Dissertations / Theses on the topic 'Human Resource Analytics'

In today’s fast paced, ever-changing world, one cannot help hearing the terms Big Data and analytics. The Internet holds vast amounts of data and this data, for example in retail, is being used to predict shopping habits, current needs, trends, and more. Why should this be limited to the retail side of an organization? Today, there is a more significant push for Human Resource (HR) professionals to be strategic business partners, and, therefore, HR professionals need to work on leading, not lagging, in the area of measurements and analytics. Some organizations that have adopted the use of analytics in their HR departments have been extremely successful. If this is the case, why are not more HR professionals adopting the use of human resource analytics (HRA)? The purpose of this study is to gain insight as to the reasons why more HR professionals are not using HRA to improve organizational performance and to gain and maintain a competitive advantage. An exploration of prior research was performed and resulted in the development of a model representing factors that impact the adoption of HRA. The model was then tested for content validity and reliability using Partial Least Squares of Path Modeling. Results of the study of 302 HR professionals, currently working in the field of HR, suggest the hypotheses testing social influence, tool availability, effort expectancy, performance expectancy, and quantitative self-efficacy as factors impacting the adoption of HRA were all significant. Conversely, the factors data availability, fear appeals, and general self-efficacy were not significant. Findings indicate that the factors impacting the adoption of HRA are not only in the hands of the HR professional but, to some extent, the organization as well. If organizations truly want to adopt HRA, they must make available to the HR professionals the tools, data, resources, and support necessary. This study contributes to the literature on individual-level adoption, specifically of HRA. Implications for theory and practice are discussed, as well as further research.

The use of analytics in human resource (HR) management has proven successful in improving company performance by reducing workforce costs, improving the quality of recruitment, improving talent management and employee engagement, and generally improving productivity. The purpose of this qualitative, multiple-case study was to explore how HR managers use analytics to improve company performance using the contextually based human resource theory as the conceptual framework. The target population comprised a purposeful sample of 5 HR managers in Washington DC; the United States; and Lagos, Nigeria, who had experience using analytics for HR management. Data were collected through semistructured interviews using face-to-face, telephone, and Internet communications and a review of company documents and websites. Data analysis included content and thematic analysis. Four themes emerged from data analysis: the need for HR analytics to align with organizational strategy, the need for understanding HR metrics and how insights derived from HR analytics improve company performance, influencers of HR analytics adoption, and the barriers to HR analytics adoption. The findings and recommendations of this study can assist HR managers in implementing HR analytics successfully. The implications for positive social change include the potential for increased employee satisfaction, improved productivity, and enhanced prosperity in local communities, leading to positive socioeconomic indicators.

The way organizations make decisions today is very often purely based on intuition or gut-feeling. It does not matter whether decisions are of high risk for the company’s future or not, managers golden-gut is the only thing that determines whether invest- ments should be made or not. Analytics is the opposite of this intuition-based decision making. If taken seriously, almost all decisions in organizations are made on facts that are analytically derived from massive amount of data from internal and external sources such as customer relationship systems to social networks. Business leaders are becoming more aware of analytically based decisions, and some use it more than others. Analytics is usually practiced in finance, customer relationships or marketing. There is, however, one area where analytics is practiced by a small number of companies, and that is on the organization’s workforce. The workforce is usually seen as one of the most complicated areas to practice analytics. An employee is, of course, more com- plicated than a product. Despite this fact, companies usually forget that conducting analytics on employees is very similar to conducting analytics on customers, which has been practiced for many decades. Some organizations are showing great success with applications of Workforce Analytics (WA). Most of these organizations are located in the US or outside of Sweden. This thesis has conducted research on to what extent Workforce Analytics is practiced in Sweden. Empirical findings show that some com- panies use WA in Sweden. The practice is not of highest sophistication of WA. Also, they show aspiration towards the idea of WA and some are locally conducting various of applications.

