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The purpose of this paper is to estimate individual promotional campaign impacts through Bayesian inference. Conventional statistics have worked well for analyzing the impact of direct marketing promotions on purchase behavior. However, many modern marketing programs must drive multiple purchase objectives, requiring more precise arbitration between multiple offers and collection of more data with which to differentiate individuals. This often results in datasets that are highly dimensional, yet also sparse, straining the power of statistical methods to properly estimate the effect of promotional treatments.

Improvements in computing power have enabled new techniques for predicting individual behavior. This work investigates a probabilistic machine-learned Bayesian approach to predict individual impacts driven by promotional campaign offers for a leading global travel and hospitality chain. Comparisons were made to a linear regression, representative of the current state of practice.

The findings of this work focus on comparing a machine-learned Bayesian approach with linear regression (which is representative of the current state of practice among industry practitioners) in the analysis of a promotional campaign across three key areas: highly dimensional data, sparse data and likelihood matching.

Because the findings are based on a single campaign, future work includes generalizing results across multiple promotional campaigns. Also of interest for future work are comparisons of the technique developed here with other techniques from academia.

Because the Bayesian approach allows estimation of the influence of the promotion for each hypothetical customer’s set of promotional attributes, even when no exact look-alikes exist in the control group, a number of possible applications exist. These include optimal campaign design (given the ability to estimate the promotional attributes that are likely to drive the greatest incremental spend in a hypothetical deployment) and operationalizing efficient audience selection given the model’s individualized estimates, reducing the risk of marketing overcommunication, which can prompt costly unsubscriptions.

The original contribution is the application of machine-learning to Bayesian Belief Network construction in the context of analyzing a multi-channel promotional campaign’s impact on individual customers. This is of value to practitioners seeking alternatives for campaign analysis for applications in which more commonly used models are not well-suited, such as the three key areas that this paper highlights: highly dimensional data, sparse data and likelihood matching.

The most recent research on the prevalence of young caring in secondary school–age children (Joseph et al., 2019) suggests that one in five 11–16 year olds have a caring role. There are inherent challenges with identifying children and young people (CYP) who have caring responsibilities; they find themselves in the role because of love for a family member, as well as the lack of provision to meet the needs of the person they are caring for (Keith & Morris, 1995), not because they have consciously chosen to become a carer, and so do not identify with the concept (Smyth, Blaxland, & Cass, 2011). School can be both precarious and a place of sanctuary for young carers (Becker & Becker, 2008). Experiences of education, as with many aspects of caring, exist on a continuum with no young carers’ educational experience being the same (Dearden & Becker, 2003). Schools have a pivotal role in identifying, understanding and supporting young carers to prevent their education from being adversely affected.

This chapter is a transcript of an informal conversation between Jack Katz and Keith Hayward that took place in Rome in August 2019. It covers a number of subjects linked to Professor Katz’s academic career, as well as some personal biographical reflections on how his upbringing shaped his sociological thinking about the ‘seductive’ nature of crime and transgression. The chapter also discusses Professor Katz’s various contributions to qualitative research methodology (specifically ‘analytic induction’ and ‘social ontology’), before concluding with a summary of his latest research for the ‘Hollywood neighborhoods’ project and some brief thoughts about future research trajectories.

Becker appoint works manager Becker Equipment & Lifts Ltd., a TI Company of Wembley, have strengthened their manufacturing departments by the appointment of Jeffrey Miller as Works Manager. Becker manufacture the Bexuda range of precise‐volume liquid filling machines, and are the UK's leading manufacturers of electrohydraulic passenger lifts.

Despite widespread interest in the gig economy, academic research on the topic has lagged behind. The present chapter applies organizational theory and research to compose a working model for understanding participation in the gig economy and how gig work may impact worker health and well-being. Drawing from past research this chapter defines the gig economy in all its diversity and advances a framework for understanding why individuals enter into gig economy. Next, the authors discuss how various characteristics of the gig economy and gig workers can be understood as both demands and resources that influence how gig work is likely to be experienced by the individual. To understand how these characteristics are likely to influence worker health and well-being, we draw from past research on alternative work arrangements and entrepreneurship, as well as the limited extant research on the gig economy. Finally, a research agenda is proposed to spur much needed research on the gig economy and its workers.

This study examines the connections between subculture theory, symbolic interaction and the work of David Matza with a special focus on exploring alcohol consumption by young adults in the UK. We apply Matza ideas of the “techniques of neutralization,” “subterranean values,” and “drift” within an ethnographic study on alcohol to suggest that young people's “calculated hedonism” can be understood as a strategy of agency in the context of a subcultural setting. This article adds to the literature of symbolic interaction, subculture and the discipline of sociology by critically focusing on the work of David Matza from its reception in the 1960s to today as a central element of the new paradigm of cultural criminology. For us the sociological imagination is “alive and well” through Matza's advocacy of naturalism whereby he sought to integrate the work Chicago School under Park and Burgess with his assessment of the so-called Neo-Chicago School. In the literature Matza's work is often defined as symbolic interactionist we see his ambition in a wider sense of wanting sociology to recover human struggle and the active creation of meaning. Our approach is to understand the calculated hedonism of young adult use of alcohol through their humanity.

The present study examined multiple antecedents of organizational citizenship behaviors (OCB) in a Mexican sample of retail salespeople. Although a quota based measure of sales performance was correlated with OCB, the correlation was relatively low. However, personality and attitude measures, with conscientiousness having the cleanest relationship, were significantly correlated with OCB. A situational judgment measure was significantly correlated with sales performance. These findings indicate that individual personality facets may be stable predictors of OCB in Mexican samples.

This chapter gives one version of the recent history of evaluation case study. It looks back over the emergence of case study as a sociological method, developed in the early years of the 20th Century and celebrated and elaborated by the Chicago School of urban sociology at Chicago University, starting throughout the 1920s and 1930s. Some of the basic methods, including constant comparison, were generated at that time. Only partly influenced by this methodological movement, an alliance between an Illinois-based team in the United States and a team at the University of East Anglia in the United Kingdom recast the case method as a key tool for the evaluation of social and educational programmes.