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The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.

The burnout, stress, and work-life balance challenges faced by teachers have received renewed interest due to the myriad disruptions and changes to K-12 schooling brought about by the COVID-19 pandemic. Even prior to the pandemic, relatively little was known about teachers' time use outside of the classroom, the blurring of work and home boundaries, and how teachers compare to similar professionals in these regards. We use daily time-diary data from the American Time Use Survey (ATUS) for 3,168 teachers and 1,886 professionals in similarly prosocial occupations from 2003 to 2019 to examine occupational differences in time use. Compared to observationally similar non-teachers, teachers spend significantly more time volunteering at their workplace and completing work outside the workplace during the school year. On average, teachers spend 19 more minutes working outside of the workplace on weekdays than observably similar non-teachers and 38 more minutes on weekends. The weekend disparity is particularly large among secondary school teachers. This suggests that before the widespread switch to online and hybrid learning necessitated by the COVID-19 pandemic, teachers were already navigating blurrier work-life boundaries than their counterparts in similar professions. This has important implications for teacher turnover and for the effectiveness and wellness of teachers who remain in the profession.

The paper provides an overview of research published in the innovation and operations management (IOM) literature on 15 methods for cost management in new product development, and it provides a comparison to an earlier review of the management accounting (MA) literature (Wouters & Morales, 2014).

This structured literature search covers papers published in 23 journals in IOM in the period 1990–2014.

The search yielded a sample of 208 unique papers with 275 results (one paper could refer to multiple cost management methods). The top 3 methods are modular design, component commonality, and product platforms, with 115 results (42%) together. In the MA literature, these three methods accounted for 29%, but target costing was the most researched cost management method by far (26%). Simulation is the most frequently used research method in the IOM literature, whereas this was averagely used in the MA literature; qualitative studies were the most frequently used research method in the MA literature, whereas this was averagely used in the IOM literature. We found a lot of papers presenting practical approaches or decision models as a further development of a particular cost management method, which is a clear difference from the MA literature.

This review focused on the same cost management methods, and future research could also consider other cost management methods which are likely to be more important in the IOM literature compared to the MA literature. Future research could also investigate innovative cost management practices in more detail through longitudinal case studies.

This review of research on methods for cost management published outside the MA literature provides an overview for MA researchers. It highlights key differences between both literatures in their research of the same cost management methods.

This article proposes a new approach to detecting the presence of common cyclical features when several time series are sampled at different frequencies. We generalize the common-frequency approach introduced by Engle and Kozicki (1993) and Vahid and Engle (1993). We start with the mixed-frequency VAR representation investigated in Ghysels (2012) for stationary time series. For non-stationary time series in levels, we show that one has to account for the presence of two sets of long-run relationships. The first set is implied by identities stemming from the fact that the differences of the high-frequency I (1) regressors are stationary. The second set comes from possible additional long-run relationships between one of the high-frequency series and the low-frequency variables. Our transformed vector error-correction model (VECM) representations extend the results of Ghysels (2012) and are important for determining the correct set of variables to be used in a subsequent common cycle investigation. This fact has implications for the distribution of test statistics and for forecasting. Empirical analyses with quarterly real gross national product (GNP) and monthly industrial production indices for, respectively, the United States and Germany illustrate our new approach. We also conduct a Monte Carlo study which compares our proposed mixed-frequency models with models stemming from classical temporal aggregation methods.

The growth of the nationalist right in Europe and the United States has set off a debate over whether “economic anxiety” or “racial resentment” is at the root of this phenomenon. Examining the case of the French National Front, I suggest that this is a poor way of posing the question of the significance of class in explaining the rise of the nationalist right. Recent advances by the National Front—particularly among working-class voters—have tended to be attributed to the party's strategic pivot toward a “leftist” economic program and an embrace of the republican tradition. This in turn has been critically interpreted in two different ways. Some take the FN’s strategic pivot at face value and see the party's success as the expression of a new political cleavage between cosmopolitanism and communitarianism. Others see the National Front's embrace of republicanism as a cynical ploy hiding its true face. Both interpretations, however, point to a strategy of “republican defense” as a means to counteract the National Front. I argue that this strategy is likely to misfire and that class remains central to explaining—and countering—the rise of the National Front, albeit in a peculiar way. Working-class support for the National Front does indeed appear to be driven primarily by ethno-cultural, not class, interests, but this is itself predicated on a historical decline in the political salience of class due to the neoliberal depoliticization of the economy. I argue that it was this disarticulation of class identity that helped deliver the working-class vote to the National Front and that any strategy for combating the nationalist right must thus find new ways to articulate a class identity capable of neutralizing racist and chauvinist articulations.

In recent decades, it has become clear that the major economic, political, and social problems in the world require contemporary development research to examine intersections of race and class in the global economy. Theorists in the Black Radical Tradition (BRT) were the first to develop and advance a powerful research agenda that integrated race–class analyses of capitalist development. However, over time, progressive waves of research streams in development studies have successively stripped these concepts from their analyses. Post-1950s, class analyses of development overlapped with some important features of the BRT, but removed race. Post-1990s, ethnicity-based analyses of development excised both race and class. In this chapter, I discuss what we learn about capitalist development using the integrated race–class analyses of the BRT, and how jettisoning these concepts weakens our understanding of the political economy of development. To remedy our current knowledge gaps, I call for applying insights of the BRT to our analyses of the development trajectories of nations.