Introduction: In the current rapidly changing world of work, organisations are investing increasingly in workforce planning. Throughout times, recruiting and retaining talented employees have been one of the most complex problems facing employers. Human Resource (HR) metrics and analytics is still a relatively untouched tool used by HR managers. However, HR practitioners are engaging in an era where recruitment and talent retention processes are becoming predictive and provide several benefits to both the employer and employee. By understanding how data can be used for insightful decisions that generate business results, HR professionals need to exploit the gap. Currently, they have the opportunity to utilise their extensive data sets by providing the organisation with the relevant and strategic analytics for informed decision-making. Research purpose: The purpose of the systematic literature review was to investigate the nature and value that metrics and analytics on recruitment and talent management add to organisations. Motivation for the study: Throughout history, people were considered as the most valuable assets in which an organisation can invest. However, HR failed to take responsibility for the programmes and initiatives which they developed and implemented. By developing a means to measure HR programmes and initiatives and assess the performance and development of employees, HR professionals will be able to demonstrate its effect on the business's 'bottom line'. Consequently, top management would then be more willing to invest money and time in HR-related activities. HR metrics and analytics will allow top management to make informed decisions on HR initiatives and programmes such as recruitment and talent management. In a volatile business environment HR departments need to prove the monetary value of the HR functions to top management. For HR analytics to be effective, it is vital that the organisation applies the correct metrics that is aligned with the overall business strategy and objectives. Research design, approach and method: For the purpose of the present study, a systematic literature review was conducted to determine the nature and value-add of recruitment and talent management analytics in an organisation. Main findings: The first objective was to determine the importance of recruitment and talent retention metrics and analytics. Results show that organisations currently struggle to recruit and retain talented employees, a factor that ultimately impacts the success of the organisation. The review provided evidence of organisations that realised the importance of recruitment and talent retention analytics by also using it to inform their human capital planning. The second objective assessed the use of metrics and analytics to manage recruitment and talent. The research indicated that organisations apply various recruitment and talent retention metrics in different ways and for diverse purposes. A possible reason may be that organisations have different data sets and also use these sets differently to develop HR-related metrics suited for the specific company. The third objective was to identify standards for metrics on recruitment and talent management. The research indicates that there is no systematic approach to evaluate recruitment and talent management. This is a topic for future research. The fourth and final objective investigated the role of recruitment and talent retention analytics in management's decision-making. Here the literature indicates that organisations have invested highly in HR analytics, and even appoint an analytics team within the company with the sole purpose of evaluating the organisation's data sets. This attest to the value management attaches to HR analytics as important contribution to the business's decision-making process, and ultimately the success of the organisation as a whole. Limitations/future research: The present research had to factor in several delimitations related to the context, constructs and theoretical perspectives of the study. The researcher firstly identified that the research context was limited to the Human Resources (HR) segment of organisations and business sector. Secondly, only HR-related analytics were used for the purpose of this study. The researcher searched, analysed and made assumptions about this research topic by consulting mainly recent (over the past 10 years) and authentic resources. Lastly, the assumptions made in the study are based on data the researcher incorporated from selected literature. Conclusion: It is evident from the findings that HR professionals began understanding the impact of recruitment and talent retention analytics on validating HR contributions to the organisation. Furthermore, it is clear that, on the other hand, management does acknowledge the importance and value that recruitment and talent retention analytics add to the organisation's 'bottom line'. This allows management to make insightful decisions, and ultimately retain a competitive edge in the market.
Dissertation (MCom)--University of Pretoria, 2016.
Marketing Management
MCom
Unrestricted

A atividade de Human Resources Analytics (HR Analytics) não é recente, mas vem ganhando importância na literatura de Recursos Humanos (RH) e no meio profissional. Contribuem para isso as pressões originadas pelos avanços de capacidade de processamento e análise que Tecnologia da Informação (TI) oferece atualmente e pela crescente exigência para que a estratégia de RH esteja mais conectada à da linha de negócios. A literatura sobre o tema aponta fatores que ainda precisam ser melhor explorados para que o desenvolvimento do tema \"HR Analytics\" possa ser melhor compreendido nos meios acadêmico e profissional, como o aporte de capacitação e conhecimento analíticos no RH, a eficiência na aplicação dos recursos de TI e a conexão da formulação estratégica de RH com as estratégias da linha de negócios e da TI. Esses fatores parecem ligados a como se desenvolve a inteligência de RH através de HR Analytics, cenário em que a criação de conhecimento e o alinhamento com TI ocupam papel central. Dado esse panorama, esta pesquisa busca identificar como é o processo de criação de conhecimento e o contexto de alinhamento entre TI e RH em empresas brasileiras que praticam o HR Analytics. Para isso, foram conduzidos estudos de caso com cinco empresas através de formulários de avaliação e entrevistas semiestruturadas. Os dados foram fornecidos por lideranças de RH e integrantes do processo de HR Analytics das empresas. Os resultados direcionam para diferenças entre como lideranças de RH e Business Partners buscam criar valor através de HR Analytics. Essa observação parece apontar para a necessidade de atenção à sinergia dos esforços para que cada campo de interações atinja seus objetivos; como as importâncias da unificação de conceitos e do direcionamento estratégico no RH. Assim, a maturidade de desenvolvimento do HR Analytics parece se encontrar ligada a esses fatores e também à influência do caminho de desenvolvimento tecnológico optado pelo RH.
Human Resources Analytics (HR Analytics) is not a recent activity, but it is getting relevance in Human Resources (HR) literature and in the professional environment. Compound this scenario pressures brought by the advances in processing and analysis capacity that Information Technology (IT) currently offers and by the increasing requirement over the HR strategy to be more connected to that of the line of business. The literature points out factors that still need to be better explored so that the development of the theme \"HR Analytics\" can be better understood in academic and professional environments, such as the contribution of analytical knowledge in HR, efficiency in the application of IT resources and the connection of strategic HR formulation with line-of-business and IT strategies. These factors seem to be related to how HR intelligence is developed through HR Analytics, a scenario in which knowledge creation and alignment with IT play a central role. Presented this scenario, this research aims to identify how the process of knowledge creation and the context of alignment between IT and HR take place in Brazilian companies that practice HR Analytics. For this, case studies were conducted with five companies through assessment forms and semi-structured interviews. The data were provided by HR leaders and members of the HR Analytics business process. The results point to differences between how HR leaders and Business Partners seek to create value through HR Analytics. This observation seems to highlight attention to the synergy of efforts, so each field of interactions could reach its objectives; such as the importance of unifying concepts and strategic direction in HR. Thus, the development of the maturity of HR Analytics seems to be connected to these factors, as well as the influence of the ways of technological development chosen by HR.

