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Choice under risk has a large stochastic (unpredictable) component. This chapter examines five stochastic models for binary discrete choice under risk and how they combine with “structural” theories of choice under risk. Stochastic models are substantive theoretical hypotheses that are frequently testable in and of themselves, and also identifying restrictions for hypothesis tests, estimation and prediction. Econometric comparisons suggest that for the purpose of prediction (as opposed to explanation), choices of stochastic models may be far more consequential than choices of structures such as expected utility or rank-dependent utility.

The purpose of this paper is to introduce a model for identifying, storing and sharing contextual information across smartphone apps that uses the native device services. The authors present the idea of using user input and interaction within an app as contextual information, and how each app can identify and store contextual information.

Contexts are modeled as hierarchical objects that can be stored and shared by applications using native mechanisms. A proof-of-concept implementation of the model for the Android platform demonstrates contexts modelled as hierarchical objects stored and shared by applications using native mechanisms.

The model was found to be practically viable by implemented sample apps that share context and through a performance analysis of the system.

The contextual data-sharing model enables the creation of smart apps and services without being tied to any vendor’s cloud services.

This paper introduces a new approach for sharing context in smartphone applications that does not require cloud services.

A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour. Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy. Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration. A single-step application of machine learning (ML) is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy. The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.

This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars. The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data, and the second step detects abnormal fuel economy in relation to contextual information. Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model. The contextual anomaly is detected by following two approaches, kernel quantile estimator and one-class support vector machine. The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour. Any error beyond a threshold is classified as an anomaly. The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection. The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder, and the performance of both models is compared. The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.

A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder. Both models achieve prediction accuracy within a range of 98%–100% for prediction as a first step. Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%–100%, whereas the one-class support vector machine approach performs within the range of 99.3%–100%.

The proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns. However, it can be extended to correlate driver's physiological state such as fatigue, sleep and stress to correlate with driving behaviour and fuel economy. The anomaly detection approach here is limited to providing feedback to driver, it can be extended to give contextual feedback to the steering controller or throttle controller. In the future, a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.

The suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts. It can also be used as a training tool for improving driving efficiency for new drivers. It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.

This paper contributes to the existing literature by providing an ML pipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values. The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy. The main contributions for this approach are as follows: (1) a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption. (2) Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.

This paper aims to theorise and test a causal model of predominantly lean-driven quality improvement (QI) in the context of health-care clinical microsystems, examining the effects contextual factors in this setting have on improvement activity.

QI practitioners at a New Zealand District Health Board were surveyed on a range of contextual factors hypothesised to influence improvement outcomes. Survey responses were analysed via partial least squares path modelling to test the causal model that was designed to be consistent with the “model for understanding success in quality” (MUSIQ) model (Kaplan et al., 2012) adopted in health-care QI.

Defined variables for teamwork, respect for people, lean actions and negative motivating factors all demonstrated significant effects. These findings support the representation of the microsystem layer within the MUSIQ model. The final model predicted and explained perceived success well (adjusted R² = 0.58).

The sample was a non-probability sample and the sample size was small (n = 105), although power analysis indicated that we exceeded the minimum sample size (97 cases). Even though health-care processes have universality, this study was conducted in only one district in New Zealand.

The results support highly functional teamwork as the critical contextual factor in health-care QI outcomes and suggest lean-driven process improvement can be a valid mediating mechanism. The key recommendation for practitioners is to increase focus on human resource capability when initiating and supporting QI.

The originality is testing the robustness of the MUSIQ model specifically in a lean environment, which provides the context for QI. The paper provides a more detailed specification of contextual factors acting as exogenous variables that moderate the cause (lean actions) and the effect (perceived success).

Leadership studies which focus on categorising leadership styles have been critiqued for failure to consider the lived experience of leadership. The purpose of this paper is to use the framework of Jepson’s model of contextual dynamics to explore whether this framework assists understanding of the “how and why” of lived leadership experience within the nursing profession.

Themes for a purposeful literature search and review, having regard to the Jepson model, are drawn from the contemporary and dynamic context of nursing. Government reports, coupled with preliminary interviews with a nurse leadership team, guided selection of contextual issues.

The contextual interactions arising from managerialism, existing hierarchical models of leadership and increasing knowledge work provided insights into leadership experience in nursing, in the contexts of professional identity and changing educational and generational profiles of nurses. The authors conclude that employing a contextual frame provides insights in studying leadership experience. The author propose additions to the cultural and institutional dimensions of Jepson’s model.

The findings have implications for structuring and communicating key roles and policies relevant to nursing leadership. These include the need to: address perceptions around the legitimacy of current nursing leaders to provide clinical leadership; modify hierarchical models of nursing leadership; address implications of the role of the knowledge workers.

Observing nursing leadership through the lens of Jepson’s model of contextual dynamics confirms that this is an important way of exploring how leadership is enacted. The authors found, however, the model also provided a useful frame for considering the experience and understanding of leadership by those to be led.

The present study sought to determine whether or not there is an association between contextual effects of violent socialization on violent youth behavior across different nations.

The data in this study derive from the International Dating Violence Study, a dataset of over 17,000 college students collected in 32 different nations. Variables consist of various scales from the Personal Relationship Profile that focuses on experiences and behaviors occurring prior to age 15 years, as well as national indicators of violence. Multilevel modeling analysis was used to analyze the data.

The results indicate two important findings. First, violent socialization significantly varies across different national contexts and this contextual effect accounts for a significant proportion of variation in youth violence. Second, violent socialization, both within individual families and as a contextual effect within different nations, is significantly associated with increases in violent youth behavior across the nations.

