, M., 2013. What is Organizational Culture? And Why Should we Care? [online]. Harvard Business Review (HBR). Available from:

Enterprise Search & Discovery engines: How we might come to ‘know’..

Delighted to be invited to give a seminar to staff and students yesterday (22nd March) at Edinburgh Napier University School for Computing and Centre for Social Informatics. Discussed how enterprises may wish to experiment with their search engines and user interfaces in an attempt to deliberately stimulate unexpected encounters which may lead to new knowledge creation. These opportunities may otherwise remain hidden using the ‘classic’ search box, ten blue links and some metadata driven refiners (faceted search) ordered by popularity or frequency of occurrence.


In the atrium at the university is a statue of John Napier, after which the university is named. He is best known for discovering logarithms.

PhD Success!

Delighted to share the news that after a successful defence of my thesis this week I have been awarded my PhD. A BIG thank you to everyone who has contributed, supported or followed my progress these past 4 years, it is very much appreciated. I am very excited at the collaborations and projects that are presenting themselves with a number of organizations and universities, so look forward to continuing the research on how enterprises can transform their capabilities to find & discover information. I will continue to blog post when I can.


Organizational Information Culture and Technology Artefacts

Organizational information culture can be defined as the behavioural norms and values shown by employees towards information. Researching enterprise search technology artefacts that represent aspects of the culture in which they are deployed, some organizations may not score highly in supporting an information culture that enables innovation and creativity. In addition to mapping information cultures through observation, surveys and interviews, there may be opportunities to infer aspects of culture through abductive reasoning based on the nature of technology artefacts (or absence of them). Its a work-in-progress, I’ve adapted Choo’s (2013) and Cameron and Quinn’s (2011) typology as applied to enterprise search.

Technology artefacts

I have kept Choo’s dimensions on the y-axis and four typology descriptions, adding my own text in red to describe the relationships to technology artefacts and x-axis related to enterprise search and discovery capability continuum (information ‘containers’ versus entities/concepts) building on the ‘modality model’. The presence or absence of technologies (or aspects of technology features) may imply certain information cultures (that have been inscribed into the technology).