Front-line managers and HR practitioners are principal implementers and gatekeepers of HR practice in organisations and as such, the survival of HR analytics is dependent on their concerted effort to enable it. HR analytics is one of the concepts that organisations can use to become more efficient and effective and to ultimately survive. Whilst the field of HR analytics presents opportunities it is, unfortunately, fraught with several internal and external challenges starting with the alignment of key actors perceptions. This research focuses on the predictors of the degree of adoption of HR analytics. In this research, extensive literature was reviewed to identify which constructs best apply in the context of the sampled subsidiaries of MNE organisations. A quantitative study carried out using a survey monkey questionnaire in which data from 256 front-line managers and HR practitioners in the subsidiaries of 6 Multinational Enterprises was collected. From the study, a model was developed that shows the best predictors for the level of adoption of HR analytics. Findings confirm that dimensions such as organisational culture, the organisation's strategy and national culture are significant predictors for the level of adoption of HR analytics and its effectiveness. Also, from the study, the alignment of perception and trust on the role of HR analytics in the organisations between key actors was found to be instrumental to its success. Such a model is important for organisations to be able to leverage HR analytics investments and for decision-making
Mini Dissertation (MBA)--University of Pretoria, 2017.
ms2017
Gordon Institute of Business Science (GIBS)
MBA
Unrestricted

Teknisk utveckling har varit en drivande kraft för effektivisering av organisationer genom historien. I takt med denna effektivisering har även arbetssätt och krav på anställda förändrats vilket har lett till ett behov av att strukturerat hantera personalrelaterade frågor. Utifrån detta har human resources (HR) som organisatorisk funktion vuxit fram. Den tekniska utvecklingen fortsätter och idag används tekniska lösningar som verktyg för många av organisationens funktioner. Dock menar tidigare forskning att HR inte hänger med i denna utveckling vilket påverkar HR-funktionens roll i organisationen och är en utmaning idag och framöver. En uppmärksammad teknisk lösning idag, i form av en uppsättning teknikbaserade vertyg, aktiviteter och arbetssätt, som anses kunna hjälpa HR som funktion att ta nästa steg i utvecklingen är people analytics. Syftet med denna studie är att öka förståelsen för people analytics i Sverige, vilka effekter som eftersträvas samt vilka möjligheter och utmaningar som finns för att uppnå dessa effekter. Detta grundar sig i att det finns ett behov av empiriskt grundad forskning i en svensk kontext eftersom det finns en avsaknad av detta idag. Genom tillämpning av en kvalitativ surveyundersökning av tolkande karaktär med semistrukturerade intervjuer framkommer ett empiriskt material som analyseras med hjälp av tidigare forskning och socioteknisk teori. Studien visar att people analytics är en uppsättning aktiviteter som är värdefulla för såväl organisationer som HR-funktioner eftersom det möjliggör evidensbaserat strategiskt arbete, med data och statistik som grund, i personalrelaterade frågor. Genom att använda people analytics kan organisationer följa upp sitt personalrelaterade arbete på ett mer strukturerat sätt och HR som funktion har även fått ökad legitimitet i organisationen på grund av detta. Däremot finns det ett antal komplext sammanflätade faktorer som idag agerar hindrande för att people analytics ska nå sin fulla potential i svenska organisationer, dessa faktorer återfinns både i sociala och tekniska aspekter av arbetet med people analytics. Genom att skapa förståelse för varför dessa faktorer påverkar användandet och effekterna av people analytics bidrar denna studie med värdefulla insikter för både praktiker och akademiker. Dessa insikter handlar om hur arbetet med people analytics kan uppnå de långsiktiga mål och syften som finns både inom forskning och hos svenska organisationer.

