Purpose
The purpose of this paper is to show that virtual mirroring-based learning allows members of an organization to see how they communicate with others in a visual way, by applying principles of “social quantum physics” (empathy, entanglement, reflect, reboot), to become better communicators and build a shared “DNA” within their organization.
Design/methodology/approach
E-mail based social network analysis creates virtual maps of communication – social landscapes – of organizations, similar to Google Maps, which creates geographical maps of a person’s surroundings.
Findings
Applying virtual mirroring-based learning at various mulitnational firms has significantly increased their organizational efficiency and performance, for instance increasing customer satisfaction by 18 per cent in a large services organization, increasing retention, making sales forecasts, and improving call center employee satisfaction.
Research limitations/implications
To address concerns of individual privacy, the guiding principle is to give individual information to the individual and provide aggregated anonymized information to management.
Originality/value
Virtual mirroring-based learning offers a unique way of creating collective awareness within an organization by empowering the individual to take corrective action aligned with collective action, and improves their own communication behavior through analyzing and visualizing their e-mail archive in novel ways, while giving strategic insight to management and improving organizational culture.
For centuries “innovation” has been a topic of book authors and academic researchers as documented by Ngram and Google Scholar search results. In contrast, “innovators” have had substantially less attention in both the popular domain and the academic domain. The purpose of this paper is to introduce a text analysis research methodology to linguistically identify “innovators” and “non-innovators” using Hebert F. Crovitz’s 42 relational words. Specifically, we demonstrate how to combine the use of two complementary text analysis software programs: Linguistic Inquiry and Word Count and WORDij to simply count the percent of use of these relational words and determine the statistical difference in use between “innovators” and “non-innovators.” We call this the “Crovitz Innovator Identification Method” in honor of Herbert F. Crovitz, who envisioned the possibility of using a small group of 42 words to signal “innovation” language. The Crovitz Innovator Identification Method is inexpensive, fast, scalable, and ready to be applied by others using this example as their guide. Nevertheless, this method does not confirm the viability of any innovation being created, used or implemented; it simply detects how a person’s language signals innovative thinking. We invite other scholars to join us in this linguistic sleuthing for innovators.
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