A fuzzy expert system (FES) tool for online personnel recruitments

The advent of the internet has facilitated greater access to the myriad of job opportunities available globally. Currently there exist many job application submission portals that are being used for online job recruitment purposes. However, the task of many of these job submission portals is limited to matching the professional and academic qualifications of applicants with the requirements of employers and several organisations and does not involve the ranking of applicants’ credentials according to their relative suitability for the jobs applied for. In this paper, we describe the implementation of fuzzy expert system (FES) tool for selection of qualified job applicants with the aim of minimising the rigour and subjectivity associated with the candidate selection process. A performance evaluation of the FES tool that was conducted confirmed the viability of a FES-based approach in handling the fuzziness that is associated with the problem of personnel recruitment

Financial cost implications of inaccurate extraction of transactional data in large African power distribution utility

Published ArticleIn view of the increasingly competitive business world, prudent spending and cost recovery have become the driving force for the optimal performance of large public organizations. This study, therefore, examined the cost-effectiveness of a Large Energy Utility (LEU) in a Southern African country by exploring the relationship between extraction of transactional customer data (that is, data on the servicing and repairing energy faults) and the Utility’s recurrent expenditure (especially its technicians’ overtime bill). Using data mining, a large corpus of the LEU Area Centre (AC) data was extracted to establish the relationship between transactional customer data extraction including capture and the financial cost of the LEU (e.g., recurrent expenditure on overtime bill). Results indicate that incorrect extraction and capturing of transactional customer service data has contributed significantly to the LEU’s escalating overtime wage bill. The data also demonstrate that the correct extraction and capturing of transactional customer service data can positively reduce the financial costs of this LEU. The paper demonstrates one of the few attempts to examine the effects of correct data extraction and capture on the financial resources of struggling large public energy utility. Using Resource Based Theory, the study also demonstrates how technicians’ feedback on incorrect transactions enhances the measurement of inaccurate transactional data albeit a burgeoning overtime wage bill incentives

ANALYZING EMPLOYEE ATTRITION USING DECISION TREE ALGORITHMS

Employee turnover is a serious concern in knowledge based organizations. When employees leave an organization, theycarry with them invaluable tacit knowledge which is often the source of competitive advantage for the business. In order foran organization to continually have a higher competitive advantage over its competition, it should make it a duty to minimizeemployee attrition. This study identifies employee related attributes that contribute to the prediction of employees’ attritionin organizations. Three hundred and nine (309) complete records of employees of one of the Higher Institutions in Nigeriawho worked in and left the institution between 1978 and 2006 were used for the study. The demographic and job relatedrecords of the employee were the main data which were used to classify the employee into some predefined attrition classes.Waikato Environment for Knowledge Analysis (WEKA) and See5 for Windows were used to generate decision tree modelsand rule-sets. The results of the decision tree models and rule-sets generated were then used for developing a a predictivemodel that was used to predict new cases of employee attrition. A framework for a software tool that can implement therules generated in this study was also proposed.Keywords: Employee Attrition, Decision Tree Analysis, Data Minin

Comparison of decision tree methods in classification of researcher’s cognitive styles in academic environment

In today's internet world, providing feedbacks to users based on what they need and their knowledge is essential. Classification is one of the data mining methods used to mine large data. There are several classification techniques used to solve classification problems. In this article, classification techniques are used to classify researchers as “Expert” and “Novice” based on cognitive styles factors in academic settings using several Decision Tree techniques. Decision Tree is the suitable technique to choose for classification in order to categorize researchers as “Expert” and “Novice” because it produces high accuracy. Environment Waikato Knowledge Analysis (WEKA) is an open source tool used for classification. Using WEKA, the Random Forest technique was selected as the best method because it provides accuracy of 92.72728. Based on these studies, most researchers have a better knowledge of their own domain and their problems and show more competencies in their information seeking behavior compared to novice researchers. This is because the “experts” have a clear understanding of their research problems and is more efficient in information searching activities. Classification techniques are implemented as a digital library search engine because it can help researchers to have the best response according to their demand

Financial cost implications of inaccurate extraction of transactional data in large African power distribution utility