Turnover is one of the most important phenomena for management scholars and practitioners. Yet, researchers and practitioners are often frustrated by their inability to accurately predict why individuals leave their jobs. This should be worrisome given that total replacement costs can exceed 100% of an employee’s salary (Cascio, 2006) and can represent up to 40% of a firm’s pre-tax income (Allen, 2008). Motivated by these concerns, the purpose of this study was to assess the predictive validity of commonly-investigated correlates and, by extension, conceptualizations of employee turnover using a large-scale database of scientific findings. Results indicate that job satisfaction, organizational commitment, and embeddedness (e.g., person-job fit, person-organization fit) may be the most valid proximal predictors of turnover intention. Results for a tripartite analysis of the potential empirical redundancy between job satisfaction and organizational commitment when predicting turnover intention align well with previous research on this topic and generally suggest that the two constructs may be empirically indistinguishable in the turnover context. Taken together, this study has important implications for the turnover and sensitivity analysis literatures. With regard to the sensitivity analysis literature, this study demonstrates the application of a sensitivity analysis for relative importance weights in the meta-analytic context. This new method takes into account variance around the meta-analytic mean effect size estimate when imputing relative importance weights and may be adapted to other correlation matrix-based techniques (i.e., structural equation modeling) that are often used to test theory.

The present study – employing psychometric meta-analysis of 92 independent studies with sample sizes ranging from 26 to 322 leaders – examined the relationship between EI and leadership effectiveness. Overall, the results supported a linkage between leader EI and effectiveness that was moderate in nature (ρ = .25). In addition, the positive manifold of the effect sizes presented in this study, ranging from .10 to .44, indicate that emotional intelligence has meaningful relations with myriad leadership outcomes including effectiveness, transformational leadership, LMX, follower job satisfaction, and others. Furthermore, this paper examined potential process mechanisms that may account for the EI-leadership effectiveness relationship and showed that both transformational leadership and LMX partially mediate this relationship. However, while the predictive validities of EI were moderate in nature, path analysis and hierarchical regression suggests that EI contributes less than or equal to 1% of explained variance in leadership effectiveness once personality and intelligence are accounted for.

This work project is a literature review, which covers current studies (theoretical and empirical) on electronic Human Resource Management, human resources analytics and telework, and discusses some implications of the adoption of technology in human resource management processes. The Work Project presents and discusses different and contradictory perspectives between empirical and theoretical studies, demonstrating that the results obtained to the moment are not consistent. We conclude that the adoption of technology in human resources management is not yet mature enough in order to understand the real impact.

A research article submitted to the Faculty of Commerce, Law and Management, University of the Witwatersrand, in partial fulfillment of the requirements for the degree of Master of Business Administration Johannesburg, 2019
Advances have been made in the usage of analytics in decision making within business units such as finance and marketing. However, this is not apparent within the human resources unit, rather the use of analytics in human resources is a relatively new area of practice and research. Human Resources Analytics is designated as an evidence-based approach for making better decision on the people side of business and consists of an array of tools and technologies, ranging from simple reporting of human resources metrics all the way to predictive analytics, all of this contributing to the evolution of the role of human resources. The need for more human resources analytics research together with the practical application guidelines in the telecommunications industry provides the rationale for this study. This research set out to discover the ways in which human resources analytics can be applied in the human resources arena, to investigate the factors that will assist in crafting human resources into a decision science that will contribute tangibly to the performance of the organisation. This research explores the presence of human resources analytics using a qualitative approach, a purposively selected sample of thirteen individuals with experience in human resources working for a South African telecommunications organisation were interviewed using a semi-structured questionaire. Data gathered has assisted in revealing an understanding the level of practical application of analytics within the human resources discipline in the telecommunications industry. Furthermore, the research findings propose that; defining an approach to HR Analytics, establishing the true value of HR analytics and identifying the HR business problem to be solved by HR Analytics are critical factors for leveraging HR Analytics. A conceptual framework based on the assumptions from literature review as well as the practical inputs from respondents has been proposed to support in the planning of HR Analytics strategy or projects.
M T 2019

Trabalho de Projeto apresentado como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação, especialização em Gestão do Conhecimento e Inteligência de Negócio
É desenvolvida uma análise exploratória com o intuito de entender a dinâmica dos comportamentos de incumprimento processual dos gestores de conta de uma instituição bancária, explicar as razões e origem desses comportamentos propondo medidas que conduzam à sua redução. São para esse efeito usados modelos supervisionados e não supervisionados, tendo-se identificado padrões que explicam os comportamentos alvo e simulada a aplicação de uma medida avaliando o seu impacto e efeitos secundários.
An exploratory analysis is performed aiming to understand the dynamics of non compliant behaviours of banking account managers on the regulatory standards , exposing the origin of those behaviours and proposing measures that might reduce the occurrence of those behaviour. For that purpose supervised and unsupervised models are applied, having identified patterns that explain the behaviours under analysis, simulating the impact and side effects for one of the proposed measures.

