130,596 research outputs found
Governing Large Projects: A Three-Stage Process to Get It Right
No full textPrivate and public megaprojects, whether new plant facilities, IT systems, skyscrapers, airports, railways, roads, or the Olympics, are frequently associated with dramatic cost and schedule overruns. The root causes are behavioral biases – such as optimism and deliberate deception – accompanied by principal-agent issues and a lack of project-related skills. Through a three-stage process – i.e., Forecasting, Organizing, and Executing (FOX) – we organize and offer solutions to mitigate the cognitive biases and agency issues planners and policymakers face in large projects. Following the three-stage FOX process and building on Behavioral Decision Theory (BDT), we first review evidence for the accuracy of Reference Class Forecasting (RCF), which considers comparable past projects to forecast a current, planned project. We provide evidence for RCF performance and recent methodological extensions such as Similarity Based Forecasting (SBF). Second, considering the principal-agent and project governance literature, we offer organizational solutions to reduce unfounded optimism and deception, including debiasing techniques and specific measures to curb principal-agent issues. Third, we suggest combining a project modular design with speedy implementation for faster, better, cheaper, and lower-risk execution. Overall, we offer an original, holistic theoretical view that deals with both behavioral and strategic elements of how to debias large projects, along with direct practical implications and advice for those who manage megaprojects with increasingly high stakes and risks
From framework to theory: an evolutionary view of dynamic capabilities and their microfoundations
No full textDynamic capabilities (DCs) are organizations' ability to integrate, build, and reconfigure competences, on which they draw to adapt to changes. Despite a significant stream of literature exploring DCs, the following question remains: how do dynamic capabilities allow organizations to adapt to changes and succeed? To fill this gap, this paper outlines a theory of DCs, based on an analysis of strategic behavior (micro)formation at the individual and collective levels. This theory conceptualizes an evolutionary paradigm in which the intentions of organizational agents are intertwined with environmental influences. It defines DCs as ‘instruments able to entrepreneurially solve problems of evolutionary fitness of organizations.’ In doing so, it advances theoretical conceptualization of DCs and their microfoundations to provide insights into how an entrepreneurially led organization may confront and solve problems and ultimately prosper
From Start to Stardom: The Impact of Resource Allocation Strategies on New Venture Survival and Growth
No full textAn enduring question in the survival and growth of new ventures literature is why some start-ups secure survival while others fail, and why certain nascent firms achieve rapid growth in the ensuing years while many stagnate. This study investigates how conservative and aggressive resource allocation strategies impact these outcomes. By analyzing 44,559 firm-year observations in Italy from 2011 to 2019, we find that the more aggressive the resource allocation strategies—i.e., allocating a larger share of total assets to non-financial resources—the greater the likelihood of survival in the early phase. The same holds true during the growth phase, where ventures that continue adopting aggressive resource allocation strategies significantly increase their chances of becoming high-growth firms. Additional analysis highlights the critical role of plant, property, and equipment in influencing these outcomes. We also demonstrate that past resource allocation strategies exert a path-dependence effect. This underscores the importance of early-stage decisions in shaping a venture’s long-term growth trajectory, as the more aggressive the resource allocation during the survival phase, the higher the likelihood of transitioning into a high-growth firm in later stages
Quo Vadis, Behavioral Strategy? A Conceptual Framework, Review, and Research Agenda
No full textBehavioral Strategy (BS) is a field that merges psychology with strategic management theory and practice. Despite the importance of BS and its increasing popularity within strategic management, no contributions provided a comprehensive and systematic analysis of BS that reflects the richness of the research, while at the same time taking steps towards synthesizing it into a cohesive, overarching framework and proposing a future agenda. Departing from the historical roots of BS, we then perform a thematic analysis of 204 scientific papers. We then develop a conceptual framework specifying antecedents, mechanisms, moderators, and outcomes of BS identified in the literature to date. Our study shows where research has made the most progress and where gaps remain. Based on this analysis, we propose a forward-looking research agenda for future research
Behavioral Strategy in Evolution: A Review and Conceptual Framework
No full textBehavioral strategy integrates psychology with strategic management theory and practice, offering realistic insights into human cognition, emotions, and social behavior in strategic management. Yet behavioral strategy's antecedents, mechanisms, consequences, and moderators and their interconnectedness and future directions remain unclear. We explore this field's development and current state based on a systematic literature review of 241 articles. We develop a conceptual framework using a coevolutionary perspective and a socially situated cognition approach, which captures essential behavioral strategy elements and dynamics. We advance the field by emphasizing multilevel coevolving dynamics and the interplay of cognition and emotions in shaping strategic behavior. Furthermore, our framework situates cognition within social contexts. We propose an expanded research agenda for the field that highlights artificial intelligence's potential role in enhancing behavioral strategy and the connection between heuristics and nudge frameworks
Future thinking and managers’ innovative behavior: An experimental study
No full textPurpose – Does future thinking enhance managers’ innovative behavior? This study aims to posit that the ability to project events while considering current/future variables and their development (i.e. future thinking) – inextricably linked with the knowledge creation process – may enhance the manager’s accuracy and the number of potentially successful innovative ideas for organizations. Design/methodology/approach – The authors use a between-group experiment to examine the innovation choices of 47 subjects with experience in evaluating the market potential of new products when asked to support or otherwise reject real-life innovation-related ideas. The authors test the accuracy of decisions made by participants primed to apply future thinking, practically implemented through abductive reasoning, in their decision-making.
Findings – The authors found a significant change in managers’ innovative choices, with participants primed for future thinking making significantly more accurate decisions than the control group. Those participants both correctly chose innovation-related ideas with significant future potential and rejected ideas with limited potential that ultimately failed.
Originality/value – This study explores how future thinking enhances managers’ innovative behavior in organizations. It provides empirical evidence on how future thinking, practiced through abductive reasoning, can work to foster innovative behavior, which is an antecedent of knowledge creation. Organizations that foster future thinking concurrently create knowledge, increasing their competitive advantage in the long run
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
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