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Underpinning clean language interviewing is a set of skills that allow the interviewer great facility in tracking what has been presented. These skills include minimising personal inference and making an informed choice of what question to ask. They are grounded in the logic of the interviewee's data and the purpose of the interview.

This chapter makes visible four hidden skills I identified through reflection on a doctoral study I conducted using clean language interviewing. These are, how I: ‘parcel out’ sentences in order to build visual-spatial schema; apply content-free codes during the interview; decide what is salient in the interviewee's words and gestures; and use adjacency to navigate my way around the data. Since these skills are applied moment-by-moment during the interview, I refer to them as ‘coding in-the-moment’. I conclude with a comparison between grounded theory methodology and clean language interviewing.

In recent years professional accountancy bodies (e.g., CPA), accreditation institutions (e.g., AACSB) and employers have steadily raised, and continue to raise expectations regarding the need for accounting graduates to demonstrate skills in data analytics. One of the obstacles accounting instructors face in seeking to implement data analytics, however, is that they need access to ample teaching materials. Unfortunately, there are few such resources available for advanced programming languages such as R. While skills in commonly used applications such as Excel are no doubt needed, employers often take these for granted and incremental value is only added if graduates can demonstrate knowledge in using more advanced data analytics tools for decision-making such as coding in programming languages. This, together with the current dearth of resources available to accounting instructors to teach advanced programming languages is what drives motivation for this chapter. Specifically, we develop an intuitive, two-dimensional framework for incorporating R (a widely used open-source analytics tool with a powerful embedded programming language) into the accounting curriculum. Our model uses complexity as an integrating theme. We incorporate complexity into this framework at the dataset level (simple and complex datasets) and at the analytics task level (simple and complex tasks). We demonstrate two-dimensional framework by drawing on authentic simple and complex datasets as well as simple and complex tasks that could readily be incorporated into the accounting curriculum and ultimately add value to businesses. R script programming code are provided for all our illustrations.

Panel data, where observations of entities are repeated over time, are common in strategic management research. However, explorations of the role of time on predictors of interest are often unexplored. In this chapter, we illustrate how the use of mixed-effect growth models can enhance theory and research in strategic management by exploring changes in outcomes of interest over time. Mixed-effects models allow for testing both within and between effects, while also calculating specific intercepts (firm average values) and slopes (trajectories of specific firms over time) using empirical Bayes estimates. We also illustrate how a discontinuous growth model could be used to assess differences in firm intercepts and slopes surrounding exogenous events (e.g., global pandemics) without requiring a control group.

In this chapter, the authors consider the role of time for research in occupational stress and well-being. First, temporal issues in studying occupational health longitudinally, focusing in particular on the role of time lags and their implications for observed results (e.g., effect detectability), analyses (e.g., handling unequal durations between measurement occasions), and interpretation (e.g., result generalizability, theoretical revision) were discussed. Then, time-based assumptions when modeling lagged effects in occupational health research, providing a focused review of how research has handled (or ignored) these assumptions in the past, and the relative benefits and drawbacks of these approaches were discussed. Finally, recommendations for readers, an accessible tutorial (including example data and code), and discussion of a new structural equation modeling technique, continuous time structural equation modeling, that can “handle” time in longitudinal studies of occupational health were provided.

Purpose – This chapter provides a theoretical and conceptual overview of Corporate Social Responsibility (CSR). It is written as a descriptive document to enhance the understanding of CSR within the context of international business.Design/methodology/approach – This chapter is built based on an extensive literature review.Findings – This chapter contains six subsections. The first subsection looks at the concept of CSR, and it highlights the possible role of CSR in mitigating the negative consequences of globalisation. The second subsection looks at the evolution of CSR since the 1990s. The third section looks at ethics theories. The fourth section looks at political theories to explain CSR. The fifth section looks at the business case for CSR. And finally the sixth section looks at specific CSR initiatives.Practical implications – This chapter provides a response to the necessity for this analysis that arises from the effects of CSR actions in international business.Originality/value of chapter – This chapter provides a summary of the conceptual and theoretical framework of CSR. It could be used as a teaching tool for undergraduate and masters’ courses on either international business or corporate social responsibility.

Different images of teacher knowledge and of teaching are described using the conceptual structure of Cochran-Smith and Lytle (1999a), in which knowledge and practice are viewed as either formal, practical, or transformative. Instructional design (ID) represents a formal image of knowledge and frames the teacher as a problem-solver. Teachers, however, have been resistant to the use of ID. In a graduate ID course, students were given the task of drawing their own representation of the ID process. Two research questions framed the study, including How might these models be categorized? and What views of teaching were found in the models? From 13 deliveries of the course, 123 models and explanatory narratives were analyzed from students who were teachers. The course and ID model task are described. A recursive cycle of categorization and theme-building were used. Types of models included those characterized by Human Activity (51 models), Components (23), Artifacts (20), Organic (15), and Flow Charts (14). Views of teaching included Teacher-centered (47 models), Designer-centered (36 models), Co-centered (18), Learner-centered (16), and De-centered (6). Analysis revealed that for teachers ID activity is a human activity and the principal focus for design activity is teacher needs. Implications are summarized in terms of teacher knowledge and expertise, as well as limitations to our methodology.

We investigate discrepancies between willingness to pay (WTP) and willingness to accept (WTA) in the context of a stated choice experiment. Using data on customer preferences for water services where respondents were able to both ‘sell’ and ‘buy’ the choice experiment attributes, we find evidence of non-linearity in the underlying utility function even though the range of attribute levels is relatively small. Our results reveal the presence of significant loss aversion in all the attributes, including price. We find the WTP–WTA schedule to be asymmetric around the current provision level and that the WTP–WTA ratio varies according to the particular provision change under consideration. Such reference point findings are of direct importance for practitioners and decision-makers using choice experiments for economic appraisal such as cost–benefit analysis, where failure to account for non-linearity in welfare estimates may significantly over- or under-state individual's preferences for gains and avoiding losses respectively.

In this chapter, we describe how random utility maximization (RUM) discrete choice models are used to estimate the demand for commodity attributes in quality-differentiated goods. After presenting a conceptual overview, we focus specifically on the conditional logit model. We examine technical issues related to specification, interpretation, estimation, and policy use. We also discuss identification strategies for estimating the role of price and non-price attributes in preferences when product attributes are incompletely observed. We illustrate these concepts via a stylized application to new car purchases, in which our objective is to measure preferences for fuel economy.