Despite advances in the application of analytics in business functions such as marketing and finance, and a significant degree of interest in the topic of Human Resource analytics, its usage is still nowhere near where it could be. This study’s primary aim was to measure the levels of usage of HR analytics among South African organisations, an exercise that has not been done before. This qualitative, exploratory study was conducted among 16 senior Human Resource practitioners from large organisations in South Africa. Being qualitative, a limitation of this study is that it is not representative and therefore the results cannot be generalised. Further opportunities therefore exist for quantitative, longitudinal research in this field to objectively ascertain the extent of usage of HR analytics. It was found that South African organisations’ usage of HR analytics is still in its infancy and that the concept and its implications are little understood. It also found that there is consensus regarding the importance for HR analytics in organisations and that the HR analytical skills challenge is the main hindrance to implementation. Importantly, the study demonstrated and that the overall outlook for HR analytics is positive. The research makes recommendations and proposes a model that should enable organisations, the HR profession and the academic world to implement HR analytics.
Dissertation (MBA)--University of Pretoria, 2013.
zkgibs2014
Gordon Institute of Business Science (GIBS)
MBA
Unrestricted

碩士
國立中山大學
人力資源管理研究所
92
For being in this changed rapidly new economic era, network technology has brought significant reform for enterprise operational mode, human resource information system has developed with the advantage of information technology, and become a necessary manage instrument gradually, adopt systematic and statistical analyze mode, collocated to display with visional graphic table (such as analytic form or statistic sketch), let high-level and human resource chief be capable of scientific and specific policy assistant data. Data Warehouse is a new technology for data storage, within the data warehouse not only could compile data, even more proceed to decompose、merge and intersect in different range and layer, and then utilizing the function of On-Line Analytical Processing (OLAP) or Data Mining to obtain one step ahead of information, providing applicable message for policy maker. Therefore, in recent year, data warehouse has become the main data source of Decision Support System (DSS) gradually. This research attempts to establish a data warehouse fledgling model for human resource management, providing the basic requirement for rapidly inquire related statistic data for policy maker, and extract data from human manage information system data base, establish a related multiple dimension data model. And apply the technology of Data Warehouse and OLAP, via Internet, policy maker could depends on his requirement inquiring related statistic data elastically and rapidly, and enhance the quality and time effectiveness of decision. This research could establish the systematic benefits as below： Ⅰ.Provide convenience to inquire data: via mouse proceed dragging action, rapidly and time effectively let user operate conveniently in data inquiring procedure. Ⅱ.Multi-dimensional analyze data: owing to OLAP could support multi-dimensional inquire, and makes different intersect analyze and variation comparison, could let the manager make decision reference material more explicitly. Ⅲ.Obtain necessary information elastically: could depend on user’s requirement, arbitrarily change dimension, obtain necessary information, increase user’s inquiring elasticity. Ⅳ.Via network medium access: establishing the base system by web-base, via network and browser could forward to inquire, enhance the system’s mobility and convenience. Ⅴ.Function of data base examination: through OLAP statistic outcome, could reach examine database correctly and completely.

碩士
中原大學
資訊管理研究所
100
The human requirement needs more and more when enterprise towards internationalization and diversification. How an enterprise expands efficiently the human resource management functions, and the enterprise can tend to a competitive advantage if human resource management is done well. Therefore, Human Resource Information System (HRIS) integrating information technology and human resource management plays an important role in enterprises. In recent years, most of the scholars emphasize to research benefits and the critical success factors for HRIS, but they rarely evaluate the usage condition after HRIS implementation. Actually, enterprises need a suitable method for performance evaluation, so as to evaluate the performance of HRIS implementation. Therefore, the study is based on the DeLone and McLean (2003) proposed information system success model, and developing performance evaluation indicators related HRIS by four architectures such as system quality, information quality, service quality in IT personnel, and the benefits produced by HRIS operation. Finally, the study integrates and filters fifteen performance evaluation indicators through literatures and expert opinions, such that fifteen items will be the performance indicators of HRIS set and evaluation. In addition, the study considers that all are group and correlation between each evaluation indicators, so as to use ANP （Analytic Network Process） method to set up the performance evaluation model, and hope that enterprises can use the practical performance evaluation indicators and weights set by the study to be a HRIS performance evaluation scale, so as to evaluate the performance of HRIS. As a result, the system shortage can be improved and the system superiority can be strengthened after the advantages or disadvantages of the system are recognized, so the purpose for system satisfaction upgraded will be achieved.

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
This project presents a predictive analytics project developed in a European multinational to understand and predict the turnover of its employees. It analyses the Human Resources current challenges, such as the increasing global competition for talent, where players compete for scarce skillsets such as technology and data science, and the new strategies necessary to deal with this scenario. The study explores the literature review of these contextual matters and of the studies of variables that influence turnover, generating insights and input for applying techniques aligned with the new mindset of identifying ‘flight-risk’ groups and developing targeted actions instead of only one-size-fits-all solutions. The project gathered data from different sources of the organization, designed variables, based on a literature review and internal brainstorms, treated data quality issues, transformed the data and applied three different machine learning algorithms to develop a classification predictive model. The study evaluated 46 input variables and selected a set of 26 that had higher impact on the turnover which were used in the models. Finally, it applied clustering techniques to divide employees in clusters, and identified two containing more extreme turnover behaviors (“Loyal” and “Flight risk”) and described them accordingly to their main characteristics contributing with practical insights to support potential decisions.

