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This chapter investigates the role of authentic leadership in international entrepreneurship. The authors examine how the four pillars of authentic leadership – self-awareness, relational transparency, internalised moral perspective and balanced processing of information – can promote effective collaboration for cross-border social value creation in entrepreneurial ventures. Questions that the authors address are: How do we define ‘international’ entrepreneurship from the perspective of authentic leadership? Are new or different leadership qualities required for the ‘international’ dimension? What are international leadership values or/and qualities and how does the international context change what competencies are needed? The authors call for research to examine how leadership can be depersonalised and become collective rather than an individual trait.

It’s been thirty years since the original articulation of “Appreciative Inquiry in Organizational Life” was written in collaboration with my remarkable mentor Suresh Srivastva (Cooperrider & Srivastva, 1987). That article – first published in Research in Organization Development and Change – generated more experimentation in the field, more academic excitement, and more innovation than anything we had ever written. As the passage of time has enabled me to look more closely at what was written, I feel both a deep satisfaction with the seed vision and scholarly logic offered for Appreciative Inquiry, as well as well as the enormous impact and continuing reverberation. Following the tradition of authors such as Carl Rogers who have re-issued their favorite works but have also added brief reflections on key points of emphasis, clarification, or editorial commentary I am presenting the article by David Cooperrider (myself) and the late Suresh Srivastva in its entirety, but also with new horizon insights. In particular I write with excitement and anticipation of a new OD – what my colleagues and I are calling the next “IPOD” that is, innovation-inspired positive OD that brings AI’s gift of new eyes together in common cause with several other movements in the human sciences: the strengths revolution in management; the positive pscyhology and positive organizational scholarship movements; the design thinking explosion; and the biomimicry field which is all about an appreciative eye toward billions of years of nature’s wisdom and innovation inspired by life.

This article presents a conceptual refigurationy of action-research based on a “sociorationalist” view of science. The position that is developed can be summarized as follows: For action-research to reach its potential as a vehicle for social innovation it needs to begin advancing theoretical knowledge of consequence; that good theory may be one of the best means human beings have for affecting change in a postindustrial world; that the discipline’s steadfast commitment to a problem solving view of the world acts as a primary constraint on its imagination and contribution to knowledge; that appreciative inquiry represents a viable complement to conventional forms of action-research; and finally, that through our assumptions and choice of method we largely create the world we later discover.

It’s been nearly 30 years since the original articulation of Appreciative Inquiry in Organizational Life was written in collaboration with my remarkable mentor Suresh Srivastva (Cooperrider & Srivastva, 1987). That article generated more experimentation in the field, more academic excitement, and more innovation than anything we had ever written. As the passage of time has enabled me to look more closely at what was written, I feel both a deep satisfaction with the seed vision and scholarly logic offered for Appreciative Inquiry (AI), as well as well as the enormous impact and reverberation. Following the tradition of authors such as Carl Rogers who have re-issued their favorite works but have also added brief reflections on key points of emphasis, clarification, or editorial commentary we have decided to issue a reprint the early article by David L. Cooperrider and the late Suresh Srivastva in its entirety, but also with contemporary comments embedded. To be sure the comments offered are brief and serve principally to add points of emphasis to ideas we may have too hurriedly introduced. My comments – placed in indented format along the way – are focused on the content and themes of furthermost relevance to this volume on organizational generativity. In many ways I’ve begun to question today whether there can even be inquiry where there is no appreciation, valuing, or amazement – what the Greeks called thaumazein – the borderline between wonderment and admiration. One learning is that AI’s generativity is not about its methods or tools, but about our cooperative capacity to reunite seeming opposites such as theory as practice, the secular as sacred, and generativity as something beyond positivity or negativity. Appreciation is about valuing the life-giving in ways that serve to inspire our co-constructed future. Inquiry is the experience of mystery, moving beyond the edge of the known to the unknown, which then changes our lives. Taken together, where appreciation and inquiry are wonderfully entangled, we experience knowledge alive and an ever-expansive inauguration of our world to new possibilities.

This article presents a conceptual refiguration of action-research based on a “sociorationalist” view of science. The position that is developed can be summarized as follows: For action-research to reach its potential as a vehicle for social innovation it needs to begin advancing theoretical knowledge of consequence; that good theory may be one of the best means human beings have for affecting change in a postindustrial world; that the discipline's steadfast commitment to a problem solving view of the world acts as a primary constraint on its imagination and contribution to knowledge; that appreciative inquiry represents a viable complement to conventional forms of action-research; and finally, that through our assumptions and choice of method we largely create the world we later discover.

Graduate students often come to statistics courses with varying levels of motivation and previous academic preparation. Within the statistics education literature, there is a growing consensus to guide instructors who want to help their students gain the requisite statistical knowledge so they can conduct their own research and report their results accurately. Recommendations from the literature include using real data, showing worked-out example problems, and providing immediate feedback to allow students to reflect on the correct and incorrect decisions they made in their analyses. This chapter describes the use of expert decision models (EDMs) in two graduate-level statistics courses – multiple regression and structural equation modeling. Decision-Based learning is an effective way to support graduate students’ developing thinking about statistics. In both courses, the students encounter the EDM through a series of assignments which guides students through the process of specifying a statistical model, running that model in Statistical Package for the Social Sciences or Mplus, and interpreting the results. These assignments use real datasets whenever possible and are designed to expose students to various issues they may experience in their research (missing data, violations of assumptions, etc.) and to illustrate how an expert would have adapted to those issues to complete the analysis. The EDM, with its just-in-time, just-enough instruction, helps students navigate these obstacles through guided practice and allows them to develop the conditional knowledge to handle issues that will arise as they carry out their own research.