Empirical research requires the collection and analysis of data and other information. The quality of the research (and the conclusions derived therefrom) depend upon the collection of appropriate data, the quality of the data collected, and on how well the data are analysed. Quantitative research requires the measurement and enumeration of the variables to be used in the analysis. In this chapter, we first explain the process of operationalization, by which researchers decide how to measure the theoretical concepts they use. The second section considers different scales of measurement, and highlights some of the implications for empirical analysis. The third section focuses on the measurement of multi-dimensional variables, and the generation of latent constructs. The fourth section addresses how to assess the reliability and validity of variables and multi-dimensional constructs. The fifth section offers some practical suggestions for improving the measurement of the variables used in quantitative research, whilst the final section is concerned with measurements in qualitative research.

In cases of international or cross-cultural research we need to take extra care at every stage of the process, and this chapter looks at various aspects of this. Research involving unfamiliar environmental and cultural differences may complicate the understanding of the research problem, and researchers often fail to anticipate the impact of local cultures on the question asked. Consideration also needs to be given to the scope and limits of the problem. In some cultures, a broader scope is necessary to cover the necessary variables. Comparability of data is the main issue in international/cross-cultural research, and it is not possible to use data gathered in one market for another market. This is due not just to the availability and reliability of data but also to the manner in which data are collected and analysed.

This chapter deals with some conceptual (theoretical) foundations of research. Practical business research is often thought of as collecting data from various statistical publications, constructing questionnaires, and analysing data by using computers. Research, however, also comprises a variety of important, non-empirical tasks, such as finding/‘constructing’ a precise problem, and developing perspectives or models to represent the problem under scrutiny. In fact, such aspects of research are often the most crucial and skill demanding. The quality of the work done at the conceptual (theoretical) level largely determines the quality of the final empirical research. This is also the case in practical business research. Important topics focused on in this chapter are the research process and the role of concepts and theory.

The research design is the overall plan for relating the conceptual research problem to relevant and practicable empirical research. In other words, the research design provides a plan or a framework for data collection and its analysis. It reveals the type of research (e.g. exploratory, descriptive, or causal) and the priorities of the researcher. The research methods, on the other hand, refer to the techniques used to collect and analyse data. This chapter looks at a variety of research designs and methods, as well as at the concept of validity.

The appropriate method of data analysis depends upon a variety of factors that have been specified in the research question and as part of the research design. One key issue is whether the data are qualitative or quantitative, and this depends upon the underlying research approach. If the research approach is deductive, then most of the data are likely to be expressed as numbers and the key issue will be selecting the appropriate statistical techniques for describing and analysing the data. In this chapter, we will concentrate on techniques for describing quantitative data and for providing simple preliminary analyses.

The research design is the overall plan for relating the conceptual research problem to relevant and practicable empirical research. In other words, the research design provides a plan or a framework for data collection and its analysis. It reveals the type of research (e.g. exploratory, descriptive, or causal) and the priorities of the researcher. The research methods, on the other hand, refer to the techniques used to collect and analyse data. This chapter looks at a variety of research designs and methods, as well as at the concept of validity.

In this chapter, we first provide a detailed discussion of the advantages and disadvantages of collecting and using secondary data, and highlight some important secondary data sources. The next section then considers the advantages and disadvantages of collecting and using primary data. The following three sections are devoted to sampling. With secondary data, the researcher is obliged to accept the data that are publicly available, and is not able to influence how the data are collected or how much data are collected. In contrast, the researcher collecting primary data needs to decide whether to survey the entire population or just a sample, to choose an appropriate sampling procedure, and to determine the sample size that will assure a satisfactory level of precision in the subsequent empirical analysis. The final two sections are then devoted to undertaking the two most common methods of primary data collection, namely questionnaire surveys and experiments.

In this chapter, we first provide a detailed discussion of the advantages and disadvantages of collecting and using secondary data, and highlight some important secondary data sources. The next section then considers the advantages and disadvantages of collecting and using primary data. The following three sections are devoted to sampling. With secondary data, the researcher is obliged to accept the data that are publicly available, and is not able to influence how the data are collected or how much data are collected. In contrast, the researcher collecting primary data needs to decide whether to survey the entire population or just a sample, to choose an appropriate sampling procedure, and to determine the sample size that will assure a satisfactory level of precision in the subsequent empirical analysis. The final two sections are then devoted to undertaking the two most common methods of primary data collection, namely questionnaire surveys and experiments.

Information technology has played a substantial role in healthcare since the 1960s. Its adoption has rapidly progressed. In 2007, only 2.2 percent of American hospitals had mature electronic medical record systems, while by 2017, 73.1 percent had mature systems. This chapter aims to inform readers about the historical drivers of health information technology (IT) adoption, as well as about the status of the industry today. The chapter provides detailed explanations of how health IT is being used by healthcare providers, payers, and consumers. Multistakeholder products and services are discussed, and the health information technology services industry is explored. The chapter concludes with a discussion of international adoption of health information technology, the size and growth of the industry, and the future of the health IT industry.