Search results

The purpose of this paper is to investigate the impact of various employment characteristics on the health of Canadian caregiver-employees (CEs), who are working full-time in the labor market while also providing informal/family care to adults.

Framed with Pearlin et al.’s (1990) stress model and using data from Statistic Canada’s General Social Survey Cycle 26 (2012), several work-related variables for caregivers were considered, including the availability of various forms of caregiver-friendly workplace policies (CFWPs), and a series of work interferences (WIs) experienced as a result of the caregiving role.

This study provides evidence for the value of CFWPs in all workplaces. Counter-intuitively, family and other forms of support were found to negatively relate to both physical and mental health.

This suggests that CFWPs will not only have an impact on CEs’ physical health outcomes, but will likely decrease the effect of the WIs experienced.

In many security domains, the ‘human in the system’ is often a critical line of defence in identifying, preventing and responding to any threats (Saikayasit, Stedmon, & Lawson, 2015). Traditionally, such security domains are often focussed on mainstream public safety within crowded spaces and border controls, through to identifying suspicious behaviours, hostile reconnaissance and implementing counter-terrorism initiatives. More recently, with growing insecurity around the world, organisations have looked to improve their security risk management frameworks, developing concepts which originated in the health and safety field to deal with more pressing risks such as terrorist acts, abduction and piracy (Paul, 2018). In these instances, security is usually the specific responsibility of frontline personnel with defined roles and responsibilities operating in accordance with organisational protocols (Saikayasit, Stedmon, Lawson, & Fussey, 2012; Stedmon, Saikayasit, Lawson, & Fussey, 2013). However, understanding the knowledge that frontline security workers might possess and use requires sensitive investigation in equally sensitive security domains.

This chapter considers how to investigate knowledge elicitation in these sensitive security domains and underlying ethics in research design that supports and protects the nature of investigation and end-users alike. This chapter also discusses the criteria used for ensuring trustworthiness as well as assessing the relative merits of the range of methods adopted.

Using cross-sectional household survey data, this paper aims to determine the impact of food price increases on poverty in Indonesia.

This paper uses the quadratic almost ideal demand system applied to the 2013 Indonesian household survey data. The impact of food price increase on household welfare is calculated using a welfare measure, compensating variation.

Three food groups with the most outstanding price impact on poverty, rice, vegetables and fish, were studied. The 20% increase in the price of each food group causes an increase in the headcount ratio by 1.360 points (rice), 0.737 points (vegetables) and 0.636 points (fish). Maintaining food price stability for these food groups is very important because the more the price increases, the more the impact on poverty. Food price policies in rural areas are also more critical than in urban areas because the impact of food price increases in rural areas is higher.

This paper does not consider the positive impact of rising food prices on food-producing households.

Implementing appropriate poverty alleviation policies through food policies for main food groups and social protection.

Promoting rural development policies and agricultural growth.

This paper contributes to the existing literature by providing empirical results regarding the impact of domestic food prices increase on poverty in Indonesia.

The authors aim to study a conceptual model based on behavioural theories (UTAUT-3 model) to evaluate the adoption, usage and recommendation for neobanking services in India.

The authors propose this model based on the UTAUT-3 integrated with perceived risk constructs. Hypotheses were developed to determine the relationships and empirically validated using the PLSs-SEM method. Using the survey method, 680 Delhi NCR respondents participated in the survey.

Empirical results suggested that behavioural intention (BI) to usage, adoption and recommendation affects neobanking adoption positively. The research observed that performance expectancy (PE), effort expectancy (EE), perceived privacy risk (PYR) and perceived performance risk (PPR) are the essential constructs influencing the adoption of neobanking services.

Limited by geographic and Covid-19 constraints, a cross-sectional study was conducted. It highlights the BI of neobanking users tested using the UTAUT-3 model during the Covid-19 period.

The study's outcome offers valuable insights into Indian Neobanking services that researchers have not studied earlier. These insights will help bank managers, risk professionals, IT Developers, regulators, financial intermediaries and Fintech companies planning to invest or develop similar neobanking services. Additionally, this research provides significant insight into how perceived risk determinants may impact adoption independently for the neobanking service.

