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.

In the chapter “The Biotechnology Sector – Therapeutics”, the author covers a wide range of topics summarizing the significant role that the formation and growth of the biotechnology sector has played in the entire biopharmaceutical industry. The chapter begins with a bit of history, from the earliest days of how genetic engineering gave birth to this sector, and takes the reader through an overview of biotechnology as it exists today and how the growing innovation in science over the years has been able to both drive the sector and have a tremendous impact on healthcare overall. There is a particular focus on describing various types of innovation which have played a huge role in driving product development in the broader biopharmaceutical industry. Later in the chapter, there is a focus on many of the business aspects of the sector, as drug development in biotechnology requires enormous amounts of capital for success. The author outlines many of the key issues related to different business and financing models that we see across the sector, in addition to the unique management issues in small biotechnology companies. There is significant description and explanation of the symbiotic relationship between the larger pharmaceutical companies and smaller biotechnology start-ups with a focus on how they help each other to bring transformative medicines to patients. The chapter concludes with a discussion about international and regulatory aspects impacting the sector. Overall the author tells the story of the birth and growth of this exciting sector, and its impact on patients and drug development over the last forty years, well substantiated with current data to build the case for how biotechnology today plays a major role in driving one of the most important and exciting technological industries of our time.

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.