Exploring Entrepreneurship (2nd edition)

We have written this book to help you to explore entrepreneurship in all its complexity and variety. Our approach is based on the view that some subjects, such as medicine, engineering, and entrepreneurship, are particularly well-suited to experience-based learning. The basic idea is that people can learn a lot more if they are able to connect the research evidence and the theory to some kind of direct personal experience. The nature of this ‘experience’ depends a great deal on what you are studying. For example, a medical student spends time working in different parts of a hospital, while an engineering student might design a new product or test some materials in a laboratory. Providing practical experience is more difficult for entrepreneurship students, but it is possible to re-create some aspects of a ‘real-life’ experience using new venture exercises, business plan competitions, and computer simulations. In this book, we provide support for all three types of activity. However, experience-based learning is about more than just having an experience. Some of the most important learning happens when practical activity is combined with well-structured reflection. With this in mind, we have designed the book around three related aims: 1. to help you gain essential practical skills and underpinning knowledge, and reflect on the challenges involved in creating an entrepreneurial venture, either individually or as part of a team; 2. to help you develop a deeper understanding of entrepreneurship, as you make connections between your experiences, relevant theoretical concepts, research findings, and the experiences of others; 3. to encourage you to take part in a broader debate about entrepreneurship in the twenty-first century, examining contrasting perspectives on entrepreneurship across a wide range of ventures. Exploring Entrepreneurship covers practical issues related to the creation of an entrepreneurial venture, together with reviews of related research evidence and more theoretical discussion about entrepreneurship. We also make considerable use of case-based examples, so that you can learn from the experiences of real entrepreneurs as they struggle to create and to develop their ventures. It is worth noting two distinctive features of this book. Firstly, it provides detailed coverage of many different types of entrepreneurship. You will find examples of commercial, primarily profit-oriented ventures and what are often termed ‘social’ enterprises, where the primary aim is to address a social or environmental challenge, rather than simply to secure a profit. In contrast to most other texts, it also addresses ‘anti-social’ forms of entrepreneurship, with examples that range from the unethical and environmentally destructive behaviour of legitimate firms to the shady world of organised crime. The argument behind these decisions is simple: entrepreneurial activity is clearly a very powerful force in the world. We think it is important for entrepreneurship students to consider seriously how that power is exercised. In summary, this book offers a fresh, wide-ranging, and up-to-date approach to entrepreneurship, combining practical relevance with critical reflection. We also hope that it will help you to experience something of the excitement, uncertainty, passion, and sheer hard work that is involved in creating a successful entrepreneurial venture

Regression models: Association of high clinical threshold [9] levels of serum IgG to selected periodontal species and incident AD.

<p>Model 1: bivariate association with single periodontal antibody.</p><p>Model 2: controlled for age at phlebotomy (baseline).</p><p>Model 3: Model 2, also controlled for APOE status, gender, and education.</p><p>Model 4: Model 3, also controlled for hypertension, smoking, stroke, and diabetes mellitus.</p><p>*Models shown using pseudolikelihood estimator, robust variance estimator, and weight of cases (1), controls (10) [most conservative method].</p><p>Models highlighted in bold indicate p<0.05. HR: Hazard ratio.</p><p>Regression models: Association of high clinical threshold <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114959#pone.0114959-Dye2" target="_blank">[9]</a> levels of serum IgG to selected periodontal species and incident AD.</p

Regression models: Association of high levels (80^th percentile) of serum IgG to selected periodontal species and incident AD.

<p>Model 1: bivariate association with single periodontal antibody.</p><p>Model 2: controlled for age at phlebotomy (baseline).</p><p>Model 3: Model 2, also controlled for APOE status, gender, and education.</p><p>Model 4: Model 3, also controlled for hypertension, smoking, stroke, and diabetes.</p><p>*Models shown using pseudolikelihood estimator, robust variance estimator, and weight of cases (1), controls (10) [most conservative method]; HR: Hazard ratio.</p><p>Regression models: Association of high levels (80^th percentile) of serum IgG to selected periodontal species and incident AD.</p

Characteristics of the study cohort.

<p>P-values based on t-test for continuous variables, and on chi-square for categorical variables.</p><p>*Case-cohort study v. complete WHICAP cohort.</p><p>**Cases v. controls within the case-cohort study.</p><p>Characteristics of the study cohort.</p

Distribution of serum IgG antibody levels to selected periodontal species.

