Lessons in Failure: Applying an Organizational Learning Framework to Understanding Attitudes Towards Failure in Development

Reflective questioning to guide socially just global health reform: a narrative review and expert elicitation

Recent research has highlighted the impacts of colonialism and racism in global health, yet few studies have presented concrete steps toward addressing the problems. We conducted a narrative review to identify published evidence that documented guiding frameworks for enhancing equity and inclusion in global health research and practice (GHRP). Based on this narrative review, we developed a questionnaire with a series of reflection questions related on commonly reported challenges related to diversity, inclusion, equity, and power imbalances. To reach consensus on a set of priority questions relevant to each theme, the questionnaire was sent to a sample of 18 global health experts virtually and two rounds of iterations were conducted. Results identified eight thematic areas and 19 reflective questions that can assist global health researchers and practitioners striving to implement socially just global health reforms. Key elements identified for improving GHRP include: (1) aiming to understand the historical context and power dynamics within the areas touched by the program; (2) promoting and mobilizing local stakeholders and leadership and ensuring measures for their participation in decision-making; (3) ensuring that knowledge products are co-produced and more equitably accessible; (4) establishing a more holistic feedback and accountability system to understand needed reforms based on local perspectives; and (5) applying systems thinking to addressing challenges and encouraging approaches that can be sustained long-term. GHRP professionals should reflect more deeply on how their goals align with those of their in-country collaborators. The consistent application of reflective processes has the potential to shift GHRP towards increased equity.

A new strategic approach for R&D project portfolio selection using efficiency-uncertainty maps

Purpose

New business practices and the globalization of markets force firms to take innovation as the fundamental pillar of their competitive strategy. Research and Development (R&D) plays a vital role in innovation. As technology advances and product life cycles become shorter, firms rely on R&D as a strategy to invigorate innovation. R&D project portfolio selection is a complex and challenging task. Despite the management's efforts to implement the best project portfolio selection practices, many projects continue to fail or miss their target. The problem is that selecting R&D projects requires a deep understanding of strategic vision and technical capabilities. However, many decision-makers lack technological insight or strategic vision. This article aims to provide a method to capitalize on the expertise of R&D professionals to assist managers in making informed and effective decisions. It also provides a framework for aligning the portfolio of R&D projects with the organizational vision and mission.

Design/methodology/approach

This article proposes a new strategic approach for R&D project portfolio selection using efficiency-uncertainty maps.

Findings

The proposed strategy plane helps decision-makers align R&D project portfolios with their strategies to combine a strategic view and numerical analysis in this research. The proposed strategy plane consists of four areas: Exploitation Zone, Challenge Zone, Desperation Zone and Discretion Zone. Mapping the project into this strategic plane would help decision-makers align their project portfolio according to the corporate perspectives.

Originality/value

The new approach combines the efficiency and uncertainty dimensions in portfolio selection into an integrated framework that: (i) provides a complete representation of the stochastic decision-making processes, (ii) models the endogenous uncertainty inherent in the project selection process and (iii) proposes a computationally practical and visually unique solution procedure for classifying desirable and undesirable R&D projects.