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This study aims to investigate the feasibility of developing general predictive models for using the learning management system (LMS) data to predict student performances in various courses. The authors focused on examining three practical but important questions: are there a common set of student activity variables that predict student performance in different courses? Which machine-learning classifiers tend to perform consistently well across different courses? Can the authors develop a general model for use in multiple courses to predict student performance based on LMS data?

Three mandatory undergraduate courses with large class sizes were selected from three different faculties at a large Western Canadian University, namely, faculties of science, engineering and education. Course-specific models for these three courses were built and compared using data from two semesters, one for model building and the other for generalizability testing.

The investigation has led the authors to conclude that it is not desirable to develop a general model in predicting course failure across variable courses. However, for the science course, the predictive model, which was built on data from one semester, was able to identify about 70% of students who failed the course and 70% of students who passed the course in another semester with only LMS data extracted from the first four weeks.

The results of this study are promising as they show the usability of LMS for early prediction of student course failure, which has the potential to provide students with timely feedback and support in higher education institutions.

This research aims to develop conceptual phase building project cost forecasting models by exploring the relationship of existing plan shape complexity indices and general design morphology parameters with total construction cost.

Plan shape indices proposed to date by the literature for measuring building design complexity are critically reviewed. Building morphology is also dictated by town planning restrictions such as plot coverage ratio or number of storeys. This study analyses historical data collected from 49 residential building projects to develop multiple linear regression (MLR) and artificial neural network (ANN) models for forecasting construction cost. Existing plan shape coefficients are calculated to evaluate the geometrical complexity of sampled projects. Ten regression-based cost estimating equations are totally derived from stepwise backward and forward methods, and their predictive accuracy is contrasted: to performance levels reported in past studies and to ANN models developed in this research with multilayer perceptron architecture.

Analysis of plan shape indices revealed that 85.7% of examined past projects possess a high degree of design complexity, hence resulting in expensive initial decisions. This highlights the need for more effective early design stage decision-making by developing new building economic tools. The most accurate regression model, with a mean absolute percentage error (MAPE) of 19.2%, predicts the log of total cost from wall to floor index and total building envelope surface. Other explanatory variables resulting in MAPE values in the order of 20%–22% are total volume, volume above ground level, gross floor area below ground level, gross floor area per storey and total number of storeys. The overall MAPE of regression-based equations is 24.3% whilst ANN models are slightly more accurate with MAPE scores of 21.8% and 21.6% for one hidden and two hidden layers, respectively. The most accurate forecasting model in the research is the ANN with two hidden layers and the sigmoid activation function which predicts total building cost from total building volume (19.1%).

This paper introduces MLR-based and ANN-based conceptual construction cost forecasting models which are founded solely on building morphology design parameters and compare favourably with previous studies with an average predictive accuracy less than 25%. This paper is expected to be beneficial to both practitioners and academics in the built environment towards more effective cost planning of building projects. The methodology suggested can further be implemented in other countries provided that accurate and relevant data from historical projects are used.

Interdependency among industries is vital for understanding economic structures and managing industrial portfolios. However, it is hard to precisely model the interconnecting structure among industries. One of the reasons is that the interdependencies show a different pattern in tail events. This paper aims to investigate industry interdependency with the tail events.

General predictive model of Rapach et al. (2016) is extended to an interdependency model via least absolute shrinkage and selection operator quantile regression and network analysis. A dynamic network approach was applied on the Fama–French industry portfolios to study the time-varying interdependencies.

A denser network with heterogeneous central industries is found in tail cases. Significant interdependency varieties across time are shown under dynamic network analysis. Market volatility is identified as an influential factor of industry connectedness as well as clustering tendency under both normal and tail cases. Moreover, combining dynamic network with prediction direction information into out-of-sample industry return forecasting, a lower tail case is obtained, which gives the most accurate prediction of one-month forward returns. Finally, the Sharpe ratio criterion prefers high-centrality portfolios when tail risks are considered.

This study examines the industry portfolio interactions under the framework of network analysis and also takes into consideration tail risks. The combination of economic interpretation and statistical methodology helps in having a clear investigation of industry interdependency. Moreover, a new trading strategy based on network centrality seems profitable in our data sample.

Advanced analytics‐driven data analyses allow enterprises to have a complete or “360 degrees” view of their operations and customers. The insight that they gain from such analyses is then used to direct, optimize, and automate their decision making to successfully achieve their organizational goals. Data, text, and web mining technologies are some of the key contributors to making advanced analytics possible. This paper aims to investigate these three mining technologies in terms of how they are used and the issues that are related to their effective implementation and management within the broader context of predictive or advanced analytics.