<p>*Thresholds are drawn from a study of NHANES-III subjects <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114959#pone.0114959-Dye2" target="_blank">[9]</a>; above each individual periodontal pathogen level, the accuracy of a serology-based diagnostic test to detect moderate-severe periodontitis according to the CDC/AAP definition is maximized. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114959#pone.0114959-Page1" target="_blank">[30]</a>.</p><p>Distribution of serum IgG antibody levels to selected periodontal species.</p

Empirical age patterns of survival of the ApoE4 carriers and non-carriers in the FHS.

<p>Patterns are shown for (A and C) men and (B and D) women genotyped in (A and B) FHS and (C and D) FHSO cohorts who carry (E4) and do not carry (NoE4) the ApoE4 allele. The numbers in the insets show the total number of genotyped individuals and the number of deaths among them.</p

Proportions of the ApoE4 allele carriers, mean age at the time of biospecimens collection, and the allele-specific proportions of deaths, CVD, cancer, and ND for the genotyped participants of the FHS, FHSO, and LLFS.

<p>FHS = the Framingham Heart Study (FHS) original cohort; FHSO = the FHS offspring cohort.</p><p>LLFS_P = long-living parental generation of the Long Life Family Study (LLFS) participants; LLFS_O = offspring of the LLFS long-living participants; LLFS_S = spouses of the LLFS long-living participants and their offspring.</p><p>CVD = cardiovascular diseases including diseases of heart and stroke; Cancer = all sites but skin; ND = dementia and Alzheimer disease combined; SD = standard deviation.</p>*<p>proportion of the ApoE4 allele carriers is in percentages;</p>**<p>maximal sample size; the number of individuals with non-missing information on ND is about 1% less in the FHS and about 3% less in the FHSO.</p>***<p>age at biospecimens collection at the 19^th FHS, 4^th FHSO and baseline LLFS examinations.</p><p>The ApoE4-allele-specific proportions of CVD, cancer, and ND are not given for the LLFS because this information was not used in this paper.</p

Cancer-stratified relative risks of death and log-base-10-transformed p-values for the ApoE4 allele carriers compared to the non-carriers.

<p>The risks were evaluated in more homogeneous groups of individuals who died or were right censored at ages 70 to 95 years in the pooled sample of the FHS and FHSO. “No” indicates individuals who did not have non-skin cancers apart from prostate neoplasm in men or breast neoplasm in women. “Yes” indicates individuals who had non-skin cancers other than prostate neoplasm in men or breast neoplasm in women. The models were adjusted for birth cohorts, an indicator of the FHS or FHSO, and additive contribution of CVD and ND. The multiplicative interaction between ApoE and non-sex-specific cancer in women was highly significant (p = 5.2×10⁻³). Thin bars show 95% confidence intervals. Exact numeric values for the estimates and sample size are given in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004141#pgen-1004141-t003" target="_blank">Table 3</a> (non-prostate and non-breast). The solid horizontal line shows the conventional level of significance, i.e., log₁₀(0.05) = −1.3.</p

Disease-stratified relative risks of death for the ApoE4 carriers compared to the non-carriers in the more homogeneous group of the FHS and FHSO participants with lifespans of 70 to 95 years.

*<p>Individuals with missing neurodegenerative disorders (ND) status were excluded in all models.</p><p>CVD = cardiovascular diseases; Cancer includes all sites but skin; Non-prostate indicates non-skin cancers apart from prostate neoplasm in men; Non-breast indicates non-skin cancers apart from breast neoplasm in women;</p><p>RR = relative risk; CI = Confidence interval; N_total and N_died denote the total number of genotyped individuals and the number of deaths among them, respectively.</p><p>All models were adjusted for birth cohorts, an indicator of the FHS or FHSO, and additive contribution of CVD, ND, and cancer, as applicable, e.g., the model for samples stratified by CVD was adjusted by cancer and ND.</p

Kaplan-Meier estimates of life expectancy of the FHS and FHSO women from the more homogeneous group who were aged between 70 and 95 years at death or the end of follow up in 2008 stratified by cancer and the ApoE4 statuses.

<p>LE = life expectancy; CI = confidence interval; N_total and N_died denote the total number of genotyped individuals and the number of deaths among them, respectively.</p