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.

In this chapter, we focus on the role that innovation plays in the medtech ecosystem, including how innovation is financed by early-stage venture capital investors and valued by other stakeholders in the ecosystem. Using specific case studies and empirical data, we review the impact that innovation has on medical device companies’ revenue growth through market expansion and derivative product sales, and the ways in which medical device companies are valued by early-stage venture capital investors, public market investors, and strategic acquirers. In addition, there is a focus on the types of medtech product and service innovations that have driven the greatest value in the market, and the process of developing new technologies from ideation and invention to clinical evidence generation and regulatory approval, and then all of the way to widespread market adoption.

After completing the data collection and analysis, the research problem, the data collected, and the findings need to be presented in a logical, consistent, and persuasive report. This chapters outlines a typical format for such a research report, and describes the contents of each section. It also discusses oral presentations and writing for publication.

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.

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 outlines the purpose, scope, and structure of the book and introduces the scientific, data-driven approach to analysing and solving business problems and conducting business research.

Strategy and structure in the current biopharmaceutical industry are a legacy of business conditions that no longer exist. As conditions change, strategy and structure must adapt. The typical large biopharmaceutical company accounts for a tiny (about 1 percent, and shrinking) share of total global biomedical innovation, yet fills its development portfolio with its own internal discoveries. Companies are spending heavily on their own leads, rather than on the best leads, with resulting high failure rates in late stage development. Companies often insist on manufacturing their products in-house, leading to low asset utilization rates, under-investment in new manufacturing technologies, and volatile gross margins. Pressure on gross margins is amplified by the recent and relatively sudden loss of real US pricing power. Communicating product attributes to patients, physicians, and payors has shifted from traditional one-way (e.g., print, TV, radio) media in which companies could control messaging to two-way (e.g., Internet, social media) channels in which companies’ voices must share bandwidth with other points of view. These and other profound changes in biopharmaceutical companies’ operating environment call for similarly profound changes in strategy and structure. The challenges are significant, but entirely addressable, and in several cases, successful transitions in other industries (e.g., integrated circuits, film) may be instructive.

Biotech venture creation is a distinct investment strategy within the broader category of venture capital. It is specialized by both stage and target space and includes four elements: (1) sourcing and seed investing; (2) the role of entrepreneurs in residence (EIRs); (3) building and scaling; and (4) business model selection. Sourcing focuses on identifying innovative science that can be translated into novel therapeutic produces. Seed investing is a critical evaluative stage during which early science is pressure tested with focused resources to stringently determine which new opportunities to advance to full funding and business launch. EIRs, experienced R&D and business executives, play a central role during the seed period and often take on leadership roles in companies that move forward. Finally, matching the science to the business model, whether a broad product platform or a more focused asset-centric company, is a defining step which determines the business, financial, operating, and organizational strategies of new biotech companies. Increasingly, as the biotech industry matures, new venture creation is focused in economic clusters. These clusters are supportive ecosystems with large talent, capital, partner, supplier, and other resource pools that foster key transfers of knowledge, assets, and cooperative assets among industry participants. These clusters are seen across industries and are arguably determined by fundamental economic forces. Early examples include the concentration of textile manufacturing during the middle ages and renaissance in specific cities and regions in England and Italy, the concentration of the automobile industry in Detroit, and the concentration of high- tech companies in the Bay Area in California.