A range of recently published research literature on business intelligence (BI); predictive analytics; and data, text and web mining is reviewed to explore their current state, issues and challenges learned from their practice.

The findings are reported in two parts. The first part discusses a framework for BI using the data, text, and web mining technologies for advanced analytics; and the second part identifies and discusses the opportunities and challenges the business managers dealing with these technologies face for gaining competitive advantages for their businesses.

The study findings are intended to assist business managers to effectively understand the issues and emerging technologies behind advanced analytics implementation.

The purpose of this paper is to review the literature on maintenance management and suggest possible gaps from the point of view of researchers and practitioners.

The paper systematically categorizes the published literature and then analyzes and reviews it methodically.

The paper finds that important issues in maintenance management range from various optimization models, maintenance techniques, scheduling, and information systems etc. Within each category, gaps have been identified. A new shift in maintenance paradigm is also highlighted.

Literature on classification of maintenance management has so far been very limited. This paper reviews a large number of papers in this field and suggests a classification in to various areas and sub areas. Subsequently, various emerging trends in the field of maintenance management are identified to help researchers specifying gaps in the literature and direct research efforts suitably.

The paper contains a comprehensive listing of publications on the field in question and their classification according to various attributes. The paper will be useful to researchers, maintenance professionals and others concerned with maintenance to understand the importance of maintenance management

Action research (AR) has been used in many social science fields as a practical problem‐solving method. What makes this paradigm unique and viable is that it allows research and practice to co‐exist and co‐work simultaneously in achieving organizational goals. The implementation of the AR paradigm in the field of training and development (T&D) is not common; thus, this paper provides methods and tools to utilize AR in T&D efforts and practice. In this paper, AR and T&D are overviewed, and the possible implications of AR for T&D, using the taxonomy of performance, are examined as an avenue for analysis.

How can action research be made more rigorous? We discuss in this paper action research, positivism and some major criticisms of action research by positivists. We then examine issues relating the conduct of IS research in organisations through multiple iterations in the action research cycle proposed by Susman and Evered. We argue that the progress through iterations allows the researcher to gradually broaden the research scope and in consequence add generality to the research findings. A brief illustrative case is provided with a study on groupware introduction in a large civil engineering company. In the light of this illustrative case we contend that effective application of the iterative approach to action research has the potential to bring research rigour up closer to standards acceptable by positivists and yet preserve the elements that characterise action research as such.

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

Promoting a safe workplace for everyone is a key tenet of Sustainable Development Goal 8 (SDG-8), which focuses on promoting inclusive and sustainable economic growth, employment and decent work for all. Therefore, this study explores how responsible leadership ensures a psychologically safe workplace for everyone, leveraging employee-oriented human resource management. Specifically, drawing on signalling theory, this study aims to examine the impact of responsible leadership on employee-oriented HRM and the subsequent effect of employee-oriented HRM on employees' psychological safety. Furthermore, it investigates the mediating role of employee-oriented HRM in the relationship between responsible leadership and psychological safety.

Data was collected from banking professionals through a survey questionnaire. A total of 270 samples were collected using both online and face-to-face data collection strategies. The data was analysed using the Partial Least Squares Structural Equation Modelling (PLS-SEM) approach.

The findings reveal that responsible leadership ensures employee-oriented HRM, which subsequently enhances employees' psychological safety. Further, the results suggest that employee-oriented HRM acts as a mediator between responsible leadership and psychological safety.

Past studies have often emphasized HRM practices as antecedents of various attitudes and behaviours. The present study offers a novel contribution by conceptualizing and empirically validating employee-oriented HRM as a mechanism that links responsible leadership and psychological safety. It stands as the first of its kind to establish this significant relationship, shedding new light on the dynamics between responsible leadership, HRM practices and employees' sense of psychological safety.

We compare the finite sample power of short- and long-horizon tests in nonlinear predictive regression models of regime switching between bull and bear markets, allowing for time varying transition probabilities. As a point of reference, we also provide a similar comparison in a linear predictive regression model without regime switching. Overall, our results do not support the contention of higher power in longer horizon tests in either the linear or nonlinear regime switching models. Nonetheless, it is possible that other plausible nonlinear models provide stronger justification for long-horizon tests.