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The prediction of a context, especially of a user’s location, is a fundamental task in the field of pervasive computing. Such predictions open up a new and rich field of proactive adaptation for context-aware applications. This study/paper aims to propose a methodology that predicts a user’s location on the basis of a user’s mobility history.

Contextual information is used to find the points of interest that a user visits frequently and to determine the sequence of these visits with the aid of spatial clustering, temporal segmentation and speed filtering.

The proposed method was tested with a real data set using several supervised classification algorithms, which yielded very interesting results.

The method uses contextual information (current position, day of the week, time and speed) that can be acquired easily and accurately with the help of common sensors such as GPS.

A challenging task for context‐aware pervasive systems is reasoning about context in uncertain environments where sensors can be inaccurate or unreliable and inferred situations ambiguous and uncertain. This paper aims to address this grand challenge, with research in context awareness to provide feasible solutions by means of theoretical models, algorithms and reasoning approaches.

This paper proposes a theoretical model about context and a set of context verification procedures, built over the model and implemented in a context reasoning engine prototype. The verification procedures utilize beneficial characteristics of spatial representation of context and also provide guidelines based on heuristics that lead to resolution of conflicts arising due to context uncertainty. The engine's reasoning process is presented and it is shown how the proposed modeling and verification approach contributes in tackling the uncertainty associated with the reasoning task. The paper experimentally evaluates this approach with a distributed simulation of a sensor‐based office environment with unreliable and inaccurate sensors.

Important features of the model are dynamic aspects of context, such as context trajectory and stability of a pervasive system in given context. These can also be used for context verification as well as for context prediction. The model strength is also in its generality and its ability to model a variety of context‐aware scenarios comprising different types of information.

The paper describes a theoretical model for context and shows it is useful not only for context representation but also for developing reasoning and verification techniques for uncertain context.

Location‐prediction enables the next generation of location‐based applications. The purpose of this paper is to provide a historical summary of research in personal location‐prediction. Location‐prediction began as a tool for network management, predicting the load on particular cellular towers or WiFi access points. With the increasing popularity of mobile devices, location‐prediction turned personal, predicting individuals' next locations given their current locations.

This paper includes an overview of prediction techniques and reviews several location‐prediction projects comparing the raw location data, feature extraction, choice of prediction algorithms and their results.

A new trend has emerged, that of employing additional context to improve or expand predictions. Incorporating temporal information enables location‐predictions farther out into the future. Appending place types or place names can improve predictions or develop prediction applications that could be used in any locale. Finally, the authors explore research into diverse types of context, such as people's personal contacts or health activities.

This overview provides a broad background for future research in prediction.

The aim is to take decision making about people beyond “gut feel” and to make psychological measurement relevant to the key challenge of our time, which is to give strategic shape to the human aspect of business. The thesis is that, if we wish to obtain better results from our decisions than we do from gut feel alone, we must have the capacity to predict accurately the behaviours which lead to success in a given context. Psychological assessment gives us this capacity because it measures key and critical attributes, specifies their interaction, predicts their impact on behaviour and so constructs a model of psychological economy to forecast the success of the self‐in‐context. How psychological measurement accomplishes these tasks is explained and some consideration is given to key points of method.

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.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

Trading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they largely depend upon a range of variables such as electricity demand and the feed-in from renewable energy sources. Hence, the purpose of this paper is to provide accurate forecasts..

This paper aims at comparing different predictors stemming from supply-side (solar and wind power generation), demand-side, fuel-related and economic influences. For this reason, this paper implements a broad range of non-linear models from machine learning and draw upon the information-fusion-based sensitivity analysis.

This study disentangles the respective relevance of each predictor. This study shows that external predictors altogether decrease root mean squared errors by up to 21.96%. A Diebold-Mariano test statistically proves that the forecasting accuracy of the proposed machine learning models is superior.

The performance gain from including more predictors might be larger than from a better model. Future research should place attention on expanding the data basis in electricity price forecasting.

When developing pricing models, practitioners can achieve reasonable performance with a simple model (e.g. seasonal-autoregressive moving-average) that is built upon a wide range of predictors.

The benefit of adding further predictors has only recently received traction; however, little is known about how the individual variables contribute to improving forecasts in machine learning.

Context‐aware mobile computing extends the horizons of the conventional computing model to a ubiquitous computing environment that serves users at anytime, anywhere. To achieve this, mobile applications need to adapt their behaviors to the changing context. The purpose of this paper is to present a generalized adaptive middleware infrastructure for context‐aware computing.

Owing to the vague nature of context and uncertainty in context aggregation for making adaptation decisions, the paper proposes a fuzzy‐based service adaptation model (FSAM) to improve the generality and effectiveness of service adaptation using fuzzy theory.

By the means of fuzzification of the context and measuring the fitness degree between the current context and the predefined optimal context, FSAM selects the most suitable policy to adopt for the most appropriate service. The paper evaluates the middleware together with the FSAM inference engine by using a Campus Assistant application.

The paper is of value in presenting a generalized adaptive middleware infrastructure for context‐aware computing and also comparing the performance of the fuzzy‐based solution with a conventional threshold‐based approach for context‐aware adaptation.

This study aims to investigate the effects of workplace bullying on the coping strategies (responses) and counterproductive behavior of hospitality employees.

The sample consisted of 284 luxury hotel employees in the Korean hospitality industry. SPSS and AMOS were the statistical programs used to verify the hypotheses of the present study. Confirmatory factor analysis and reliability analysis were conducted to verify the validity and reliability of the measured items. Before verification of the hypotheses, directivity between factors derived through correlation analysis was verified, and causal relationships with regard to the three hypotheses were verified through the structural equation model.

Organizational and work-related bullying had a significant effect on task coping, whereas personal bullying had a significant effect on emotional and avoidance coping. The results also showed that positive task coping did not significantly affect counterproductive behavior, but negative coping, such as emotional and avoidance responses, significantly affected employees’ counterproductive behavior.

The present study verified that coping responses in work situations can differ depending on the type of workplace bullying that occurs. Task coping, a positive coping strategy, was affected by organizational and work-related bullying, whereas emotional and avoidance coping, negative coping strategies, were negatively affected by personal bullying. Consequently, the possibility of harmful actions against organizations varies depending on the coping strategies chosen by employees who are exposed to bullying. Therefore, appropriate education should be offered to employees to use positive and proactive work-oriented coping strategies when dealing with bullying rather than negative methods such as emotional or avoidance coping.