In view of the increasingly competitive business world, prudent spending and cost recovery have become the driving force for the optimal performance of large public organizations. This study, therefore, examined the cost-effectiveness of a Large Energy Utility (LEU) in a Southern African country by exploring the relationship between extraction of transactional customer data (that is, data on the servicing and repairing energy faults) and the Utility’s recurrent expenditure (especially its technicians’ overtime bill). Using data mining, a large corpus of the LEU Area Centre (AC) data was extracted to establish the relationship between transactional customer data extraction including capture and the financial cost of the LEU (e.g., recurrent expenditure on overtime bill). Results indicate that incorrect extraction and capturing of transactional customer service data has contributed significantly to the LEU’s escalating overtime wage bill. The data also demonstrate that the correct extraction and capturing of transactional customer service data can positively reduce the financial costs of this LEU. The paper demonstrates one of the few attempts to examine the effects of correct data extraction and capture on the financial resources of struggling large public energy utility. Using Resource Based Theory, the study also demonstrates how technicians’ feedback on incorrect transactions enhances the measurement of inaccurate transactional data albeit a burgeoning overtime wage bill incentives. Keywords: Large Energy Utility, inaccurate transactional data extraction, financial costs, Resource Based View. JEL Classification: L94, L97, C

A importância do people analytics na retenção de talento nas organizações

A retenção de pessoas nas organizações é uma área crucial para a Gestão de Recursos Humanos, estimulando os níveis de produtividade e desempenho, diminuição dos níveis de rotatividade e diminuição dos custos com a saída de colaboradores, gerando maior rentabilidade. O People Analytics surge no sentido de aumentar a contribuição dos Recursos Humanos (RH), nomeadamente, de auxiliar as organizações a tomar decisões críticas informadas em torno da aquisição, desenvolvimento e retenção de talento. Com o objetivo de mapear as contribuições da ciência para o People Analytics na retenção de talento, este estudo apresenta uma revisão sistemática da literatura sobre o tema. Na metodologia utilizada, implementaram-se critérios de seleção que direcionaram a recolha da amostra. A amostra integra 16 documentos científicos escritos por 31 autores, publicados entre 2008 e 2020. Os resultados obtidos inferem sobre as vantagens, o impacto, os desafios e limitações e as Técnicas de Data Mining mais utilizadas no People Analytics na retenção. Este estudo indica que apesar da crescente produção científica do People Analytics, esta ainda é escassa quando aplicada ao domínio da retenção. Identificam-se as seguintes vantagens do People Analytics na retenção: melhorar as práticas de gestão de talento; aumentar o desenvolvimento da tecnologia; a diminuição da taxa de rotatividade e o aumento a competitividade. O estudo indica um impacto positivo da implementação do People Analytics na retenção e aponta como principais desafios, as limitações de sistemas de GRH, a indisponibilidade de dados e a necessidade de formação dos profissionais de RH para aquisição de competências de análise de dados. Quanto às técnicas de data Mining mais utilizadas na retenção, foram identificadas as redes neuronais e as árvores de decisão. Por fim, são discutidas as principais implicações e algumas limitações do estudo, bem como são apresentadas sugestões para pesquisas futuras.Nowadays, effectively managing human capital is fundamental for organizations, which implies the creation of strategies for organizations to gain competitive advantage. People retention in organizations is a crucial area for Human Resources Management, stimulating productivity and performance levels, reducing turnover levels and reducing costs with the departure of employees, generating greater profitability. People Analytics (PA) arises to increase its contribution to HR and has the potential to help organizations make informed critical decisions around the acquisition, development and retention of people. With the objective of mapping the scientific contributions towards People Analytics in people retention, this study presents a systematic literature review on this subject. In the methodology applied, selection criteria were implemented that guided the collection of the sample. The sample includes 16 scientific documents written by 31 authors, published between 2008 and 2020. The results obtained infer about: Advantages of People Analytics in retention; Impact of People Analytics on retention; Challenges and Limitations of People Analytics in retention and Data Mining Techniques most used in retention. This study indicates that despite the increasing scientific production of People Analytics, this is still scarce when applied to retention. The following advantages of People Analytics in retention are identified: improving talent management practices; increase the development of technology; reduce the attrition rate and increase competitiveness. The study indicates a positive impact of the implementation of People Analytics in retention and points out as main challenges, the limitations of HRM systems, the unavailability of data and the need to train HR professionals to acquire data analysis skills. As for the data mining techniques most used in retention, neuronal networks and decision trees were identified. Finally, the main implications and some limitations of the study are discussed, as well as suggestions for future research