The International Dating Violence Study is a cross-sectional convenience sample of college students, which is not representative of specific nations nor college students. Further, the present study classifies nations as a “community” rather than explore microlevel communities within a nation. Future research should focus on examining variation of area specific norms for subsets of communities within nations with representative samples of a general population.

The present study appears to be one of the first published studies offering empirical evidence for international research on the theoretical argument of the contextual effects of violence within a nation, and begins to increase knowledge among criminologists of such contextual factors being associated with youth violent behavior across different nations.

The purpose of this paper is to propose an approach to incorporate contextual information into collaborative filtering (CF) based on the restricted Boltzmann machine (RBM) and deep belief networks (DBNs). Traditionally, neither the RBM nor its derivative model has been applied to modeling contextual information. In this work, the authors analyze the RBM and explore how to utilize a user’s occupation information to enhance recommendation accuracy.

The proposed approach is based on the RBM. The authors employ user occupation information as a context to design a context-aware RBM and stack the context-aware RBM to construct DBNs for recommendations.

The experiments on the MovieLens data sets show that the user occupation-aware RBM outperforms other CF models, and combinations of different context-aware models by mutual information can obtain better accuracy. Moreover, the context-aware DBNs model is superior to baseline methods, indicating that deep networks have more qualifications for extracting preference features.

To improve recommendation accuracy through modeling contextual information, the authors propose context-aware CF approaches based on the RBM. Additionally, the authors attempt to introduce hybrid weights based on information entropy to combine context-aware models. Furthermore, the authors stack the RBM to construct a context-aware multilayer network model. The results of the experiments not only convey that the context-aware RBM has potential in terms of contextual information but also demonstrate that the combination method, the hybrid recommendation and the multilayer neural network extension have significant benefits for the recommendation quality.

The design of context‐aware mobile applications can be improved through a clear and in‐depth understanding of context and how it can be used to meet users' requirements. Using tourism as a case application, this paper aims to address the lack of understanding of context and tourists' goals.

This is achieved through a literature review of existing research and focus groups to gather information needs for tasks commonly executed by tourists.

This paper proposes the TILES (temporal, identity, location, environmental and social) model to define and classify five main contextual types, and properties associated with each type for tourism‐related applications. The TILES model (with 32 factors) derived from the analysis of the literature review is refined through inputs from two focus groups to incorporate an additional ten factors.

The TILES model can be generalised to support domains other than tourism, such as medical and edutainment.

The model will help to achieve a better understanding of context, users' information needs and their goals. In addition, this work extends findings in the field of context‐aware computing and information retrieval on mobile devices. Solution providers will also be able to adopt TILES as a framework for guiding the design of their context‐aware mobile applications.

An issue of managing a business (unit) as a whole successfully is perceived to belong to the fundamental issues within strategic management. This paper proposes that a business unit can be managed successfully in short and longer term in its focal contexts as a set of three recursive, competence-based, and process-based systems. Many elements of Stafford Beer's (1985) viable system model along the key competence-based theoretical bases are applied to this system design task. The outcome is an ideal, recursive template for advancing competence-based business management (CBBM) and its conceptual modeling. It is assumed that it is possible to design a business unit as a viable system that is capable of sustaining a separate existence at only three levels of hierarchy, as part of single or multi-business firms. Business-process models and their redesign processes are chosen as the 2nd-order, focal system which produces a business unit's competitiveness and solves longitudinal CBBM problems. One level of recursion down includes a unit's value creating, capturing, releveraging, and respective processes that enable to solve cross-sectional problems. One level of recursion up includes a unit's existential foresights and their crafting processes that solve existential problems. Recursivity is designed inside each system in terms of three kinds of subsystems for (a) primary value releveraging, process-model redesign, and business-foresight crafting, (b) the management of varieties in releveraging, modeling, and foreseeing, and (c) the monitoring and probing of all three systems. Systemic competences are incorporated inside respective systems. Such competences possess three flexibilities of absorption, attenuation, and amplification. At each level of recursion, a competence-based process is a unit of conceptual modeling of CBBM. A business unit is defined as a set of its purposeful processes. No thing or one is left outside them. Viability is ensured by real-time interaction and the 1st-, 2nd-, and 3rd-order feedback loops between three systems. Overall, the suggested, recursive, 3-system template is intended to serve future, compatible modeling efforts among interested, pioneering firms, professional CBBM modelers, scholars, and alike. Its novelty is produced by choosing and designing the CBBM modeling as the 2nd-order system-in-focus with its two recursions, by designing and using systemic, competence-based processes as the units of conceptualization, and by choosing and drawing the figures to illustrate the 3-system template in the ways that allow also business managers comprehend and apply the suggested template in practice.

The purpose of this paper is to discover the contextual factors affecting the development of digital library education (DLE) in Vietnam and to determine how those factors are enabling or hindering its development.

Based upon the literature and the theories of Fullan (2007), Nowlen (1988) and Rogers (2003), an initial model of contextual factors that were potentially affecting DLE development in Vietnam was developed. The model was then tested using a qualitative methodology involving interviews and focus groups with key stakeholders, along with the gathering of documentary evidence. The data were then analysed using the interactive model of Miles et al. (2014), and the initial model was then revised.

The initial model allowed for the depiction of potential factors, both internal and external, along with some relationships between the factors. Based on the data analysis, a revised model was developed composed of seven major contextual factors that were enabling and/or hindering DLE development in the Vietnamese context. These are grouped into three categories: external factors – government, IT infrastructure and social and cultural values; internal factors – stakeholders’ attitudes, DLE characteristics and the personal and organisational nexus; and change agents, which is both an internal and external factor. These factors had different levels of influence on DLE development and are inter-related.

No prior study has looked at contextual factors affecting DLE development in a developing country such as Vietnam.