碩士
大葉大學
工學院碩士在職專班
98
Due to the decline of the global economy and decay of enterprise growth and economic development, resulting pressures on government owned companies have become of increasing public concern and debate. Facing elected officials and taxpayer’s expectations, government owned companies needs to improve company reputation, ownership relations, customer service and the company’s balance sheet. Therefore, the question becomes how does a government owned enterprises or other organizations, achieve these objectives recognizing " human resource" is the most precious resource and source of any company’s core competitiveness. The purpose of this research, is to study related literature and application, then using the Fuzzy analytical hierarchical process to analyze the Taiwan Power Company’s current manpower and prioritize the weights of those factors found in human resource planning and management, and then explore the older aged employees’ strengths and characteristics, then to compare findings to the manpower structure and finally to develop avenues by which to achieve higher performance for the enterprise and make correct human resource acquisition and placement policy. The results show that executive personnel and first line supervisors have different views on the importance of human resources planning from four principal views; (1) Adjust manpower structure, (2) Improve manpower efficiency, (3) Motivation and human resources development and (4) Change staff attitudes. The views of the high-level managers verses those of the first line supervisor differ from one another with regard to human resource planning. Management is interested in total management and performance appraisal, whereas, the supervisors are interested in attaining sufficient number of personnel and actual manpower placement. These two different viewpoints and the weights of those factors can help a company to make strategic human resources planning, implementation, budget allocation, and performance appraisals. The results of this research will allow enterprises to effectively manage employees and labor unions to achieve the optimal human resource planning, allocation and implementation.

碩士
國立中山大學
人力資源管理研究所
99
The function of human resource in business is changing. For this reason, the tasks of human resource top managers become much more complicated as well. Therefore, the purpose of this thesis is to seek and rank the roles of the human resource top manager. In this study, the framework of human resource top manager’s roles is adapted from Schuler(1990). It becomes a five-role framework of the human resource top manager： business person, shaper of change, partner to line, strategy formulator and implementer, and talent manager. In order to elaborate the five roles, each role has added three to four particular activities. Through applying the Analytical Hierarchy Process to gather the opinions of 19 human resource top managers and analyzing the data, it can rank the five roles as below： strategy formulator and implementer, partner to line, talent manager, shaper of change, and business person. Furthermore, the ranks will change depends on the different of industries, business scales, and the ownership. From this study, it suggests that the human resource top manager should adjust his/ her roles according to the scenarios. Also, the chef of the business can peek the human resource top manager by evaluating the candidates’ ability to play these five roles.

Trabalho de projeto de mestrado em Matemática Aplicada à Economia e Gestão, Universidade de Lisboa, Faculdade de Ciências, 2020
Nos últimos anos, tem-se vindo a registar um aumento da rotatividade laboral. Efectuar uma avaliação adequada aos riscos associados à rotatividade dos colaboradores, pode contribuir para a diminuição dos custos associados às novas contratações e evitar a perda de produtividade da organização. Nesse sentido, com este trabalho pretende-se, como objectivo global, criar um modelo capaz de prever antecipadamente as saídas voluntárias dos colaboradores, com o recurso a metodologia de Human Resources Analytics (HRA), que corresponde ao conjunto de competências, tecnologias e práticas que permite aos Recursos Humanos (RH), com base na exploração de dados, fornecer insights de suporte à tomada de decisão, na gestão e resolução de desafios de negócio. Como tal, pretende-se criar dois modelos distintos de previsão de saída dos colaboradores, através do uso de modelos de regressão logística e de árvores de decisão. Estas metodologias permitem, através de um conjunto de variáveis independentes, prever a rescisão voluntária do contrato de trabalho. Para além disso, é possível averiguar qual é a variável que tem o maior ganho de informação no modelo. Para a construção dos modelos, a variável resposta é definida como a vinculação, ou não, de um colaborador à empresa, consoante as variáveis que melhor caracterizam o seu perfil. Por neste trabalho se ter assumido o propósito de identificar e quantificar os colaboradores que pretendem rescindir o contrato de trabalho, está-se perante a um modelo de classificação. Desta forma, são estabelecidas duas amostras distintas, de treino e de teste. Assim, o conjunto de observações da amostra de teste nunca irá influenciar a construção do modelo e, por sua vez, é possível testar e avaliar a capacidade discriminatória do mesmo. O melhor modelo obtido foi através do uso da regressão logística, que permitiu predizer corretamente todas as observações em 74,71%, sendo a variável idade aquela que tem maior importância no modelo.
In recent years, there has been an increase in labor turnover. Carrying out an adequate assessment of the risks associated with employee turnover may contribute to the reduction of costs associated with new hires and avoid the loss of productivity of the organization. In this sense, the overall objective of this work is to create a model capable of predicting in advance the voluntary departures of employees, using the Human Resources Analytics methodology, which corresponds to the set of skills, technologies and practices that allow Human Resources (HR), based on data exploration, to provide insights to support decision making, management and resolution of business challenges. As such, it aims to create two distinct models of employee exit forecasting, through the use of logistic regression models and decision trees. These methodologies allow, through a set of independent variables, the voluntary termination of the employment contract. In addition, it is possible to find out which variable has the greatest information gain in the model. For the construction of the models, the response variable is defined as the link, or not, of an employee to the company, depending on the variables that best characterize his/her profile. Since the purpose of this work is to identify and quantify the employees who intend to terminate the employment contract, this is a classification model. In this way, two distinct training and test samples are established. Thus, the set of observations in the test sample will never influence the construction of the model and, in turn, it is possible to test and evaluate its discriminatory capacity. The best model obtained was through the use of logistic regression, which allowed the correct prediction of all observations at 74.71%, the age variable being the one that has the greatest importance in the model.

