Author
Listed:
- Kristin O’Brien
(Voop.Global, Melbourne, Australia)
- Suresh Sood
(University of Technology, Sydney, Australia)
- Rohan Shete
(Voop.Global, Melbourne, Australia)
Abstract
This paper explores the use of big data to measure company culture, good and bad, including toxic culture. Culture is a central factor driving employee experiences and contributing to the “great resignation”. Harnessing the key Artificial Intelligence (AI) technology of neural networks using deep learning methodology for NLP provides the capability to extract cultural meanings from a diverse array of organizational information and cultural artefacts ( texts, images, speech and video) available online. Using big data and AI provides a predictive capability surpassing the value of employee survey instruments of the last century providing a rear view of insights. Big data helps break free from the paradigm of only thinking about culture moving at a glacial pace. An innovative methodology and AI technologies help measure and visually plot the organizational culture trajectory within a company cultural landscape. Cultural values, inclusive of toxicity, have the potential for detection across all forms of communications media. A non-invasive approach using a broad range of open data sources overcomes limitations of the traditional survey instruments and approaches for achieving a culture read. The benefits of the approach and the AI technology are the real-time ingestion of ongoing executive and managerial feedback while entirely sidestepping the issues of survey biases and viable samples. The methodology under study for reading a culture moves well beyond traditional text-centric searches, content analyses, dictionaries and text mining, delivering an understanding of the meanings of words, phrases, sentences or even concepts comprising company culture. Embeddings are an ideal neural network breakthrough technology enabling the computation of text as data through creating a meaningful space in which similar word meanings exist in close proximity. Vector algebra in a multidimensional space helps unpack the cultural nuances and biases pent up within the unstructured information flowing through and from organizations, from tweets to text-centric corporate communications, including annual reports. This modelling enables predictions about an organizational future culture based on communication data existing across internal and external digital platforms. The variety of communications represents the twenty-first century culture requiring exploration and discovery. Visualizations of the traces of multiple dimensional cultures make current-state and culture predictions for an organization and competitive organizations in the same or adjacent industries within a company cultural landscape.
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