Six Sigma in Human Resources

HR business partner, Business Excellence are some buzzwords in the industry nowadays. Profitability and efficiency are being driven through various strategic initiatives aligned to the vision of the organization. Customer satisfaction is now being replaced by customer delight. Organizations are taking steps ahead of voice of customer. The consumer insights are thoroughly analyzed and interpreted. Data analytics is not restricted to only finance and operation functions but are widely used across the support functions along with line functions. Human resource is now considered as an asset. Organizations are also trying to find out ways to capitalize the full potential of human asset. Various tools and methodologies are paving its way to bring efficient human resource management practices. Six Sigma is one of the tools, which is booming into the application space of Human Resource Management. Six Sigma is being considered as a business process and is helping the in shaping and improving their bottom line by designing and monitoring various activities to reduce the defects.

Measuring the Different Facets of Diversity Using Quantitative Methods

The aim of this chapter is to provide quantitative techniques and guidance for analyzing different problems related to the measurement of diversity and inclusion practices present in organizations. The example of only one dimension of diversity; viz Gender diversity is given in this chapter. However, these Quantitative tools can be used to explore other facets of diversity as well. In this way, this chapter shall seek to provide a basic understanding of how to analyze and study the data collected for research on Diversity and Inclusion practices in organizations.

Application of Statistics in HR Research

Application of statistics in HR research has been briefly explained in our introductory chapter. It is now acknowledged, with statistics, we can ensure our HR research is more effective. Such research results can also help to take critical HR decisions at organization level. In this chapter, we have discussed on application of statistics in HR research in two critical areas, i.e., human resource planning (HRP) and performance management. Both in HRP and performance management, we can make extensive use of various mathematical, econometric, and statistical tools. Also, we have many established models. However, here our focus is restricted to only some of the simple statistical tools that can help in research in this two-critical human resource management areas. As the purpose of this chapter is to explain use of statistics in two major areas of HR research, it will cover only some selected areas of application. At the outset focus is on the specific research nitty-gritty, as these may help prospective researchers to get their basics clear, before they proceed for research in HRP and performance management areas.

Application of Lens Model in HRM Research

The appropriate use of data-gathering tools and statistical analysis is a formidable challenge in several domains of HRM research. The application of Brunswik's lens mode offers an innovative strategy in this context. Brunswik's lens model is presented and its procedural application as suggested by Hammond in terms of social judgment theory is elaborated. A broad range of application domains including multiple-cue learning, cognitive conflict, policy formation and social issues is described. Studies carried out in Indian context are reviewed. The immense possibility of application in HRM domain is indicated. The idiographic-statistical elements are pointed out. It is asserted that the application of lens model in HRM research would pave the way for greater elegance and expansion of research.

Introduction to HR Research Using Statistics

HR research through collection, collation and analysis of data, using statistics, immensely help. Even in day to day HR decision making, we need statistics. HR managers can take decisions, and so also a HR researcher can frame suitable hypothesis for getting deeper insights on specific research issue. In all HR functions, we see use of statistics can help. While for some HR researcher statistics help in finding answer to a research issue or a problem; for HR manager statistics can help in taking critical HR decisions with minimum chances of error. Thus, statistics promotes fact based approach in HR research and HR decision making. With statistics, it is also possible to take predictive HR decisions, when we simultaneously make use of HR analytics. Predictive HR decisions help in understanding the future implications of HR decisions, which help HR managers to initiate corrective actions well in advance, before the catastrophic failure in organizations.

Application of Statistics in Human Resource Management

The chapter gives an outline of the shift in HRM from being intuitive to quantitative in its decision making and overall functioning. The role of HRM is transforming with application of statistical techniques that make HR more evidence based and accountable. The chapter will discuss some successful applications of statistical techniques, basic and, in HRM by renowned organizations worldwide as well as elucidate upon some of the most applied statistical techniques. After reading this chapter learner will appreciate the need for applying Statistics in HRM, have an understanding of the avenues for application of statistical tools and get an outline of the various statistical techniques that are appropriate for different HR functions.

Methodological Considerations for Research in Compensation Management

Compensation and Benefits continues to be the most researched field with more than thousand academic studies. Given the extensive research on Compensation in academia, there has been evolution in approaches to explore and keep pace with recent trends along with research methodology and technology. As a Research Scholar, I began to realize that while dominant literature on Compensation and Benefits favoured quantitative research to study its impact on organizational outcomes such as performance, turnover, job satisfaction, commitment, etc., both qualitative and quantitative research are needed to be able to study and explore unexplored areas of the said field. The book chapter will elaborate the specific applications of qualitative and quantitative statistical applications in Compensation Research with relevant basic examples. I am hopeful that the book chapter will be of use to academics, researchers and students focusing their studies and research on Compensation and Benefits.

Uncovering Data for Decision Making With Critical Statistical Analysis

Every field of study generates a huge amount of data. The volume of data generated leads to information overload, and the ability to make sense of all these data is becoming increasingly important. This requires a good understanding of the data to be analyzed and different statistical techniques to be used in that context. On the basis of the issues important to the data set as well as other practical considerations, it is necessary to select appropriate methods to apply to the problem under study. This work focuses on different issues arising in the context of data analysis which need attention like understanding classifications of data, magnitude of errors in measurement, missing observations in the data set, outlier observations and their influences on the conclusion derived from the data, non-normal data, meta analysis, etc. In the process of discussion some examples have been included to illustrate how critical a data analysis procedure could be in order to make a meaningful decision from a data set.

Organizational Well-Being

The aim of this paper is two aspects: to provide an overview of organizational wellbeing (OW) research; to present a new model of OW focusing on successful outcomes and its operationalization of the construct and the recommendations for future. A summary literature review of the OW literature, focusing on organizational well-being and its possible consequences. The literature is used to develop and propose a new model of OW and its success indicators. Testable relationships are proposed between these indicators. The research model has not been tested empirically. It is an external representation, is a new and untested concept in the OW literature. The paper provides a model that leaders, managers and newcomers may find useful to successfully establish the OW process. The model proposed is novel and raises the important issue of appropriate OW success indicators. New propositions are made regarding relationships between antecedents and output variables.

Driving Employee Engagement Through Data Analytics

Employee engagement refers to a condition where the employees are fully engrossed in their work and are emotionally attached to their organization. An “engaged employee” is one who is fully involved in, and enthusiastic about their work, and thus will act in a way that furthers his/her organization's interests and productivity. There is a clear and mounting evidence that employee engagement keenly correlates to corporate performance in areas such as retention, productivity, customer service and loyalty. This timely treatment provides a comprehensive framework, language, and process that genuinely connects People Strategy with Business Strategy. Aimed at HR Professionals and People Managers, this chapter offers a complete, practical resource for understanding, measuring and building engagement with the use of data. Grounded in engagement theory and an understanding of psychology combined with practical tools, techniques and diagnostics this will help professionals make better and more informed decisions across the Engagement, Retention and People Satisfaction space.