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Article
Publication date: 31 December 2018

Ramakrishnan Raman, Sandeep Bhattacharya and Dhanya Pramod

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…

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Abstract

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.

Details

Benchmarking: An International Journal, vol. 26 no. 1
Type: Research Article
ISSN: 1463-5771

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Article
Publication date: 12 August 2021

Dhanya Pramod

This study intends to find the industries that have leveraged Robotic Process Automation (RPA) technology and elucidate the extent of the adoption of RPA in various…

Abstract

Purpose

This study intends to find the industries that have leveraged Robotic Process Automation (RPA) technology and elucidate the extent of the adoption of RPA in various industry domains with benefits. The identification of tasks eligible for RPA itself is a challenge. Therefore, the study further brings out the challenges faced in various industry verticals and postulates the future direction of research and applications in RPA.

Design/methodology/approach

The study focuses on articles from popular databases such as SCOPUS, Web of Science and Google scholar. PRISMA methodology is used for systematic literature review and 113 papers are shortlisted for study. Three questions are framed to carry out the review and set the research direction.

Findings

It is evident from this study that RPA has been widely used in banking and related areas with moderate use in healthcare and manufacturing leading to operational efficiency and productivity. However, there are a lot more opportunities in other domains that need to be taped by leveraging technology advancements and a research agenda has been devised by postulating future directions.

Originality/value

The study brings out a new comprehensive perspective as regards RPA implementation across domains. There is no promising study found that gathers three-dimensional aspects of the meta-themes applications, benefits and challenges. The study summarizes the research agenda and projects the industry domains that have not yet explored, the benefits of RPA. This will be a good reference article for those who develop RPA techniques and organizations that have plans to go for RPA.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

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Article
Publication date: 19 June 2019

Prafulla Bafna, Dhanya Pramod, Shailaja Shrwaikar and Atiya Hassan

Document management is growing in importance proportionate to the growth of unstructured data, and its applications are increasing from process benchmarking to customer…

Abstract

Purpose

Document management is growing in importance proportionate to the growth of unstructured data, and its applications are increasing from process benchmarking to customer relationship management and so on. The purpose of this paper is to improve important components of document management that is keyword extraction and document clustering. It is achieved through knowledge extraction by updating the phrase document matrix. The objective is to manage documents by extending the phrase document matrix and achieve refined clusters. The study achieves consistency in cluster quality in spite of the increasing size of data set. Domain independence of the proposed method is tested and compared with other methods.

Design/methodology/approach

In this paper, a synset-based phrase document matrix construction method is proposed where semantically similar phrases are grouped to reduce the dimension curse. When a large collection of documents is to be processed, it includes some documents that are very much related to the topic of interest known as model documents and also the documents that deviate from the topic of interest. These non-relevant documents may affect the cluster quality. The first step in knowledge extraction from the unstructured textual data is converting it into structured form either as term frequency-inverse document frequency matrix or as phrase document matrix. Once in structured form, a range of mining algorithms from classification to clustering can be applied.

Findings

In the enhanced approach, the model documents are used to extract key phrases with synset groups, whereas the other documents participate in the construction of the feature matrix. It gives a better feature vector representation and improved cluster quality.

Research limitations/implications

Various applications that require managing of unstructured documents can use this approach by specifically incorporating the domain knowledge with a thesaurus.

Practical implications

Experiment pertaining to the academic domain is presented that categorizes research papers according to the context and topic, and this will help academicians to organize and build knowledge in a better way. The grouping and feature extraction for resume data can facilitate the candidate selection process.

Social implications

Applications like knowledge management, clustering of search engine results, different recommender systems like hotel recommender, task recommender, and so on, will benefit from this study. Hence, the study contributes to improving document management in business domains or areas of interest of its users from various strata’s of society.

Originality/value

The study proposed an improvement to document management approach that can be applied in various domains. The efficacy of the proposed approach and its enhancement is validated on three different data sets of well-articulated documents from data sets such as biography, resume and research papers. These results can be used for benchmarking further work carried out in these areas.

Details

Benchmarking: An International Journal, vol. 26 no. 6
Type: Research Article
ISSN: 1463-5771

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Article
Publication date: 12 March 2019

Prafulla Bafna, Shailaja Shirwaikar and Dhanya Pramod

Text mining is growing in importance proportionate to the growth of unstructured data and its applications are increasing day by day from knowledge management to social…

Abstract

Purpose

Text mining is growing in importance proportionate to the growth of unstructured data and its applications are increasing day by day from knowledge management to social media analysis. Mapping skillset of a candidate and requirements of job profile is crucial for conducting new recruitment as well as for performing internal task allocation in the organization. The automation in the process of selecting the candidates is essential to avoid bias or subjectivity, which may occur while shuffling through thousands of resumes and other informative documents. The system takes skillset in the form of documents to build the semantic space and then takes appraisals or resumes as input and suggests the persons appropriate to complete a task or job position and employees needing additional training. The purpose of this study is to extend the term-document matrix and achieve refined clusters to produce an improved recommendation. The study also focuses on achieving consistency in cluster quality in spite of increasing size of data set, to solve scalability issues.

Design/methodology/approach

In this study, a synset-based document matrix construction method is proposed where semantically similar terms are grouped to reduce the dimension curse. An automated Task Recommendation System is proposed comprising synset-based feature extraction, iterative semantic clustering and mapping based on semantic similarity.

Findings

The first step in knowledge extraction from the unstructured textual data is converting it into structured form either as Term frequency–Inverse document frequency (TF-IDF) matrix or synset-based TF-IDF. Once in structured form, a range of mining algorithms from classification to clustering can be applied. The algorithm gives a better feature vector representation and improved cluster quality. The synset-based grouping and feature extraction for resume data optimizes the candidate selection process by reducing entropy and error and by improving precision and scalability.

Research limitations/implications

The productivity of any organization gets enhanced by assigning tasks to employees with a right set of skills. Efficient recruitment and task allocation can not only improve productivity but also cater to satisfy employee aspiration and identifying training requirements.

Practical implications

Industries can use the approach to support different processes related to human resource management such as promotions, recruitment and training and, thus, manage the talent pool.

Social implications

The task recommender system creates knowledge by following the steps of the knowledge management cycle and this methodology can be adopted in other similar knowledge management applications.

Originality/value

The efficacy of the proposed approach and its enhancement is validated by carrying out experiments on the benchmarked dataset of resumes. The results are compared with existing techniques and show refined clusters. That is Absolute error is reduced by 30 per cent, precision is increased by 20 per cent and dimensions are lowered by 60 per cent than existing technique. Also, the proposed approach solves issue of scalability by producing improved recommendation for 1,000 resumes with reduced entropy.

Details

VINE Journal of Information and Knowledge Management Systems, vol. 49 no. 2
Type: Research Article
ISSN: 2059-5891

Keywords

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