The purpose of this paper is to understand the strategic management of a technology-enabled shift from a product-centric to a service-centric logic and to identify the sociotechnical dynamics underlying this transition. The study focuses on how manufacturers manage to create value in industrial markets through digital servitization.

An abductive research approach is used to investigate two manufacturing firms, and an interpretive framework is used as an analytical template. A cross-case analysis is conducted.

The case companies strategically managed sociotechnical processes of digitization to co-create value. Their service orientation delineates dissimilarity in terms of digital servitization. It reflects a viable ecosystem that moves toward datatization through adaptation in one case and a viable ecosystem that moves toward digitization through reconfiguration in the other case.

A theoretically grounded, empirically informed framework is proposed to detect transformational mechanisms to manage value co-creation in digitally servitized contexts, thus contributing to ecosystem viability.

This is the first study to adopt a system perspective such as the viable system approach combined with service-dominant logic to reconceptualize the overall sociotechnical processes and the underlying mechanisms leading to digitized value creation. In line with a systems view and a systematic process based on a transformative attitude toward digital servitization, the empirically informed framework identifies specific co-creation activities and recursive feedback loops.

This paper aims to establish the determinants of production in the Spanish Designation of Origin (DO) area for Cava wine and forecasts sales to establish vineyard area variations that maintain market equilibrium.

By applying a vector autoregressive (VAR) model, the authors forecast demand and the consequent requirements for base wine production.

The results show that Cava sales determine the base wine supply. After forecasting demand and the consequent requirements for base wine, the authors’ results show that, to avoid oversupply, the vineyard area for Cava wine should not be increased.

The paper develops a simple and effective method for DOs affected by the current European wine plantation regulations to forecast from a supply and demand perspective and their surface needs in response to market changes.

This study contributes to the literature because, to the best of the authors’ knowledge, no other study has investigated the determinants of Cava supply and demand or defines a model to assess the effects of changes in growing areas. The model is applicable to other European protected designations of origin wines and would help policymakers to accurately establish vine planting authorizations.

Leadership content and pedagogical strategies are fundamental to health professionals’ education. All health professionals must be able to lead effectively and thrive in today’s complex health systems. Students must be involved in meaningful didactic and experiential leadership development early in their academic progression, and educators are positioned to lead in this initiative. This paper describes pre-postfindings from an application of Kouzes and Posner’s Student Leadership Practices Inventory with students who completed an interdisciplinary undergraduate leadership development program and observers’ perspectives of these students’ leadership characteristics. Outcome data found positive change in pre-post data except for encourage the heart for the student participants and challenges others for the observers. Critical reflectionand authentic assessment of actions that occurred during the leadership program could have shifted students’ realization of behaviors they actually did not demonstrate as originally thought at the beginning of the program. Observers’ scores tended to be higher than students’ scores; however, minimal change in posttest scores could be attributed to not using the same observers for the pre and post assessments.

This study aimed to assess the potential of the Clinical Dementia Rating (CDR) Scale in the prognosis of dementia in elderly subjects.

Dementia staging severity is clinically an essential task, so the authors used machine learning (ML) on the magnetic resonance imaging (MRI) features to locate and study the impact of various MR readings onto the classification of demented and nondemented patients. The authors used cross-sectional MRI data in this study. The designed ML approach established the role of CDR in the prognosis of inflicted and normal patients. Moreover, the pattern analysis indicated CDR as a strong cohort amongst the various attributes, with CDR to have a significant value of p < 0.01. The authors employed 20 ML classifiers.

The mean prediction accuracy varied with the various ML classifier used, with the bagging classifier (random forest as a base estimator) achieving the highest (93.67%). A series of ML analyses demonstrated that the model including the CDR score had better prediction accuracy and other related performance metrics.

The results suggest that the CDR score, a simple clinical measure, can be used in real community settings. It can be used to predict dementia progression with ML modeling.