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 most commonly used technique for the analysis of quantitative data in business research is multiple regression analysis. This is a powerful technique for understanding the relationships between variables, which variables have the most impact, and for prediction. In this chapter, we consider how to specify regression models, how to estimate the models, and how to use the estimated models to undertake some simple hypothesis tests. We emphasize that the researcher has to exercise his/her judgement in deciding not only the specification of the initial model but also in how to adapt and interpret the model in response to the various statistical tests.

Product innovation is the central strategic challenge in the healthcare value chain. Companies in each of the sectors covered in this volume – pharmaceuticals, biotechnology, medical devices, and information technology – compete largely on their rate of innovation and the innovativeness of the new products they make. Start-up companies live and die based on their ability to develop new products and therapies that meet needs not satisfied by larger incumbents. This chapter examines the similarities and differences across these sectors, the commonalities they all face in the innovation process, the basis for the imperative to develop new technologies, and the common challenges facing firms in these sectors. Finally, the chapter explains why everyone studying the healthcare industry needs to know more about these technology-based sectors and their impact upon the rest of the healthcare industry and the economy.

In business studies most researchers need to collect some primary data to answer their research question. This entails deciding what kind of data collection method to use, which depends upon an overall judgement on which type of data is needed for a particular research problem. One important aspect is to identify the scope of the study and unit of analysis and what type of analysis is needed. After looking briefly at the chief differences between quantitative and qualitative approaches, the chapter looks at different qualitative methods and when to use them.

Qualitative research imposes specific analytical challenges. This chapter addresses important characteristics of qualitative research and qualitative data. Strategies and procedures to handle the analytical challenges are also dealt with, as well as validity and reliability issues in qualitative research.

This chapter explains what we mean by research in business studies and to discuss differences between systematic research and common sense or practical problem solving. It looks at what we mean by knowledge and why we do research, examining different research orientations and approaches and the influence of the researcher’s background and basic beliefs concerning research methods and processes. We stress the importance of learning to think and work systematically and developing analytical capabilities in order to produce accurate and reliable results. We also discuss researchers’ moral responsibility towards both their subjects and the readers of their reports.

Problems, that is ‘questions’, drive research. Without research questions there would hardly be any research at all. Research problems are not ‘given’, however; they are detected and constructed. How research problems are captured and framed drives subsequent research activities. In normal research situations, we first select a topic and then formulate a research problem within that topic. The process of constructing a research problem is not straightforward and often involves a lot of back-and-forth adjustments and refinement. In this chapter we particularly focus on how to construct and adequately capture research problems. The role of reviewing past literature to identify weaknesses and gaps is also examined.

A huge array of statistical methods are available to the researcher, of variable levels of sophistication, and a comprehensive survey would be well beyond the scope of this textbook. Here we outline three methods which are widely used in business studies research, namely factor analysis, structural equation modelling, and event study analysis. In each case, we explain the key elements of each method, the underlying intuition, and how to interpret the results, and then provide an example from the business literature.

Medical devices and medical technology, with worldwide revenues of roughly $330 billion, comprise an important segment within healthcare. This broad set of products, ranging from extraordinarily complex implantable defibrillators to metal mesh stents to hip and knee implants, have truly advanced the practice of medicine and represent life-saving therapies to patients in need. Growth, in recent years, while slower than that of the 1990s when several entirely new therapeutic categories emerged, continues at a good pace. The industry is increasingly dominated by large companies such as Medtronic, Abbott, Johnson & Johnson, and Stryker which offer a broad mix of technologies in multiple anatomies and diseases. In as much as structural developments, including reimbursement and the containment of healthcare costs, they have made it more difficult for single product/single anatomy companies to flourish. Those that provide truly innovative products that are treatment-altering can succeed and remain independent. Indeed, there exist several examples – in areas such as diabetes, heart failure, and neurological diseases. Furthermore, the industry remains highly profitable – companies on average enjoy operating margins in the mid-twenties, considerably higher than nearly every other industry. We anticipate continued growth for the sector as devices and technology play an expanded role in healthcare. Of the US $3.6 trillion healthcare spend, medical technology represents less than 5 percent on a revenue basis.

The most commonly used technique for the analysis of quantitative data in business research is multiple regression analysis. This is a powerful technique for understanding the relationships between variables, which variables have the most impact, and for prediction. In this chapter, we consider how to specify regression models, how to estimate the models, and how to use the estimated models to undertake some simple hypothesis tests. We emphasize that the researcher has to exercise his/her judgement in deciding not only the specification of the initial model but also in how to adapt and interpret the model in response to the various statistical tests.