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The purpose of this study was to explore how variables such as student demographics, pre-college leadership activities, and perceived pre-college parenting behaviors predict students’ leader self-efficacy (i.e., individuals’ confidence in themselves to lead and belief that others will support their leadership [Hannah et al., 2008]) in college and leader emergence (i.e., college-based leadership involvements [DeRue & Ashford, 2010]) in college. Undergraduate students (n = 420) at a large, public university in the Mid-Atlantic were surveyed to examine these relationships and data were analyzed using hierarchical and logistic regression, with appropriate controls and moderators. Findings included discovery that pre-college engagement with sports team positional leadership, community service, extracurriculars, and positive parenting behaviors, such as family routine and greater quality time with parents, predicted leader self-efficacy. Further, findings noted that pre-college community service, extracurriculars, peer tutoring and perceptions of parental quality time and proactive parenting predicted leader emergence. This study suggests that students’ leadership development is influenced by myriad systems across the lifespan and demonstrates that, as educators committed to student development, we must engage the full arc of our students’ leadership journeys and provide for intentional partnerships between higher education and the K-12 community.

Purpose – This chapter examines the evolution of new audit and traceability systems in New Zealand horticultural export industries. Identified as one trajectory in New Zealand agriculture partly resulting from neoliberal reform, the arrival of audit culture in food export industries has significantly repositioned these export sectors, particularly in relation to how they might respond to new energy and climate change challenges.

Design/methodology/approach – The chapter reviews the neoliberalisation of New Zealand agriculture in the 1980s and then examines the emergence of specific industry, audit and regulatory responses to new challenges around energy and climate change. Horticultural export sectors are used to demonstrate these responses and then compared with other, more productivist-oriented sectors in New Zealand.

Findings – The argument presented at the end of this chapter is that those food export sectors that have embraced the new audit approaches rather than taking a more productivist pathway will be better positioned to cope with the shocks of new energy costs and climate change requirements.

Originality/value – This chapter demonstrates the variable outcomes of neoliberal reform in agriculture. It identifies new audit and governance technologies as both an essential contributor to understanding the nature of global food chains and a potentially important contributor to achieving greater agri-food resilience in the face of future shocks like climate change.

The physician–patient relationship is the cornerstone of care quality. Unfortunately, it may be adversely affected by physician burnout, which is becoming more prevalent according to the literature. We present a model, based on the burnout and physician–patient communication literatures, which delineates the impact of physician burnout on the physician–patient interaction and ultimately on patient outcomes. In short, when physicians use depersonalization to cope with emotional exhaustion, their communication style becomes more biomedically oriented. Faced with this communication style when interacting with their physician, patients are less satisfied, trusting, and adherent. The implications of this model and directions for future research are presented.

Positive organizational scholarship in healthcare (POSH) suggests that, to promote widespread improvement within health services, focusing on the good, the excellent, and the brilliant is as important as conventional approaches that focus on the negative, the problems, and the failures. POSH offers different opportunities to learn from and build resilient cultures of safety, innovation, and change. It is not separate from tried and tested approaches to health service improvement – but rather, it approaches this improvement differently. The paper aims to discuss these issues.

POSH, appreciative inquiry (AI) and reflective practice were used to inform an exploratory investigation of what is good, excellent, or brilliant health service management.

The researchers identified new characteristics of good healthcare and what it might take to have brilliant health service management, elucidated and refined POSH, and identified research opportunities that hold potential value for consumers, practitioners, and policymakers.

The secondary data used in this study offered limited contextual information.

This approach is a platform from which to: identify, investigate, and learn about brilliant health service management; and inform theory and practice.

POSH can help to reveal what consumers and practitioners value about health services and how they prefer to engage with these services.

Using POSH, this paper examines what consumers and practitioners value about health services; it also illustrates how brilliance can be theorized into health service management research and practice.

The purpose of this paper is to present computer‐aided simultaneous signal and noise modeling and analysis for mm‐wave field‐effect transistors (FETs) based on scattering parameters approach.

A mm‐wave FET is modeled as three active‐coupled transmission lines, and the developed wave approach is applied to this model to calculate both signal and noise performances of the device.

The measurements show a good match with the calculated data from the point of view of both signal and noise performances of the device.

This CAD‐oriented analysis and modeling can be easily applied to the mm‐wave simulators to improve the simultaneous signal and noise optimization, modeling and analysis of mm‐wave devices, especially for traveling wave transistors in which the distributed model seems to be more exact than the usual lumped models. Also the proposed routine compared to the admittance approach is conceptually more compatible with scattering representations of active and passive circuits. The developed algorithm has been applied successfully to mm‐wave MESFETs and HEMTs.

This paper aims to develop a novel and generalizable machine-learning based method of measuring established marketing constructs through passive analysis of consumer-generated textual data. The authors term this method scale-directed text analysis.

The method first develops a dictionary of words related to specific dimensions of a construct that is used to assess textual data from any source for a specific meaning. The method explicitly recognizes both specific words and the strength of their underlying sentiment.

Results calculated using this new approach are statistically equivalent to responses to traditional marketing scale items. These results demonstrate the validity of the authors’ methodology and show its potential to complement traditional survey approaches to assessing marketing constructs.

The method we outline relies on machine learning and thus requires either large volumes of text or a large number of cases. Results are reliable only at the aggregate level.

The method detail provides a means of less intrusive data collection such as through scraped social media postings. Alternatively, it also provides a means of analyzing data collected through more naturalistic methods such as open-response forms or even spoken language, both likely to increase response rates.

Scale-directed text analysis goes beyond traditional methods of conducting simple sentiment analysis and word frequency or percentage counts. It combines the richness of traditional textual and sentiment analysis with the theoretical structure and analytical rigor provided by traditional marketing scales, all in an automatic process.