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We develop and validate measurement instruments for the business partner, watchdog, and scorekeeper roles of controllers. This study addresses calls to enhance the quality of survey research in management accounting by devoting more attention to scale development and especially to construct validity. By focusing on the activity sets of the controllers’ roles, we provide a theoretically and empirically grounded picture of their current roles. The measurement instruments presented in this study enable systematic research progress on controller roles, their relationships, antecedents, and performance outcomes.

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.

This chapter reviews empirical studies into the relationship between management control, strategy and sustainability.

The review explores the theoretical frameworks and models used in previous empirical research as well as the research questions and methods applied to empirically explore this emerging research area.

Even if a growing body of empirical research has emerged over the last decade, our knowledge of how companies design or use management control to support sustainability strategy appears to be limited, providing considerable scope for further research.

This review structures the state of our empirical knowledge in the area of management control, strategy and sustainability and makes suggestions for future research paths.

This chapter examines the origins and institutionalization of sport sociology in Germany and Switzerland and provides an overview of the current state of research. It shows how academic chairs and research committees were established and how the first textbooks, anthologies, and journals appeared from the 1970s onwards. The institutionalization process of German-speaking sport sociology proceeded parallel to the establishment of sport science. With regard to its theoretical and empirical basis, German-speaking sport sociology is rooted in theories and concepts of general sociology. Studies using a system theory perspective, conceptualizing sport as a societal sub-system and examining its linkage with and dependence on economy, media, or politics are particularly common in the German-speaking region. In addition, actor theoretic perspectives are very popular, and French sociologists such as Bourdieu and Foucault have had a marked influence on German-speaking sport sociology. A large number of sport sociology studies are concerned with the changes in leisure and elite sports. In this context, the emergence of new trends in risk sports as well as the fitness boom and its implications on body perception are of special interest. Further areas of research refer to sport participation and the impact of social inequality, particularly with respect to gender differences and social integration. Finally, organization research focusing on change at the level of sport associations and clubs has a long tradition. Major challenges for the future of German-speaking sport sociology include its internationalization and an enhanced international linkage in order to improve the visibility of research results.

Across disciplines, researchers and practitioners employ decision tree ensembles such as random forests and XGBoost with great success. What explains their popularity? This chapter showcases how marketing scholars and decision-makers can harness the power of decision tree ensembles for academic and practical applications. The author discusses the origin of decision tree ensembles, explains their theoretical underpinnings, and illustrates them empirically using a real-world telemarketing case, with the objective of predicting customer conversions. Readers unfamiliar with decision tree ensembles will learn to appreciate them for their versatility, competitive accuracy, ease of application, and computational efficiency and will gain a comprehensive understanding why decision tree ensembles contribute to every data scientist's methodological toolbox.

The expansion of marketing data is encouraging the growing use of deep learning (DL) in marketing. I summarize the intuition behind deep learning and explain the mechanisms of six popular algorithms: three discriminative (convolutional neural network (CNN), recurrent neural network (RNN), and Transformer), two generative (variational autoencoder (VAE) and generative adversarial networks (GAN)), and one RL (DQN). I discuss what marketing problems DL is useful for and what fueled its growth in recent years. I emphasize the power and flexibility of DL for modeling unstructured data when formal theories and knowledge are absent. I also describe future research directions.

Using a unique data set from the National Association of Colleges and Employers (NACE), we estimate the gender starting-salary gap for college graduates from 2000 to 2010. Simulation techniques are used to estimate how the salary gap would change if women had selected the same majors or job types as men. We find that about 90% of the starting-salary gap is explainable by gender differences in majors and types of job offers – a higher percentage than found in most other studies. Duncan indexes of dissimilarity also indicate that the gender distributions of job offers by college major and type of first jobs have not become more similar over the past 10 years. Although differences in college major and types of first jobs explain most of the gender gap in starting salaries of college graduates, small but unexplained gender pay differences reveal themselves in the NACE statistics.

We provide an overview of how artificial intelligence is transforming the identification, structuring, and prioritization of customer needs – known as the voice of the customer (VOC). First, we summarize how the VOC helps firms gain insights on using user-generated data. Second, we discuss the types of user-generated data and the challenges associated with analyzing each type of data. Third, we describe common methods, matched to the firms' goals and the structure of the data, that are used to analyze the VOC. Fourth, and most importantly, we map the methods to relevant applications, providing guidance to select the appropriate method to address the desired research questions.

This study extends prior balanced scorecard (BSC) research by incorporating the effects of uncertainty, payment schemes and the strength of causal relations proposed in the performance measurement model (PMM) on the budgetary dynamics. Our analysis was restricted to two strategic business units (SBU), engineering projects and electricity distribution service, from a Brazilian electric power concessionaire. We postulate a mediated moderation association between uncertainty (treatment), bonus scheme (mediator), dispersion of payment scheme and the strength of causal relations proposed in the PMM (moderators) on budgetary slack (outcome). Additionally, we postulate that the use of accounting-based measures (ABM) also mediates the effect of uncertainty on budgetary slack. We gathered monthly observations from 102 indicators containing the target and achievement values throughout 2002–2006. Managers were later asked to answer questionnaires about the possible cause–effect relations between these indicators, then 215 causal maps of the department and corporate indicators were drawn up. Econometric analysis provided evidence that the budgetary slack observed is directly impacted by uncertainty, and this impact is moderated by the dispersion of payment scheme. However, we did not find any evidence that supported the mediation process proposed between uncertainty, ABM and budgetary slack. Incomplete implementation of BSC and the level of analysis adopted are possible explanations for that.

This study aims to examine the mediating role of organisational fairness on the association between the emphasis on budgets and budget difficulty with budget value and job stress. Data were collected using an online survey questionnaire with 515 responses from middle and lower-level managers in Australian business organisations. The results indicate that organisational fairness fully mediates the association between budget difficulty with both budget value and job stress. Organisational fairness was not found to mediate the association between the emphasis on budgets with budget value and job stress. Rather, the emphasis on budgets was significantly negatively associated with job stress, implying that a greater emphasis on budgets is desirable in alleviating job stress. The findings have important implications for practice.