Quantitative Research in Economics and Management Sciences


Book Description

In this thematic issue of the Journal of Entrepreneurship, Management and Innovation, entitled Qualitative Research in Economics and Management Sciences, the authors used many quantitative methods and research models, e.g. SEM, PLS-SEM, or probit models (Table 1). Each of these approaches is characterized by methodological rigor and an assessment of the reliability and validity of the research instruments used. Pini and Tchorek (2022) analyze the determinants of exports in two European, culturally related countries, such as Italy and Poland, using an econometric and probit model, which implies a normal distribution of errors and is adapted to binary responses (excluding size and age variables). The authors investigate the influence of many independent variables (size, age, management by family members or external managers) on the dependent variable (export), controlling the research model by product and process innovation, location in a less developed region, operations in a high/medium-high technology-intensive sector or cooperation with many banks. The results confirm the authors' initial assumptions that the size of companies influences the exports of the surveyed countries; the age of companies exporting their goods is more important in Italy than in Poland, where no such impact has been recorded. In addition, management by an external manager increases the likelihood of exports for younger family businesses in Italy and smaller family businesses in Poland. The authors also showed that product innovation is the engine of exports in Italy and Poland, and geographic location affects the likelihood of exports in Italy, but not in Poland. In other studies, Paulino (2022) presents the growing business analytics and business intelligence in the Philippines, their impact on organizational performance, and marketing, financial, and business process performance indicators. Retail companies were selected for the study, focusing on advanced data management used in business operations. The author mainly used the well-known PLS-SEM model, and his research instrument was assessed in terms of content validity, construct validity, and reliability. The results of the measurement and structural model evaluation were also subject to verification. The results indicate the impact of business analytics capabilities (including the ability of the decision support system (DSS), business process improvement (BPM), data dashboard (DD), and financial analysis (FA) on the business intelligence level. In addition, it has been empirically verified that organizational performance influences marketing, financial, and business process performance. Overall, business intelligence is an essential predictor of a retail company's organizational performance. The assumption that the level of readiness to implement business analytics can be treated as a moderating factor between business analytics and organizational performance has not been confirmed. The next article by Klimontowicz and Majewska (2022) presents the positive impact of intellectual capital (IC), especially its three components, such as process capital, human capital and relational capital, on the competitiveness of banks and market efficiency. The authors used the following methods and tools: Principal Axis Factor Analysis, PLS-SEM, PAPI, and CAWI. As a result of their application, they emphasize that, in contrast to previous research, the process capital dominates the bank's potential to create a competitive advantage, not human capital, proving the vital role of technology and innovation. They found that competitive performance moderates the relationship between IC and market efficiency; the environment positively moderates the relationship between IC and competitor performance as well as the relationship between competitor performance and market efficiency. The size of the bank and the length of its market activity affect the market efficiency measured by the average rate of changes in ROA and ROE. The study expands the existing evidence, mainly from well-developed countries, on the intellectual capital of Polish banks, emphasizing the process capital to a much greater extent as a modern and so far little exposed component of IC in other research. The last two articles refer to human resource management. Hassan's study (2022) explores the impact of human resource management (HRM) practices on employee retention. In addition, he moderates the role of performance evaluation, training and development in the relationship between HRM practices and employee retention. Using SEM and questionnaires validated by other researchers, the author proves the originality of research in the retail sector in the Maldives on improving employee retention, a complementary approach to the impact of rewards and compensations, training and employee development, as well as assessing their results in human capital management, recommending practical solutions for the sector retail Maldives. In another study on workers’ adaptive performance, Tan and Antonio (2022) using PLS-SEM prove that the new form of remote work and the so-called e-leadership forced by the COVID-19 pandemic have changed the way employers and employees interact. Organizational commitment, teleworking and a sense of purpose affect the adaptive performance of employees directly, while the perception of e-leadership indirectly. It is also one of the first studies to capture intrinsic motivation as the antecedent of employee adaptive performance, along with perceived e-leadership and teleworking results.




Quantitative Methods in Economics and Finance


Book Description

The purpose of the Special Issue “Quantitative Methods in Economics and Finance” of the journal Risks was to provide a collection of papers that reflect the latest research and problems of pricing complex derivates, simulation pricing, analysis of financial markets, and volatility of exchange rates in the international context. This book can be used as a reference for academicians and researchers who would like to discuss and introduce new developments in the field of quantitative methods in economics and finance and explore applications of quantitative methods in other business areas.




