SWOT analysis, a commonly used tool for strategic planning, is traditionally a form of brainstorming. Hence, it has been criticised that it is likely to hold subjective views of the individuals who participate in a brainstorming session and that SWOT factors are not prioritized by their significance thus it may result in an improper strategic action.While most studies of SWOT analysis have only focused on solving these shortcomings separately, this study offers an approach to diminish both shortcomings by applying Importance-Performance Analysis (IPA) to identify SWOT based on customer satisfaction surveys which produces prioritized SWOT corresponding to the customers' perception. Through the use of IPA based SWOT analysis, it is expected that a organisation can efficiently formulate strategic planning as the SWOT factors that should be maintained or improved can be clearly identified based on customers' viewpoints. The application of the IPA based SWOT analysis was illustrated and evaluated through a case study of Higher Education Institutions in Thailand. The evaluation results showed that SWOT analysis of the case study accurately reflected the organisation s' situations thereby demonstrating the validity of this study.

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Demand for autonomous swarms, where robots can cooperate with each other without human intervention, is set to grow rapidly in the near future. Currently, one of the main challenges in swarm robotics is understanding how the behaviour of individual robots leads to an observed emergent collective performance. In this paper, a novel approach to understanding robot swarms that perform foraging is proposed in the form of the InformationCost-Reward (ICR) framework. The framework relates the way in which robots obtain and share information (about where work needs to be done) to the swarm's ability to exploit that information in order to obtain reward efficiently in the context of a particular task and environment. The ICR framework can be applied to analyse underlying mechanisms that lead to observed swarm performance, as well as to inform hypotheses about the suitability of a particular robot control strategy for new swarm missions. Additionally, the informationcentred understanding that the framework offers paves a way towards a new swarm design methodology where general principles of collective robot behaviour guide algorithm design.

Agent-based modelling and simulation (ABMS) has been used by researchers from a variety of disciplines to study a range of phenomena. At present, ABMS is vastly underutilized in organizational psychology, yet we believe it offers a range of potential benefits that are currently not well catered for by existing tools. In this paper, we introduce ABMS and explain how it differs from current approaches. We illustrate the potential advantages of the approach through a range of examples and through the identification of opportunities in the field of organizational psychology. We also highlight potential limitations of the ABMS approach, and discuss the circumstances under which it may make a worthwhile contribution. Practitioner PointsThis paper outlines ABMS and explains how it adds to the existing toolset of the organizational psychologist. Practitioners will find ABMS and this paper particularly useful:• When they are working in high-risk environments, where getting it wrong is costly.• Where there are practical or ethical difficulties in conducting real-world research.• Where they want to develop and test more holistic interpretations of complex systems and problems.• Where they wish to examine feedback loops and/or the impact of time on behaviour.Computer simulations have existed for several decades. The earliest models were enabled by the first computers in the 1960s, though such models were far less sophisticated than those of today. Computer models and simulations have become

An important characteristic of a robot swarm that must operate in the real world is the ability to cope with changeable environments by exhibiting behavioural plasticity at the collective level. For example, a swarm of foraging robots should be able to repeatedly reorganise in order to exploit resource deposits that appear intermittently in different locations throughout their environment. In this paper, we report on simulation experiments with homogeneous foraging robot teams and show that analysing swarm behaviour in terms of information flow can help us to identify whether a particular behavioural strategy is likely to exhibit useful swarm plasticity in response to dynamic environments. While it is beneficial to maximise the rate at which robots share information when they make collective decisions in a static environment, plastic swarm behaviour in changeable environments requires regulated information transfer in order to achieve a balance between the exploitation of existing information and exploration leading to acquisition of new information. We give examples of how information flow analysis can help designers to decide on robot control strategies with relevance to a number of applications explored in the swarm robotics literature.

Abstract-The paper introduces a conceptual model for the design of serious games and uses the Technology Acceptance Model (TAM) for its validation. A specially developed game introduced international students to public transport in Southampton. After completing the game, participants completed a short questionnaire and the data was analysed using structural equation modelling (SEM). The results identified the attributes and combinations of attributes that led the learner to accept and to use the serious game for learning. These findings are relevant in helping game designers and educational practitioners design serious games for effective learning.

When is it profitable for robots to forage collectively? Here we compare the ability of swarms of simulated bio-inspired robots to forage either collectively or individually. The conditions under which recruitment (where one robot alerts another to the location of a resource) is profitable are characterised, and explained in terms of the impact of three types of interference between robots (physical, environmental, and informational). Key factors determining swarm performance include resource abundance, the reliability of shared information, time limits on foraging, and the ability of robots to cope with congestion around discovered resources and around the base location. Additional experiments introducing odometry noise indicate that collective foragers are more susceptible to odometry error.