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Since the outbreak of the COVID-19 pandemic, most organizations have experienced a sudden and unprecedented drop in revenue and productivity. However, the pandemic did not exclusively negatively impact organizations; rather, it resulted in both negative and positive effects. To delve into the multi-level process through which organizational outcomes change from negative to positive indicators, this study focuses on organizational resilience as a theoretical concept to overcome pandemic-related turmoil.

The authors conducted a multi-level analysis based on grounded theory with a sample of 30 healthcare employees who worked in hospitals and were simultaneously enrolled in a part-time master of business administration (MBA) program at a university in the Midwest. Of the 30 participants, 21 were from a single university hospital (UH), and the remaining 9 participants were from other hospitals (non-UH).

The authors analyzed the data and incorporated three existing perspectives of organizational resilience (attribute, process and multi-level views) into an integrated model. The authors identified 25 first-order concepts and 8 second-order themes and categorized them into 4 aggregate dimensions at different unit levels: organizational field, leadership, operation and individual units.

A resilient hospital operates as a cohesive system, with entities at various levels – from individuals and teams to the broader organization – collaborating seamlessly to foster resilience. Top management team (TMT) should persistently communicate with employees to provide information about the current crisis and clear strategic directions to reduce employees' fear and prevent anomie stemming from future uncertainty. Managers should not only be concerned about employees' physical safety from infection and psychological safety from isolation but also encourage employees to elicit meaningfulness from their work. Furthermore, TMT and human resource (HR) teams should adapt human resource management (HRM) practices to allow for flexibility and optimism in employee roles.

In this study, the authors utilized a qualitative methodology with grounded theory in order to develop a comprehensive model that holds theoretical, methodological and practical significance. Theoretically, the authors' novelty lies in the synthesis of three distinct perspectives: attribute, process and multi-level. The authors merged these approaches into a unified model, identifying precursors of resilience at different levels. Methodologically, the authors focused on hospitals as target samples, which were the foremost and representative organizations severely confronting the crisis and turmoil brought by the pandemic. The authors documented organizations' experiences amidst the crisis as they unfolded in real time rather than in hindsight. This approach highlights the immediacy and significance of the authors' research in the realm of crisis management. Practically, the authors' findings illuminate that organizational resilience can be developed through a collaborative effort. It emerges from coordinated interactions across various organizational actors, from employees and middle managers to the TMT.

The purpose of this study is to propose a systematic method for the diffusion of forecasting technology in the pre‐launch stage.

The authors designed survey question items that are familiar to interviewees as well as algebraically transformable into the parameters of a logistic diffusion model. In addition, they developed a procedure that reduces inconsistency in interviewee responses, removes outliers, and verifies conformability, in order to reduce the error and yield robust estimation results.

The results show that the authors' method performed better in the empirical cases of digital media broadcasting and internet protocol television in terms of sum of squared error compared with an existing survey‐based method, a regression method, and the guessing‐by‐analogy method. Specifically, the authors' method can reduce the error by using the conformability and outlier tests, while the consistency factor contributes to determining the final estimate with personal estimates.

The procedure proposed in this study is confined to the presented logistic model. Future research should aim to extend its application to other representative diffusion models such as the Bass model and the Gompertz model.

The authors' method provides a better quality of forecasting for innovative new products and services compared with the guessing‐by‐analogy method, and it contributes to managerial decisions such as those in production planning.

The authors introduce the concepts of conformability and consistency in order to reduce the error from personal biases and mistakes. Based on these concepts, they develop a procedure to yield robust estimation results with less error.