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Longitudinal Designs for Organizational Research  

James M. Diefendorff, Faith Lee, and Daniel Hynes

Longitudinal research involves collecting data from the same entities on two or more occasions. Almost all organizational theories outline a longitudinal process in which one or more variables cause a subsequent change in other variables. However, the majority of empirical studies rely on research designs that do not allow for the proper assessment of change over time or the isolation of causal effects. Longitudinal research begins with longitudinal theorizing. With this in mind, a variety of time-based theoretical concepts are helpful for conceptualizing how a variable is expected to change. This includes when variables are expected to change, the form or shape of the change, and how big the change is expected to be. To aid in the development of causal hypotheses, researchers should consider the history of the independent and dependent variables (i.e., how they may have been changing before the causal effect is examined), the causal lag between the variables (i.e., how long it takes for the dependent variable to start changing as a result of the independent variable), as well as the permanence, magnitude, and rate of the hypothesized change in the dependent variable. After hypotheses have been formulated, researchers can choose among various research designs, including experimental, concurrent or lagged correlational, or time series. Experimental designs are best suited for inferring causality, while time series designs are best suited for capturing the specific timing and form of change. Lagged correlation designs are useful for examining the direction and magnitude of change in a variable between measurements. Concurrent correlational designs are the weakest for inferring change or causality. Theory should dictate the choice of design, and designs can be modified and/or combined as needed to address the research question(s) at hand. Next, researchers should pay attention to their sample selection, the operationalization of constructs, and the frequency and timing of measures. The selected sample must be expected to experience the theorized change, and measures should be gathered as often as is necessary to represent the theorized change process (i.e., when the change occurs, how long it takes to unfold, and how long it lasts). Experimental manipulations should be strong enough to produce theorized effects and measured variables should be sensitive enough to capture meaningful differences between individuals and also within individuals over time. Finally, the analytic approach should be chosen based on the research design and hypotheses. Analyses can range from t-test and analysis of variance for experimental designs, to correlation and regression for lagged and concurrent designs, to a variety of advanced analyses for time series designs, including latent growth curve modeling, coupled latent growth curve modeling, cross-lagged modeling, and latent change score modeling. A point worth noting is that researchers sometimes label research designs by the statistical analysis commonly paired with the design. However, data generated from a particular design can often be analyzed using a variety of statistical procedures, so it is important to clearly distinguish the research design from the analytic approach.


The Adaptive Organization and Fast-Slow Systems  

Torben Juul Andersen and Carina Antonia Hallin

Contemporary organizations operate under turbulent business conditions and must adapt their strategies to ongoing changes. Sustainable performance can be achieved when the organization engages in interactive processes that link emerging opportunities to forward-looking analytics. But few organizations are able to practice this consistently. Fast processes performed by managers at the frontline respond to ongoing environmental stimuli and slow processes initiated by managers at the center interpret events and reasons about updated strategic actions. Current experiential insights from the fast processes can be aggregated systematically to inform the slow processes of reasoning. When the fast and slow processes interact they can form a dynamic system that adapts organizational activities to changing conditions.


The Uppsala Model in the Twenty-First Century  

Jan-Erik Vahlne

When it was developed in 1977, the Uppsala internationalization process model (Uppsala model for short) had three basic premises: process ontology, behavioral assumptions, and the presence of uncertainty. Multinational business enterprises (MBEs), among all actors, were in their infancy, and their future could not be known. Later on, the model was extended to cover the evolution of the MBEs, with factors such as internationalization, globalization, and the development of characteristics prompting changes and making them possible. Likewise, the knowledge concept was substituted for by capabilities, operational and dynamic, fitting well the other concepts of the model. The neoclassical view of the firm as an independent unit on the market is considered unrealistic. Instead, firms, MBEs, and small and medium enterprises are seen as embedded in networks with other cooperating and competing actors. The mechanisms of the 2017 version, though, are the same as in the original version. Hopefully, the latest version can be used as a tool within the scope of the “theory of the firm” research and as a platform for more studies on causal mechanisms, later to be applied in normative conclusions. It follows that static cross-sectional statistical methods are not fully satisfactory. Application of dynamic analytical methods requires investment in longitudinal data collection, which is costly, and has to be performed by institutions rather than individuals. A dream is that the Uppsala model can be used as a stepping stone in the construction of realistic macro-level studies of the economy.