In response to the United Kingdom’s government’s desire to improve the performance of tourism, hospitality, and leisure small medium-sized enterprises, this article analyzes the performance measurement processes within 10 best practice organizations. Related to contemporary approaches to improving business performance in the management literature, performance measurement approaches are analyzed using the balanced scorecard framework. An exploratory case study approach using the balanced scorecard as the theoretical framework was taken to explore and elicit critical success factors in performance measurement. Results revealed that four key concepts drove measurement and performance evaluation systems across the sample. These were the exercising of budgetary control with a view to increasing total revenue, the undertaking of customer relationship management as a means of improving quality of service and customer retention, the necessity for strategic management in managing internal business processes, and collaboration (both inter and intra) to drive innovation and learning. The article also proposes a balanced scorecard template for hotels.

The ability of various econometric and univariate time-series models to generate accurate forecasts of international tourism demand is evaluated. Accuracy is assessed in terms of error magnitude and also directional change error. Statistical testing for both forecasting bias and directional change forecasting performance is introduced. The empirical results show that for 1-year-ahead forecasting, the time-varying parameter model performs consistently well. However, for 2- and 3-years-ahead forecasting, the best model varies according to the forecasting error criterion under consideration. This highlights the importance (for longer term forecasts) of selecting a forecasting method that is appropriate for the particular objective of the forecast user.

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The recent Covid-19 outbreak has had a tremendous impact on the world, and many countries are struggling to help incoming patients and at the same time, rapidly enact new public health measures such as lock downs. Many of these decisions are guided by the outcomes of so-called Susceptible-Exposed-Infectious-Recovered (SEIR) models that operate on a national level. Here we introduce the Flu And Coronavirus Simulator (FACS), a simulation tool that models the viral spread at the sub-national level, incorporating geospatial data sources to extract buildings and residential areas in a region. Using FACS, we can model Covid-19 spread at the local level, and provide estimates of the spread of infections and hospital arrivals for different scenarios. We validate the simulation results with the ICU admissions obtained from the local hospitals in the UK. Such validated models can be used to support local decision-making for an effective health care capability response to the epidemic.

eXfiltration Advanced Persistent Threats (XAPTs) increasingly account for incidents concerned with critical information exfiltration from High Valued Targets (HVTs). Existing Cyber Defence frameworks and data fusion models cannot cope with XAPTs due to a lack of provision for multi-phase attacks characterized by uncertainty and conflicting information. The Markov Multi-phase Transferable Belief Model (MM-TBM) extends the Transferable Belief Model to address the multi-phase nature of cyber-attacks and to obtain previously indeterminable Cyber SA. As a data fusion technique, MM-TBM constitutes a novel approach for performing hypothesis assessment and evidence combination across phases, by means of a new combination rule, called the Multi-phase Combination Rule with conflict Reset (MCR 2). The impact of MM-TBM as a Cyber Situational Awareness capability and its implications as a multi-phase data fusion theory have been empirically validated through a series of scenario-based Cyber SA experiments for detecting, tracking, and predicting XAPTs.

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Managing the dynamics of customer behaviour in the rapidly emerging multi-channel e-business environment is complex. Establishing an enduring and profitable dialogue with a customer requires that online relationship management applications can accommodate the channel variety in the customer’s e-communications portfolio, including their buyer behaviour dynamics. With reference to the global hotel industry, this paper considers the impact of Internet multi-channel access on the customer decision-making process; how differences in buyer behaviour and loyalty level influence the relationship management process; and the implications of effectively managing buyer behaviour and the provision of multi-channel customer accessibility for competitive advantage.