Learning in and from projects: the learning modes and a learning capability model

Abstract

The notion of ‘project delivery’ is well embedded in and across the management and organizational sciences literature – generating a narrative that reflects and recognizes the instrumental nature of projects and programmes in strategy execution. Project management, as a distinct and well-established body of research enquiry, has increasingly sought to focus our attention on the impacts of complexity, risk and uncertainty in projects; the corollary being a desideratum to strengthen our theoretical understanding of how insight and learning from projects may influence improvements to organizational efficiency. The wider literature suggests that organizational learning remains a challenging proposition, particularly in the context of organizations operating in environments of high complexity. In this paper, we enhance the conversation on organizational learning through a series of case studies, generating evidence of thirteen ‘learning modes’. The paper proposes that mature organizations tend to exhibit a greater number of learning modes and that there is a tendency to capture and socialize knowledge with a greater emphasis on the context of the learning situation rather than the learning artefact in isolation. The empirical evidence gathered in this paper forms the basis of a capability model, characterized by the thirteen modes of learning. The model intimates that learning occurs, and is more effective, when knowledge and information are enacted in practice through the learning modes which form a nucleus of the organizational learning capability. The research concludes with a 'call to action' that emphasizes the strategic importance of learning practices and routines in project oriented-organizations.     

Global Governance,neoliberalism and national responses: The case of Bangladesh’s ready‐made garment (RMG) sector

This article on the ready‐made garment (RMG) sector of Bangladesh shows how over‐reliance on foreign capital for development financing and deregulated investment—a hallmark of neoliberal economic arrangements—undermines the incorporation of SDGs’ and INGOs’ equity principles, contributing to biased policy responses yielding unequal outcomes. The article cautions that while countries prioritize economic growth over social and environmental nourishment and continue to adopt neoliberal economic policies to promote economic growth, inequity is unavoidable, if not inevitable. Thus, the way forward may be to shift the focus of ‘development’ from the economy to society, to building ‘good societies’ where institutions and strategies, including those that contribute to economic growth, are organized such that these complement not compromise the evolution of such societies.     

Auditing biochemical research data: A case study

There is little documentation in the literature of how to conduct a data audit of laboratory research. The purpose of such an audit is to verify the extent to which the published data agree with the corresponding laboratory notebook data and whether the data thus examined appear to be sufficient to justify the published conclusions. A case study is presented, which demonstrates a step by step approach towards conducting a data audit of a published research study.     

Concurrent consideration of product design,process planning and production planning activities

In manufacturing engineering, product design, process planning and production planning activities are often considered independently. However, in order to effectively respond to changes in business situations, such as changes in demand forecast, product mix and technology, it is desirable to consider them concurrently. For this purpose, a large-scale linear programming model has been developed. The model considers minimization of the sum of processing cost, late shipment cost and inventory holding cost as the objective, and concurrently selects product designs, and generates process plans and production plans. The number of columns in the formulation can be large and, hence, an efficient column generation scheme is developed to solve the model. The model and solution procedure are illustrated with examples.     

Accountability in research using persons with mental illness

Although medical research involving the use of persons with mental illness is critically important, in order for the research to be ethical and legal there are certain considerations and restrictions which should be immediately readdressed in order to insure that the welfare of these vulnerable research subjects is protected, and their best interests are assured. A brief historical examination of medical research codes, guidelines, recommendations and Federal Regulations reveals the various considerations and restrictions on informed consent and accountability applicable to the use of persons with mental illness in medical research. Several concerns are raised about how these considerations and restrictions have been interpreted, and specific recommendations are offered to improve them immediately by means of representation from consumers and/or their families, and organizations, e.g., NAMI members.     

Which ethics for science and public policy?

The problem of inaccurate, misapplied or fraudulent scientific data could be addressed by government regulations, or by self‐regulation from within science itself. To many, self‐regulation implies the grounding of research activities in some “neutral”; standard of “ethics”; acceptable in a “pluralistic”; society. Yet, there is no such thing as a “neutral ethics”; and many “contemporary”; theories contain such serious theoretical deficiencies and contradictions that they are practically inapplicable. As a viable alternative to these theoretical and practical problems, an objectively based realistic framework of ethics is considered, and used to ground both the individual scientific and the collective public policy decision making processes. This is an ethics of properly integrated relationships. It is then applied to an analysis of many of the causes of incorrect scientific data, as well as of many of the internal and external pressures and abuses often experienced by scientists today. This approach respects the integrity of each decision maker as a human being and a moral agent—which in turn better insures the integrity of the protocol, the data, and the public policy decisions which follow—and ultimately, the integrity of the scientific enterprise itself. The alternative is government regulations.     

Data Audit as a Way to Prevent/Contain Misconduct

Research misconduct is frequently in the media headlines. There is consensus among leading experts on research integrity that the prevalence of misconduct in research is at least 1%, and shoddy work may even go over 5%. Unfortunately, misconduct in research impacts all walks of life from drugs to human subject protections, innovations, economy, policy, and even our national security. The main method of detecting research misconduct depends primarily on whistleblowers. The current regulations are insufficient since dependence on whistleblowers manifests itself as an accidental hit or miss. No other endeavor in our society depends on such a poor system of discovery of misconduct to remedy it. Nearly a quarter of a century ago, I proposed data audit as a means to prevent/contain research misconduct. The audit has to protect the creative process and be non-obtrusive. Data audit evaluates the degree of correspondence of published data with the source data. The proposed data audit does not require any changes in the way researchers carry out their work.