Research Summary: In this article, we explore the interaction between internationalization and language strategy. We identify a range of language coping mechanisms that internationalizing firms use in response to the multilingualism they encounter. Learning outcomes and strategy implications of each of these mechanisms are identified. We then build a conceptual model to depict how, over time, interaction and influence between internationalization and language strategy become a two‐way, co‐evolutionary process. A key aspect is the role of management in shifting the firm from a reactive to a more proactive stance on language strategy. A case study is used to contextualize and illustrate the co‐evolutionary process over the long term. Case data demonstrate the constant adoption and adaptation of coping mechanisms that feed into language strategy as internationalization unfolds. Managerial Summary: This article links the internationalization process of firms with the exposure to multilingualism and the development of language strategy. We outline how internationalizing firms may utilize a range of language coping mechanisms—such as the adoption of a common corporate language—to handle multilingualism. These feed into the development of language strategy. The case of Fazer, the Finnish bakery, confectionary, and catering firm, provides an illustration of how language strategy co‐evolves over time as internationalization proceeds—in Fazer's case, many decades. Fazer's experience also demonstrates the importance of management and changes in top management in ensuring a more proactive language strategy is adopted and enforced. Adequate allocation of resources and a link to performance management were found to be critical in supporting strategic implementation.

Despite the growing prevalence of work-life support (WLS) practices in companies, there is a lack of theoretical and empirical clarity on their benefits to organizational performance. It is also unclear if the organizational performance effects of WLS practices vary based on an organization's internal and external environments. The dual objective of this paper is to investigate whether WLS practices relate to customer-focused outcomes and, if so, under which conditions WLS practices yield benefits. Drawing on contingency theory, we examine how the boundary conditions of internal firm characteristics (e.g., percentage of top management team [TMT] members with children) and external environmental factors (e.g., gender egalitarianism of the country) moderate the relationship between WLS practices and customer satisfaction. We shed light on these issues by examining multisource, longitudinal data collected over three years from a multinational corporation operating in 27 countries. The results show that both percentage of TMT members with children and gender egalitarianism of the country strengthen the relationship between WLS practices and customer satisfaction. The findings provide insights into the circumstances when WLS practices provide performance benefits for firms and the translatability of these benefits from one country to another.

Studies addressing breast cancer risk factors have been looking at trends relative to age at menarche and menopause. These studies point to a downward trend of age at menarche and an upward trend for age at menopause, meaning an increase of a woman's reproductive lifespan cycle. In addition to studying the effect of the year of birth on the expectation of age at menarche and a woman's reproductive lifespan, it is important to understand how a woman's cohort affects the correlation between these two variables. Since the behavior of age at menarche and menopause may vary with the geographic location of a woman's residence, the spatial effect of the municipality where a woman resides needs to be considered. Thus, a Bayesian multivariate structured additive distributional regression model is proposed in order to analyze how a woman's municipality and year of birth affects a woman's age of menarche, her lifespan cycle, and the correlation of the two. The data consists of 212,517 postmenopausal women, born between 1920 and 1965, who attended the breast cancer screening program in the central region of Portugal.

Joint models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique clinical studies where longitudinal outcomes are recorded alongside event times. Those two processes are often linked and the two outcomes should thus be modeled jointly in order to prevent the potential bias introduced by independent modeling. Commonly, joint models are estimated in likelihood-based expectation maximization or Bayesian approaches using frameworks where variable selection is problematic and that do not immediately work for high-dimensional data. In this paper, we propose a boosting algorithm tackling these challenges by being able to simultaneously estimate predictors for joint models and automatically select the most influential variables even in high-dimensional data situations. We analyze the performance of the new algorithm in a simulation study and apply it to the Danish cystic fibrosis registry that collects longitudinal lung function data on patients with cystic fibrosis together with data regarding the onset of pulmonary infections. This is the first approach to combine state-of-the art algorithms from the field of machine-learning with the model class of joint models, providing a fully data-driven mechanism to select variables and predictor effects in a unified framework of boosting joint models.