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This study provides a systematic literature review and categorization of the costs reported in the literature for the introduction of new vaccines, focusing on sub-Saharan Africa within LMICs, where vaccines are highly needed, financial resources are scarce and data are lacking and scattered.

A systematic literature search of PubMed and Web of Science databases was conducted according to the PRISMA requirements. Searches also included the relevant grey literature. In total, 39 studies were selected and nine cost categories were investigated to build a comprehensive framework.

The paper considers nine cost categories that cover the whole life of the vaccine, from its initial study to its full implementation, including for each of them the relevant subcategories. The systematic review, besides providing specific quantitative data and allowing to assess their variability within each category, points out that delivery, program preparation, administration and procurement costs are the most frequently estimated categories, while the cost of the good sold, costs borne by households and costs associated to AEFI are usually overlooked. Data reported on R&D costs and investment in the production plant differ significantly among the selected contributions.

The literature contributions on cost estimation tend to focus on a precise vaccine, a specific geographic area, or to adopt a narrow approach that captures only a subset of the costs. This article presents a rich and inclusive set of the economic quantitative data on immunization costs in limited-resource countries.

Three different pedagogical approaches grounded in three different definitional foundations of entrepreneurship have been compared in relation to their effects on students. They are: (1) “Idea and Artefact-Creation Pedagogy” (IACP), grounded in opportunity identification and creation, (2) “Value-Creation Pedagogy” (VaCP), grounded in value creation and (3) “Venture-Creation Pedagogy” (VeCP), grounded in organisation creation.

Data were collected at 35 different sites where education was deemed to be entrepreneurial and experiential. A quantitative, smartphone app-based data collection method was used alongside a qualitative interview approach. 10,953 short-survey responses were received from 1,048 participants. Responses were used to inform respondent selection and discussion topics, in 291 student and teacher interviews. Comparative analysis was then conducted.

The three approaches resulted in very different outcomes, both in magnitude and in kind. VaCP had strong effects on entrepreneurial competencies, on student motivation and on knowledge and skills acquisition. VeCP had weaker effects on knowledge and skills acquisition. IACP had weak effects on all outcomes probed for. Differences were attributed to variation in prevalence of certain emotional learning events and to variation in purpose as perceived by students.

VaCP could serve as an escape from the potential dilemma faced by many teachers in entrepreneurial education, of being caught between two limiting courses of action; a marginal VeCP approach and a fuzzy IACP one. This could prompt policymakers to reconsider established policies. However, further research in other contexts is needed, to corroborate the extent of differences between these three approaches.

Most impact studies in experiential entrepreneurial education focus only on organisation-creation-based education. This study contributes by investigating entrepreneurial education that is also grounded in two other definitional foundations. Allowance has been made for novel comparative conclusions.

Access to finance, especially by the poor and marginalized section of the population, is a prerequisite for creating employment opportunities, economic growth, poverty reduction and social cohesion. Access to finance makes transactions quicker, cheaper and safer. Most people around the world having an account in a formal financial institution serve as an entry point into formal financial sector. This study aims to analyze the status of financial inclusion in Assam with respect to demographic penetration, geographic penetration and usage ratio, i.e. credit–deposit ratio.

The study covers a period of 12 years from 2007–08 to 2018–19. Both the parametric and non-parametric statistical tools have been used to analyze the various dimensions of financial inclusion.

The study clearly indicates that there is a significant difference between Assam and aggregate India in financial inclusion and the status of Assam is somewhat lower as compared to the aggregate financial inclusion status of India. To achieve a satisfactory level of financial inclusion, it is not enough to open a bank account for the excluded people, but banks must look at flexibility and timeliness in services to offer a complete package to this segment of the population.

The study is a significant attempt to meet the shortcomings and improve banking coverage for achieving financial inclusion.

This paper tests whether Bayesian A/B testing yields better decisions that traditional Neyman-Pearson hypothesis testing. It proposes a model and tests it using a large, multiyear Google Analytics (GA) dataset.

This paper is an empirical study. Competing A/B testing models were used to analyze a large, multiyear dataset of GA dataset for a firm that relies entirely on their website and online transactions for customer engagement and sales.

Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the intellectual property fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits. Frequentist A/B testing identified fraud in bounce rate at 5% significance, and bounces at 10% significance, but was unable to ascertain fraud at the standard significance cutoffs for scientific studies.

None within the scope of the research plan.

Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the IP fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits.

Bayesian A/B testing can derive economically meaningful statistics, whereas frequentist A/B testing only provide p-value’s whose meaning may be hard to grasp, and where misuse is widespread and has been a major topic in metascience. While misuse of p-values in scholarly articles may simply be grist for academic debate, the uncertainty surrounding the meaning of p-values in business analytics actually can cost firms money.

There is very little empirical research in e-commerce that uses Bayesian A/B testing. Almost all corporate testing is done via frequentist Neyman-Pearson methods.