碩士
國立成功大學
高階管理碩士在職專班
97
Taiwan Semiconductor industry has been developing into top industry in the world after 30 years continuous development and foundry manufacturing plays a key role to bring it rising and flourishing. In terms of business scope, TSMC & UMC have achieved top 1 & 2 statuses in the world after the toughest competitive. However, the business gap has been grown up between TSMC and UMC since 2000, therefore, this study intend to explore if TSMC and UMC employ different human resource practices. Human Resource Management Practices are various management activities of company to execute its strategies, which directly influence employee’s motivation and ability towards to providing products and services. Suitable human resource management practices lead company to create competitive advantage. A successful company requires many competitive advantages, and human resource management is the key criteria for the company. This study explores both two companies deliver the different opinions of human resource management strategies and practices on company-side or employee-side. Employee rewards is usually prior to other human resource management practices by companies to satisfy the physiological and safety needs according to Maslow’s hierarchy of needs. However, when physiological and safety needs get satisfied, the advanced needs such as love/belonging and esteem needs for employee relationship and self-actualization needs for job characteristic and training and development. Indeed, it is not surprised that both companies present different outcome whether in company-side or employee-side as it is not news in semiconductor field that both companies have different management style. This study just attempts to further explore the human resource management practices differences between both companies through the academic research.

Os seres humanos não são capazes nem de absorver toda a informação disponível nem de prever todas as consequências dos seus atos. Deste modo, implementar métodos para extração de conhecimento das bases de dados pode auxiliar nos processos de tomadas de decisão. O propósito desta tese foi o de estudar os objetivos, os desafios e as oportunidades que os métodos de Data Mining oferecem à Gestão de Recursos Humanos. O estudo empírico foi realizado entre dezembro de 2019 e março de 2020. Três grandes empresas e uma pequena e média empresa, em Portugal, foram entrevistadas. Outras fontes secundárias foram também analisadas. Para completar o estudo, realizou-se um caso de estudo de um processo de recrutamento e seleção, sendo aplicado o coeficiente Gower’s Distance numa base de dados real. Os resultados do estudo permitirão perceber quais (1) os propósitos, (2) os desafios e (3) as oportunidades que surgem da aplicação de métodos de DM. Do estudo foi percetível que a aplicação destes métodos na Gestão de Recursos Humanos não é uma prioridade para as empresas. Para investir nestes métodos mais sofisticados com o DM, as empresas precisam de averiguar quais os desafios éticos e os problemas relacionados com a privacidade que podem surgir. Precisam, ainda, de ter uma equipa com profissionais com diferentes formações. Contudo, se as empresas tiverem dados fiáveis e atualizados e acompanharem o progresso tecnológico, o investimento na análise de dados permitirá acrescentar valor a cada processo assim como o crescimento no mercado.
Humans cannot absorb all the information available and predict all the consequences. Hence, implementing methods to extract knowledge from databases may guide in the decision-making process. The thesis aimed to study the purposes, the challenges and the opportunities that Data Mining methods offer to Human Resources Management. The empirical study was conducted between December of 2019 and March of 2020. Three large companies and one SME, in Portugal, were interviewed. Additionally, another secondary source was also included. Also, a Recruitment and Selection Case Study was conducted through an application of an algorithm step, the Gower’s Distance coefficient, in a real database. The findings will be used to promote understanding regarding the (1) purposes, (2) challenges, and (3) opportunities that may arise from the use of DM methods. From the empirical study was possible to understand that the application of DM methods in HRM is not a priority for most of the companies. To invest in more sophisticated methods, as DM methods, companies need to face privacy and ethical challenges and have an analytical team with people from different backgrounds. However, if companies achieve a set of requirements concerning data and monitor technological progress, the investment in data analytics will allow them to add value to each process and to keep continued growth in the market.

