Richard A. Guzzo’s research while affiliated with German Historical Institute, Washington DC and other places

This paper advocates for the value of open science in many areas of research. However, after briefly reviewing the fundamental principles underlying open science practices and their use and justification, the paper identifies four incompatibilities between those principles and scientific progress through applied research. The incompatibilities concern barriers to sharing and disclosure, limitations and deficiencies of overidentifying with hypothetico-deductive methods of inference, the paradox of replication efforts resulting in less robust findings, and changes to the professional research and publication culture such that it will narrow in favor of a specific style of research. Seven recommendations are presented to maximize the value of open science while minimizing its adverse effects on the advancement of science in practice.

Adopting an interdisciplinary perspective, this article reports new evidence on the impact of age and experience on work unit performance. Two types of experience that increase with age are “general” and “firm-specific.” The focus here is on the influence of general human capital (which increases with time spent in the workforce) and firm-specific human capital (which increases with tenure with the current employer) on work unit performance. Although age–performance relationships have been investigated extensively in two research literatures, psychology and economics, neither addresses such relationships at the unit-within-organization level of analysis, concentrating instead on age–performance relationships at individual, organizational, or national levels. Using a unique data set comprised of large-sample, long-duration, multivariate studies of unit performance within firms this meta-analysis synthesizes partial effect sizes for the effects of age and tenure. A key finding is that tenure positively affects unit performance whereas age has no effect. Work unit leaders’ tenure but not age was found to positively affect unit performance. The lack of evidence of an age–performance relationship is consistent with psychological research at the individual level but contravenes economics research literature which, at all levels of analysis, generally reports negative relationships between age and performance. Neither the heterogeneity of tenure nor age was related to performance nor was there evidence of nonlinearities in relationships. Practical implications of the findings are discussed regarding ageism and employers’ use of gig or contract workers. Implications for future research and theory focus on interdisciplinary theory development and the scientific contribution of organizationally based research.

The world is awash in data. Data is being created and stored at ever-increasing rates through a variety of new methods and technologies. Data is accumulating in all sorts of accessible places. Much of that data is of great interest to industrial–organizational (I-O) psychologists, often in ways never anticipated by those who develop technologies and processes that generate and store that data. I-O psychologists also generate data in the course of research and practice in ways that, especially if joined with data originating from other sources, create giant datasets. This abundance of data—variables, measurements, observations, facts—can be used to inform a vast number of issues in research and practice. This is the new “big data” world, and beyond opportunities, this new world also presents challenges and potential hazards.

For several reasons, securing an ethnically, nationally, and gender-diverse workforce and maintaining an inclusive environment for it are increasingly important objectives for many organizations. This entry offers an internal labor market view of organizational workforce diversity. The article begins by reviewing the core concept of an internal labor market and applies it to workforce diversity. The internal labor market approach is a data-rich approach that both describes organizational “talent flows” (such as hires, promotions, and attrition) and analyzes them through statistical modeling to identify the factors that most influence those flows and ultimate representation outcomes. Applied to workforce diversity, the approach enables organizations to better understand their internal workforce dynamics and provides the basis for prioritizing and aligning interventions to better achieve diversity objectives. A case example is presented to illustrate the approach.

Learning changes individuals, and learning in organizations can be affected in several ways such as through formal educational programs, relationship-based coaching and advising, and by direct experience. The changes have further consequences, both for the individual and for the employing organization. This chapter's central concern is finding the best way of understanding how the consequences of learning play out, over time, for individual and organization. It summarizes a few highlights of the internal labor market (ILM) framework. “Systems” and “systems thinking” are hallmarks of the internal labor market dynamics framework. Another feature is that a single event or process—such as learning—can influence multiple outcomes. The greatest power of an ILM analysis comes from applying statistical modeling processes to identify causes and consequences. That power comes from two sources. One is temporal ordering and the other is the capacity to account for other influences on the outcomes of interest.