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Outlook This book has aimed to offer a business manager’s perspective on how to boost productivity in higher education so as to ensure broad access to affordable and high-quality educational services. It is perhaps best seen as an attempt to sketch a rough map of uncharted territory. Much research still needs to be completed in order to arrive at a more solid understanding of how the actions of academic managers and policymakers influence productivity in higher education. Given the huge size of the global higher education industry, the crucial role of higher education in driving social and economic progress, and the significant potential for improvement, this is clearly an exciting field of research. Future scholarly contributions to develop this field are likely to fall into one of three generic categories: (1) the generation of new hypotheses, (2) the testing of existing hypotheses, and (3) the transfer of existing hypotheses to new contexts. The generation of new hypotheses. While an attempt has been made in this book to consider a broad range of managerial practices, the ideas presented here cover only a small subset of promising approaches to boost productivity in higher education. Over the past decades, management scholars from across the globe have built a vast store of knowledge that has been successfully applied by business enterprises in different industries and that may be relevant for academic managers as well. The translation of this knowledge into the realm of higher education requires expertise from several fields, so it should ideally be pursued as an interdisciplinary endeavor involving scholars of higher education, management scholars, economists, and public policy scholars. One example of such an approach – albeit targeted at urban schools districts rather than institutions of higher education – is the Public Education Leadership Program, launched in 2004 by faculty members from Harvard Business School and Harvard’s Graduate School of Education. 880 Whether as an interdisciplinary initiative or not, one of the top priorities of any effort to generate new hypotheses on how to boost productivity in higher education should be to build a library of detailed case studies. After all, as Clayton Christensen and Michael Raynor have pointed out, breakthroughs in management theory tend not to result from the crunching of vast amounts of data to correlate attributes and outcomes with ever increasing degrees of statistical significance, but rather from highly detailed field research that fosters a gradual conceptual understanding of the causal links between different phenomena. 881 Unfortunately, such case studies are exceedingly rare in higher education. 882 The testing of existing hypotheses. It is not enough to merely generate hypotheses, of course, as elegant and plausible as they might be. At some point, these hypotheses must be subjected to rigorous statistical analyses in order to ascertain whether they withstand the test of reality. This may sound straightforward enough, but researchers will face a few challenges 880 See Datar, S. M./Garvin, D. A./Knoop, C.-I. (2008a), p. 11. 881 See Christensen, C. M./Raynor, M. E. (2003b), pp. 68-71. 882 See Keller, G. (2004), p. 110. C. Schierenbeck, Fixing Higher Education, DOI 10.1007/978-3-658-00213-8, © Springer Fachmedien Wiesbaden 2013

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Outlook This book has aimed to offer a business manager’s perspective on how to boost productivity in higher education so as to ensure broad access to affordable and high-quality educational services. It is perhaps best seen as an attempt to sketch a rough map of uncharted territory. Much research still needs to be completed in order to arrive at a more solid understanding of how the actions of academic managers and policymakers influence productivity in higher education. Given the huge size of the global higher education industry, the crucial role of higher education in driving social and economic progress, and the significant potential for improvement, this is clearly an exciting field of research. Future scholarly contributions to develop this field are likely to fall into one of three generic categories: (1) the generation of new hypotheses, (2) the testing of existing hypotheses, and (3) the transfer of existing hypotheses to new contexts.

The generation of new hypotheses. While an attempt has been made in this book to consider a broad range of managerial practices, the ideas presented here cover only a small subset of promising approaches to boost productivity in higher education. Over the past decades, management scholars from across the globe have built a vast store of knowledge that has been successfully applied by business enterprises in different industries and that may be relevant for academic managers as well. The translation of this knowledge into the realm of higher education requires expertise from several fields, so it should ideally be pursued as an interdisciplinary endeavor involving scholars of higher education, management scholars, economists, and public policy scholars. One example of such an approach – albeit targeted at urban schools districts rather than institutions of higher education – is the Public Education Leadership Program, launched in 2004 by faculty members from Harvard Business School and Harvard’s Graduate School of Education.880 Whether as an interdisciplinary initiative or not, one of the top priorities of any effort to generate new hypotheses on how to boost productivity in higher education should be to build a library of detailed case studies. After all, as Clayton Christensen and Michael Raynor have pointed out, breakthroughs in management theory tend not to result from the crunching of vast amounts of data to correlate attributes and outcomes with ever increasing degrees of statistical significance, but rather from highly detailed field research that fosters a gradual conceptual understanding of the causal links between different phenomena.881 Unfortunately, such case studies are exceedingly rare in higher education.882

The testing of existing hypotheses. It is not enough to merely generate hypotheses, of course, as elegant and plausible as they might be. At some point, these hypotheses must be subjected to rigorous statistical analyses in order to ascertain whether they withstand the test of reality. This may sound straightforward enough, but researchers will face a few challenges

880 See Datar, S. M./Garvin, D. A./Knoop, C.-I. (2008a), p. 11. 881 See Christensen, C. M./Raynor, M. E. (2003b), pp. 68-71. 882 See Keller, G. (2004), p. 110.

C. Schierenbeck, Fixing Higher Education,DOI 10.1007/978-3-658-00213-8, © Springer Fachmedien Wiesbaden 2013

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in this undertaking. Perhaps most obviously, researchers will have to apply a bit of creativity in order to measure and quantify the effect of individual managerial practices and policy interventions on productivity, especially as regards educational impact. Less obviously, researchers need to avoid making inferences from narrow observations in traditional higher education to the entire higher education industry. Many promising managerial practices, for example, are likely to produce disastrous results if applied within the institutional context of traditional higher education. This does not mean, however, that these practices are flawed per se. After all, they may very well prove effective within the institutional context of for-profit institutions. Likewise, researchers should try to control for the effects of regulatory constraints on the effectiveness of certain managerial practices. If an individual managerial practice likely to improve productivity in higher education smashes on the rocks of input-based regulation, for example, the adequate response is surely not to ditch the practice, but to consider adjusting the regulatory framework.

The transfer of existing hypotheses to new contexts. Finally, as researchers accumulate a robust store of knowledge about ways to improve productivity in higher education, they should consider applying their findings to new contexts. Perhaps the most obvious target for such an effort would be primary and secondary education. Conveniently, the same definition of productivity used to guide the search for and evaluate the effectiveness of management best practices and policy interventions in higher education could also be applied to the management of schools. What’s more, primary and secondary education have also seen the rise of a vibrant for-profit sector883 which might offer researchers a starting point for quantifying a possible productivity gap and identifying promising individual managerial practices.

Even though substantial research still needs to be completed, academic managers and policymakers should not necessarily hold their breath until scholars have gathered the last piece of statistically significant evidence needed to confirm the effectiveness of individual managerial practices or policy interventions. Gold-seekers who hit it big in the mid-19th century California Gold Rush did not wait for pristine roads to be paved before heading west. Likewise, academic managers and policymakers eager to play a leading role in the closing of higher education’s gaping productivity gap should start experimenting with new managerial practices and policy interventions as soon as possible. It is hoped that the ideas presented in this book will offer them a place to start.

883 Examples include Britain’s Cognita, Germany’s Phorms, and Sweden’s Kunskapsskolan; see Griffiths, S.

(2004); Köckritz, A. (2007); and N. N. (2008d).

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