Hypotheses Development

This chapter offers a rigorous examination of hypothesis development and testing within the quantitative research paradigm, contextualised for the management fields. It starts by placing the quantitative method within its positivist philosophical foundations, explaining how the principles of objectivity, generalisability, and reductionism enlighten the scientific method of hypothesis formulation. The chapter shed light on different statistical techniques, such as t-tests, ANOVA, and SEM, to illustrate the quantitative testing of organisational hypotheses. A significant portion of the analysis is dedicated to the critical issue of hypothesis testing errors, providing a detailed exposition of Type I (false positive) and Type II (false negative) errors, complete with clear, illustrative examples to emphasise their potential impact on managerial decision-making. In addition, the chapter addresses the transformative role of Artificial Intelligence in enhancing hypothesis development, representing how AI can serve as a powerful exploratory tool to identify novel, data-driven research questions from massive datasets, thus enlightening the creative and inductive phases of inquiry. This synergy between AI-driven discovery and conventional statistical validation promises more robust and theoretically sophisticated management research. Finally, the chapter highlights that meticulous research design and methodological rigour are paramount for minimising inferential errors and guaranteeing the credibility of scholarly findings.