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
Os processos analíticos são um elemento-chave para o sucesso organizacional. Atualmente, a capacidade de analisar as informações das empresas fornece uma vantagem para qualquer departamento, nomeadamente para o departamento de Recursos Humanos. A revisão literária sobre o tema desta dissertação aponta fatores que precisam ser mais bem explorados e aperfeiçoados para que o desenvolvimento e implementação de processos analíticos nos RH possam ser aceites e melhor compreendidos, quer num contexto profissional, quer num contexto académico. Através dos processos analíticos de Recursos Humanos, surgirá um cenário em que a criação de conhecimento ocupa um papel central para toda a estrutura organizacional. Um dos fatores motivadores desta pesquisa é conseguir compreender que processos de análise de dados está a ser utilizado no departamento de Recursos Humanos nas consultoras em Portugal, como estas criam conhecimento, como o gerem e o impacto que estes processos têm nas áreas em que são postos em prática. Para isso, realizou-se um questionário junto destas empresas, para conseguir responder aos objetivos desta dissertação. Os dados foram obtidos por colaboradores de departamentos de RH, que utilizam processos analíticos nas suas funções. Os resultados demonstram as técnicas analíticas utilizadas, como é desenvolvido todo o processo de análise, assim como quais são as vantagens da realização das mesmas. Este estudo evidenciou a necessidade de potencializar mais os resultados destas análises que surgem dos departamentos de RH, permitindo ajudar de forma transversal os outros departamentos a atingir os seus objetivos, como também partilhar e evidenciar o impacto das tecnologias de informação nas organizações.
Analytical processes are a key element of the organizational success. Currently, the ability to analyze company information provides an advantage for any department, namely the Human Resources department. The literary review of this dissertation points out factors that need to be better explored and improved so that the development and implementation of analytical processes in HR can be accepted and better understood, both in a professional context and in an academic context. Through the Human Resources analytical processes, a scenario will arise in which the creation of knowledge plays a central role for the entire organizational structure. One of the motivating factors of this research is to be able to understand what data analysis processes are being used in the Human Resources department at consulting in Portugal, how they create knowledge, how they manage it and the impact that these processes have in the areas in which they are placed. For this, a questionnaire was carried out with consulting organizations, in order to answer the objectives of this dissertation. The data was obtained by employees of the HR departments that use analytical processes in their functions. The results demonstrate the analytical techniques used, how the entire analysis process is developed, as well as what are the advantages of performing it. This study highlighted the need to enhance the results of these analyzes that emerge from the HR departments, allowing to help other departments in a transversal way to achieve the organizational objectives, as well as to share and highlight the impact of information technologies in organizations.

Measuring salespeople’s performance is a process that occurs multiple times per year on a company. During this process, the manager and the salesperson evaluate how the salesperson performed on numerous Key Performance Indicators (KPIs). To prepare the evaluation meeting, managers have to gather data from Customer Relationship Management, Financial Systems, Excel files, among others, leading to a very time-consuming process. The result of the performance evaluation is a classification followed by actions to improve the performance where it is needed. Nowadays, through predictive analytics technologies, it is possible to make classifications based on data. In this work, the author applied a Naive Bayes model to classify salespeople into pre-defined categories provided by the business, through the use of data mining techniques over a dataset of about three years of sales made by 566 salespeople of a global freight forwarder. The classification is done in 3 classes, being: Not Performing, Good and Outstanding, the classification was achieved based on KPI’s like growth volume and percentage, sales variability along the year, opportunities created, customer baseline, target achievement among others. The author also identified the most critical factors for salesperson’s success based on the dataset as Growth amount, Target achievement, Growth percentage, and the number of months with growth above 0. The author assessed the performance of the model with a confusion matrix and other techniques like True Positives, True Negatives, and F1 score. The results showed an accuracy of 92,10% for the whole model.
Avaliar a performance de vendedores é um processo que ocorre várias vezes por ano numa empresa. Durante este processo, o gestor e o vendedor avaliam o desempenho do vendedor em vários Indicadores de Performance. Para a reunião de avaliação, os gestores recolhem dados do sistema de Gestão de Vendas, Sistemas Financeiros, ficheiros Excel, entre outros, levando a um processo longo e exaustivo. O resultado da avaliação de desempenho é uma classificação seguida por sugestões de melhoria. Atualmente, através das tecnologias de análise preditiva, é possível fazer classificações com base em dados. Neste trabalho, o autor aplicou um modelo Naive Bayes para classificar os vendedores em categorias predefinidas fornecidas pelo negócio, usando técnicas de data mining aplicados a um conjunto de dados, composto por cerca de três anos de vendas de um transitário global. A classificação é feita em 3 classes, sendo estas: Baixo desempenho, Bom e Fora de Série, a classificação foi alcançada com base em KPI’s como a percentagem de crescimento, a variabilidade de vendas entre muitos outros. O autor também identificou os fatores críticos para o sucesso de um vendedor, de acordo com os dados, como sendo volume do crescimento da base de clientes, a capacidade de atingir os objetivos, a percentagem de crescimento e número de meses com crescimento positivo. O autor avaliou o desempenho do modelo com uma matriz de confusão e outras técnicas como True Positives, Negatives, e o score F1. Os resultados apresentaram uma precisão de 92,10 % para todo o modelo.