Tweeting Resistance: The Evolution of Engagement Frameworks

Hallward, Maia Carter; Armstrong, Crystal

doi:10.1108/s0163-786x20160000040008

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…Also, research work has been carried out to explore if one’s actions are influenced by online interactions, and whether such manifestation and influences can be modelled and predicted (Ramanathan and Purani, 2014; Helvie-Mason, 2011; Hua and Haughton, 2009). There have been several approaches proposed and experimented including, network analysis of social network graphs (Coons and Chen, 2014; Jay, 2005), user perception and awareness related to online privacy (Raman and Pamod, 2015), analysis of unstructured data (Seng and Yang, 2017; Zhang et al , 2016; Xue and Deng, 2012), analysis of tweets (Ozturkcan et al , 2017; Hallward and Armstrong, 2016; Haustein et al , 2014), likes and posts on social media (Khobzi and Teimourpour, 2014; Chauhan and Pillai, 2013; Su et al , 2015) and their correlation to the real-time behaviour of people.…”

Section: Introductionmentioning

confidence: 99%

Predict employee attrition by using predictive analytics

Raman

Bhattacharya

Pramod

2018

BIJ

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Purpose Research questions that this paper attempts to answer are – do the features in general email communication have any significance to a teaching faculty member leaving the business school? Do the sentiments expressed in email communication have any significance to a teaching faculty member leaving the business school? Do the stages mentioned in the transtheoretical model have any relevance to the email behaviour of an individual when he or she goes through the decision process leading to the decision to quit? The purpose of this paper is to study email patterns and use predictive analytics to correlate with the real-world situation of leaving the business school. Design/methodology/approach The email repository (2010–2017) of 126 teaching faculty members who were associated with a business school as full-time faculty members is the data set that was used for the research. Of the 126 teaching faculty members, 42 had left the business school during this time frame. Correlation analysis, word count analysis and sentiment analysis were executed using “R” programming, and sentiment “R” package was used to understand the sentiment and its association in leaving the business school. From the email repository, a rich feature set of data was extracted for correlation analysis to discover the features which had strong correlation with the faculty member leaving the business school. The research also used data-logging tools to extract aggregated statistics for word frequency counts and sentiment features. Findings Those faculty members who decide to leave are involved more in external communication and less in internal communications. Also, those who decide to leave initiate fewer email conversations and opt to forward emails to colleagues. Correlation analysis shows that negative sentiment goes down, as faculty members leave the organisation and this is in contrary to the existing review of literature. The research also shows that the triggering point or the intention to leave is positively correlated to the downward swing of the emotional valence (positive sentiment). A number of email features have shown change in patterns which are correlated to a faculty member quitting the business school. Research limitations/implications Faculty members of only one business school have been considered and this is primary due to cost, privacy and complexities involved in procuring and handling the data. Also, the reasons for exhibiting the sentiments and their root cause have not been studied. Also the designation, roles and responsibilities of faculty members have not been taken into consideration. Practical implications Business schools all over India always have a challenge to recruit good faculty members who can take up research activities, teach and also shoulder administrative responsibilities. Retaining faculty members and keeping attrition levels low will help business schools to maintain the standards of excellence that they aspire. This research is immensely useful for business school, which can use email analytics in predicting the intention of the faculty members leaving their business school. Originality/value Although past studies have studied attrition, this study uses predictive analytics and maps it to the intention to quit. This study helps business schools to predict the chance of faculty members leaving the business school which is of immense value, as appropriate measures can be taken to retain and restrict attrition.

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