Quantitative Methods for Management


Book Description

This book focuses on the use of quantitative methods for both business and management, helping readers understand the most relevant quantitative methods for managerial decision-making. Pursuing a highly practical approach, the book reduces the theoretical information to a minimum, so as to give full prominence to the analysis of real business problems. Each chapter includes a brief theoretical explanation, followed by a real-life managerial case that needs to be solved, which is accompanied by a corresponding Microsoft Excel® dataset. The practical cases and exercises are solved using Excel, and for each problem, the authors provide an Excel file with the complete solution and corresponding calculations, which can be downloaded easily from the book’s website. Further, in an appendix, readers can find solutions to the same problems, but using the R statistical language. The book represents a valuable reference guide for postgraduate, MBA and executive education students, as it offers a hands-on, practical approach to learning quantitative methods in a managerial context. It will also be of interest to managers looking for a practical and straightforward way to learn about quantitative methods and improve their decision-making processes.




Some Quantitative Methods and Models in Economic Theory


Book Description

Introduction -- Linear dynamic models -- Lotka-Volterra's models in economics -- Various dynamic models




Analysing Quantitative Survey Data for Business and Management Students


Book Description

In Analysing Quantitative Survey Data, Jeremy Dawson introduces you to the key elements of analysing quantitative survey data using classical test theory, the measurement theory that underlies the techniques described in the book. The methodological assumptions, basic components and strengths and limitations of this analysis are explained and with the help of illustrative examples, you are guided through how to conduct the key procedures involved, including reliability analysis, exploratory and confirmatory factor analysis. Ideal for Business and Management students reading for a Master’s degree, each book in the series may also serve as reference books for doctoral students and faculty members interested in the method. Part of SAGE’s Mastering Business Research Methods series, conceived and edited by Bill Lee, Mark N. K. Saunders and Vadake K. Narayanan and designed to support researchers by providing in-depth and practical guidance on using a chosen method of data collection or analysis.




Qualitative and Quantitative Economics (Q2E)


Book Description

Clark and Fast invented and created Qualitative Economics because people need to know what they are doing through interactions in their daily life. Economics is not just about statistics, data and numbers, but more about the meaning of this information. We provide everything from the definition of Qualitative Economics (QE) to the use of it in our daily lives, which includes everything we do daily from families, relatives and friends to work, vacations and hobbies.




Doing Quantitative Research in Education with SPSS


Book Description

This accessible and authoritative introduction is essential for education students and researchers needing to use quantitative methods for the first time. Using datasets from real-life educational research and avoiding the use of mathematical formulae, the author guides students through the essential techniques that they will need to know, explaining each procedure using the latest version of SPSS. The datasets can also be downloaded from the book′s website, enabling students to practice the techniques for themselves. This revised and updated second edition now also includes more advanced methods such as log linear analysis, logistic regression, and canonical correlation. Written specifically for those with no prior experience of quantitative research, this book is ideal for education students and researchers in this field.




Analysing Quantitative Data for Business and Management Students


Book Description

In Analysing Quantitative Data, Charles A. Scherbaum and Kristen M. Shockley guide the reader through Understanding Quantitative Data Analysis, Basic Components of Quantitative Data Analysis, Conducting Quantitative Data Analysis, Examples of Quantitative Data Analysis and Conclusions. An appendix contains Excel Formulas. Ideal for Business and Management students reading for a Master’s degree, each book in the series may also serve as reference books for doctoral students and faculty members interested in the method. Part of SAGE’s Mastering Business Research Methods Series, conceived and edited by Bill Lee, Mark N. K. Saunders and Vadake K. Narayanan and designed to support researchers by providing in-depth and practical guidance on using a chosen method of data collection or analysis.




Social Science Research


Book Description

This book is designed to introduce doctoral and graduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. This book is currently used as a research text at universities on six continents and will shortly be available in nine different languages.




Quantitative Economics with R


Book Description

This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham’s tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader’s R skills are gradually honed, with the help of “your turn” exercises. At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inference is illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods—generalized additive models and random forests (an important and versatile machine learning method)—are introduced intuitively with applications. The book will be of great interest to economists—students, teachers, and researchers alike—who want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively, while also equipping them with key data science skills.