Cultural influences moderating learners’ adoption of serious 3D games for managerial learning

Purpose The purpose of this paper is to investigate whether learners from different cultures adopt a serious 3D game to facilitate the learning of transferable managerial skills (ethics) and knowledge. Design/methodology/approach A cross-sectional, cross-country survey study (n=319) was conducted recruiting participants from one North American and two British universities. The survey data and the conceptual model have been analysed and tested using confirmatory factor analysis and structural equation modelling. Findings Participants displayed positive attitudes towards the 3D game and responded positively to theory presented as “real-life” scenarios; gamification techniques such as interactions and dialogue, and rewards and progression levels, which are part of the game, albeit the participants’ adoption was driven more by extrinsic motivations (rewards) than intrinsic ones (ease of use and entertainment). In addition, the empirical results suggest that when gender is taken into account, the perceptions and needs of cross-cultural learners in serious gaming environments vary and display characteristics that are similar to Rogers’ five adopter categories; thus, culture could significantly shape learners’ decisions to adopt a serious game as a managerial learning tool. Research limitations/implications For future researchers, this paper highlights various levels of training, support and promotional awareness that need to be considered to facilitate the adoption of serious games for managerial learning. Practical implications For academics and practitioners in work-based learning and managerial training environments, this paper highlights the salient factors that need to be inherent in a serious 3D game, and best practices for scaffolding existing instructional approaches or training interventions. Originality/value In light of Rogers’ five adopter categories, this cross-country study involving culturally diverse learners provides key insight into the potential application of serious games as a practice-based learning instrument in academia and industry

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-the-art data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored

Changing the Game : A Case for Gamifying Knowledge Management

Purpose: This exploratory paper investigates gamification as a medium for knowledge workers to interact with each other. The paper aims to open the discussion around the sustaining impact that gamification might have on knowledge management. Design/methodology/approach: The paper employs an exploratory literature review investigating the current state of the art in relation to knowledge management and gamification; this literature review serves as the starting point of subsequent theorizing. Findings: Based on the literature review we theorize that the use of gamification in knowledge management can go far beyond the motivational aspects. To name just a few uses of gamification, it can help in: supporting flexibility, facilitating transparency and therefore improving trust, visualizing skills and competences as well as generating requirements for new competences, and promoting a collaborative environment among the knowledge workers. Research limitations/implications: This paper opens the discussion around knowledge management and gamification and suggests a wide range of areas for further research. Practical implications: In this paper we argue that by looking at gamification as more than just a set of tools for improving motivation and engagement a company can address some pitfalls of a particular type of knowledge workers. Social implications: Originality/value: Gamification is a new, but increasingly popular approach, which has been shown to be to be powerful in many areas. This paper is novel in that it initiates a dialogue around the impact that gamification might have on knowledge management

21st Century Simulation: Exploiting High Performance Computing and Data Analysis

This paper identifies, defines, and analyzes the limitations imposed on Modeling and Simulation by outmoded paradigms in computer utilization and data analysis. The authors then discuss two emerging capabilities to overcome these limitations: High Performance Parallel Computing and Advanced Data Analysis. First, parallel computing, in supercomputers and Linux clusters, has proven effective by providing users an advantage in computing power. This has been characterized as a ten-year lead over the use of single-processor computers. Second, advanced data analysis techniques are both necessitated and enabled by this leap in computing power. JFCOM's JESPP project is one of the few simulation initiatives to effectively embrace these concepts. The challenges facing the defense analyst today have grown to include the need to consider operations among non-combatant populations, to focus on impacts to civilian infrastructure, to differentiate combatants from non-combatants, and to understand non-linear, asymmetric warfare. These requirements stretch both current computational techniques and data analysis methodologies. In this paper, documented examples and potential solutions will be advanced. The authors discuss the paths to successful implementation based on their experience. Reviewed technologies include parallel computing, cluster computing, grid computing, data logging, OpsResearch, database advances, data mining, evolutionary computing, genetic algorithms, and Monte Carlo sensitivity analyses. The modeling and simulation community has significant potential to provide more opportunities for training and analysis. Simulations must include increasingly sophisticated environments, better emulations of foes, and more realistic civilian populations. Overcoming the implementation challenges will produce dramatically better insights, for trainees and analysts. High Performance Parallel Computing and Advanced Data Analysis promise increased understanding of future vulnerabilities to help avoid unneeded mission failures and unacceptable personnel losses. The authors set forth road maps for rapid prototyping and adoption of advanced capabilities. They discuss the beneficial impact of embracing these technologies, as well as risk mitigation required to ensure success

Open the Jail Cell Doors, HAL: A Guarded Embrace of Pretrial Risk Assessment Instruments

In recent years, criminal justice reformers have focused their attention on pretrial detention as a uniquely solvable contributor to the horrors of modern mass incarceration. While reform of bail practices can take many forms, one of the most pioneering and controversial techniques is the adoption of actuarial models to inform pretrial decision-making. These models are designed to supplement or replace the unpredictable and discriminatory status quo of judicial discretion at arraignment. This Note argues that policymakers should experiment with risk assessment instruments as a component of their bail reform efforts, but only if appropriate safeguards are in place. Concerns for protecting individual constitutional rights, mitigating racial disparities, and avoiding the drawbacks of machine learning are the key challenges facing reformers and jurisdictions adopting pretrial risk assessment instruments. Absent proper precautions, risk assessment instruments can reinforce, rather than alleviate, modern criminal justice disparities. Drawing from a case study of New Jersey’s recent bail reform program, this Note examines the efficacy, impact, and pitfalls of risk assessment instrument adoption. Finally, this Note offers a broad framework for policymakers seeking to thoughtfully experiment with risk assessment instruments in their own jurisdictions

About Challenges in Data Analytics and Machine Learning for Social Good

The large number of new services and applications and, in general, all our everyday activities resolve in data mass production: all these data can become a golden source of information that might be used to improve our lives, wellness and working days. (Interpretable) Machine Learning approaches, the use of which is increasingly ubiquitous in various settings, are definitely one of the most effective tools for retrieving and obtaining essential information from data. However, many challenges arise in order to effectively exploit them. In this paper, we analyze key scenarios in which large amounts of data and machine learning techniques can be used for social good: social network analytics for enhancing cultural heritage dissemination; game analytics to foster Computational Thinking in education; medical analytics to improve the quality of life of the elderly and reduce health care expenses; exploration of work datafication potential in improving the management of human resources (HRM). For the first two of the previously mentioned scenarios, we present new results related to previously published research, framing these results in a more general discussion over challenges arising when adopting machine learning techniques for social good