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This study compares the efficiency of two analytic approaches—qualitative and quantitative—to social network analysis for identifying stakeholder groups. Social network data were collected from 23 water and agriculture stakeholders in Arizona, USA, and analyzed quantitatively and qualitatively. Analysis of the sample in the original order of data collection found qualitative analysis was more efficient, in that it yielded a stable result—the identification of four stakeholder groups—within 16 interviews. In contrast, the quantitative analysis did not produce a stable result after 23 interviews. Repeated analyses with randomized order and reverse order samples found qualitative approaches yielded more stable results, took about the same number of interviews to yield results, and produced slightly fewer stakeholder groups compared to quantitative approaches. Our findings suggest that, in resource-constrained projects, qualitative social network analysis for identifying stakeholder groups can provide an efficient alternative to conventional quantitative social network analysis.

Purpose – The purpose of this paper is to describe the present interest in the current and predicted business models of Massive Open Online Courses (MOOC); the business models that are being used by MOOC platforms and MOOC providers; discussion of the key issues and challenges; predictions about the future MOOC business models from the perspectives of platforms, providers and consumers. Design/methodology/approach – Review of the current literature for business models and costs for MOOCs and including the business models of higher education and possible future of the higher education system. Findings – The findings of this paper show that most MOOCs are currently based upon a “freemium” model where content is provided freely and additional services are charged for; that there are already a range of different “flavours” of MOOC and that this range is likely to further develop over time with some clear winners emerging; and that completely free and open MOOCs are not likely to be the focus of growth in the future, rather MOOCs that are sustained through charges to customers. Practical implications – To inform the debate about likely future business models and to help greater understanding of possible business models for MOOCs to inform decision-making by managers at universities. Originality/value – To inform the debate about likely future business models and to help greater understanding of possible business models for MOOCs to inform decision-making by managers at universities.

This case study is based upon institutions that have a history of engagement with online and distance learning (ODL) that has developed over a number of years and has formed a key part of their organizational strategy. They have developed expertise in ODL by investing in the creation of fully online courses, in some cases for more than a decade. They are now investing in Massive Open Online Courses (MOOCs) and the case study explores how they are going about this. This type of institutions has many advantages in its readiness to follow new online courses in MOOC formats but may face particular challenges to its business model. The case study describes the decision-making that took place before investment in MOOCs was made, approaches to quality assurance, resourcing, partnerships, and accreditation.

This chapter describes the “institutional innovator” case study of MOOC development. In this case, the university has leadership that supports innovation at many different levels in the organization, and which supports its senior team and next tier of management to make decisions about new approaches to test out and adopt. This has led to innovation at two levels: larger-scale and more dramatic “step change” innovations when appropriate and more gradual, continuous improvement in other areas. The case study describes the strategic context and how MOOCs are becoming part of the university's strategy. It describes how decisions were made about investing in MOOCs, how governance works, resourcing issues, partnerships, and quality assurance.

This chapter describes a case study of an imaginary university that is investing in the development of MOOCs. This group is termed the “cautious experimenters”—institutions that are in the top layer of universities globally in terms of national and international rankings, based mostly upon their research activity. They tend to be risk averse and wish to protect their current strong and stable position rather than to invest in innovation, but they also wish to keep up with their main competitors and not be left behind, so they are engaging cautiously with MOOCs and seeking to identify the benefits that they may bring in the future. The case study describes the decision making that took place before investment in MOOCs was made, approaches to quality assurance, resourcing, partnerships, and accreditation.

This chapter analyses and draws out the main conclusions from six case studies of MOOC implementation by universities. The case studies focus upon six types of universities: the entrepreneurs, the opportunists, the institutional innovators, the old hands with online and distance learning, those who are cautious about implementing MOOCs, and the “old guard” who do not wish to engage with MOOCs. It describes the most significant decisions that universities are making about MOOCs and describes the approaches that they are taking to planning and implementing them.

The benefits of providing MOOCs are not just about income generation and research has shown that many universities are investing in MOOCs with the hope of longer-term benefits rather than just short-term income generation. This chapter discusses some of the most frequently cited reasons for MOOC investment that were given by universities interviewed during this research activity. These “softer” outcomes included relationship building, profile-raising and fulfilment of strategies for public engagement and public good and similar objectives, and there is also the motivation to engage in practical experiments that may involve many thousands of online learners in order to get a better understanding of what works well in online learning. Universities need to think strategically when they are planning their MOOC activity in order to be clear what they plan to achieve and how they can get most benefit from the investment of time and money. The chapter includes examples of how specific universities are getting additional value from their MOOC investment.

This chapter explores the design of massive Open Online Courses (MOOCs) from a learner's perspective. It discusses key issues including the motivation of learners in an online environment; it describes the concept of “learning design” and how it can be used to help plan online learning, and how to use learning design approaches to plan the design of MOOCs. It talks about learner support in the MOOC environment and the issues to consider in providing support to MOOC learners. Finally, it discusses MOOC content and some of the issues relating to reusing existing content in MOOCs, and so-called “flipping the classroom.”

This chapter describes the costings that may be involved in creating and running a MOOC. It goes beyond the ballpark estimates that are being shared by some MOOC providers to analyze the activites that are involved in running MOOCs and suggest ways to estimate potential costs, using the Stanford analytics framework that describes a MOOC in terms of interactive learning environment; learner background and intentions; technology infrastructure; and evidence-based improvement. The chapter includes a checklist to use for estimating MOOC costs to help business planning.

This chapter draws upon the experience gained by universities in creating MOOCs. It draws upon six case studies to provide guidance on the main steps involved in devising and implementing an MOOC strategy. It is based upon established good practice for program and project management, using the terminology that is common to the PRINCE2 and Managing Successful Programmes management methodologies.