Marketing applications: from Angry Birds to happy marketers

This marketing teaching case is focussed on the rapidly emergent industry associated with Apps for mobile devices. After setting the context in respect of the awe-inspiring numbers associated with these markets the case is made that innovative marketing is happening across all parts of the basic marketing framework – the 4Ps. The case presents many specific examples of marketing related decision making and outcomes, focussing on games-Apps such as Rovio’s best-selling Angry Birds game

Nostalgia, reflexivity, and the narratives of self : reflections on Devine's 'removing the rough edges?'

This paper offers some theoretical insights on Devine’s account of the Riverside Museum in Glasgow. It elaborates on three interrelated themes authors have derived from Devine’s report: 1) how historical representations arouse nostalgic sensations and sensibilities in museum visitors 2) the role of narratives in visitors’ development of their nostalgic experiences 3) the importance of engagement to the creation of such nostalgic experiences. The paper contributes to the existing literature on nostalgia, experiential consumption, and the museum experience literature by establishing a relationship between nostalgia, reflexivity, and individuals’ narratives of self

Guest editorial

Sustainability and Corporate Social Responsibility (CSR) in hospitality and tourism is laden with contradictions. From the simple “carbon offsetting” of budget flights to the warning from the Maldivian Government that their country will disappear due to rising water levels whilst also building, in one year, at least seven additional airports to service their resort islands. The academic literature does not always help; the continually inconclusive or contradictory findings of financial impact studies, often meaningless CSR reporting, and consumer cynicism over perceived “green-washing” activities (Farrington et al., 2017) further contribute to the lack of clarity in this area. There is a need for a substantive move towards sustainable, ethical, responsible, environmentally or socially friendly strategies, but also towards concern for the well-being of future generations in the coming decades (Farrington et al., 2017; Jones et al., 2016; Wells et al., 2016a). Despite continued interest, this is a challenge for many countries, particularly with regards to meeting the ever-shifting opinions and customer expectations surrounding environmental issues pertaining to modern hospitality and tourism. Research should fundamentally debate the relevance and application of sustainability to the sector and its relationship with external stakeholders, and move away from narrow focuses. More specifically, ‘one size does not fit all’ with regards to sustainability and CSR, hence societies and organisations with different cultures and beliefs may be motivated to be involved in sustainability and CSR developments for different reasons, and may also face diverse barriers to implementation (Nyahunzvi, 2013; Thompson et al., 2018; Wells et al., 2015; Wells et al., 2016b; Xu, 2014; Yadav et al., 2016). Hence, the goal of this special issue was to encourage new theoretical and empirical development on sustainability and CSR studies in the hospitality and tourism field

How much for your kidney? The rise of the global transplant tourism industry

The term 'Transplant Tourism' is becoming commonly used to describe any form of travel that serves in the attainment of new organs; this practice is utterly condemned by the medical community and the World Health Organisation. Medical Tourism involves tourists travelling to, 'obtain medical, dental and surgical care while simultaneously being holidaymakers' (Connell, 2006, p. 1094). British Medical Journal (2008) highlights that Medical Tourism is a billion dollar industry, where companies advertise health services and attract patients for a fraction of the price they would have paid at home (Turner, 2008a). However, the typically legitimate Medical Tourism industry's reputation is being tarnished by its association with Transplant Tourism. Human organs used in transplantation can be obtained in two ways: live organ donation or cadaveric organ procurement (Lamb, 1990). In general, recipients prefer having living donor transplants over deceased ones, as the former offer them a better chance of survival (Steinberg, 2004). There is a worldwide struggle to meet the demand for organs; the gap between supply and demand has stimulated global organ trade and transplant tourism. Transplant Tourism has been overlooked within tourism literature and hoping to begin a debate, this note investigates the concept of Transplant Tourism, outlining why it cannot, in general, be considered a legitimate part of the Medical Tourism industry

Optimizing Parameters of the DC Power Flow

Many power system operation and planning problems use the DC power flow approximation to address computational challenges from the nonlinearity of the AC power flow equations. The DC power flow simplifies the AC power flow equations to a linear form that relates active power flows to phase angle differences across branches, parameterized by coefficients based on the branches' susceptances. Inspired by techniques for training machine learning models, this paper proposes an algorithm that seeks optimal coefficient and bias parameters to improve the DC power flow approximation's accuracy. Specifically, the proposed algorithm selects the coefficient and bias parameter values that minimize the discrepancy, across a specified set of operational scenarios, between the power flows given by the DC approximation and the power flows from the AC equations. Gradient-based optimization methods like Broyden-Fletcher-Goldfarb-Shanno (BFGS), Limited-Memory BFGS (L-BFGS), and Truncated Newton Conjugate-Gradient (TNC) enable solution of the proposed algorithm for large systems. After an off-line training phase, the optimized parameters are used to improve the accuracy of the DC power flow during on-line computations. Numerical results show several orders of magnitude improvements in accuracy relative to a hot-start DC power flow approximation across a range of test cases

AC Power Flow Feasibility Restoration via a State Estimation-Based Post-Processing Algorithm

This paper presents an algorithm for restoring AC power flow feasibility from solutions to simplified optimal power flow (OPF) problems, including convex relaxations, power flow approximations, and machine learning (ML) models. The proposed algorithm employs a state estimation-based post-processing technique in which voltage phasors, power injections, and line flows from solutions to relaxed, approximated, or ML-based OPF problems are treated similarly to noisy measurements in a state estimation algorithm. The algorithm leverages information from various quantities to obtain feasible voltage phasors and power injections that satisfy the AC power flow equations. Weight and bias parameters are computed offline using an adaptive stochastic gradient descent method. By automatically learning the trustworthiness of various outputs from simplified OPF problems, these parameters inform the online computations of the state estimation-based algorithm to both recover feasible solutions and characterize the performance of power flow approximations, relaxations, and ML models. Furthermore, the proposed algorithm can simultaneously utilize combined solutions from different relaxations, approximations, and ML models to enhance performance. Case studies demonstrate the effectiveness and scalability of the proposed algorithm, with solutions that are both AC power flow feasible and much closer to the true AC OPF solutions than alternative methods, often by several orders of magnitude in the squared two-norm loss function

AC Power Flow Informed Parameter Learning for DC Power Flow Network Equivalents

This paper presents an algorithm to optimize the parameters of power systems equivalents to enhance the accuracy of the DC power flow approximation in reduced networks. Based on a zonal division of the network, the algorithm produces a reduced power system equivalent that captures inter-zonal flows with aggregated buses and equivalent transmission lines. The algorithm refines coefficient and bias parameters for the DC power flow model of the reduced network, aiming to minimize discrepancies between inter-zonal flows in DC and AC power flow results. Using optimization methods like BFGS, L-BFGS, and TNC in an offline training phase, these parameters boost the accuracy of online DC power flow computations. In contrast to existing network equivalencing methods, the proposed algorithm optimizes accuracy over a specified range of operation as opposed to only considering a single nominal point. Numerical tests demonstrate substantial accuracy improvements over traditional equivalencing and approximation methods

Keeping your audience : presenting a visitor engagement scale

Understanding visitors’ level of engagement with tourist attractions is vital for successful heritage management and marketing. This paper develops a scale to measure visitors’ level of engagement in tourist attractions. It also establishes a relationship between the drivers of engagement and level of engagement using Partial Least Square, whereby both formative and reflective scales are included. The structural model is tested with a sample of 625 visitors at Kelvingrove Museum in Glasgow, UK. The empirical validation of the conceptual model supports the research hypotheses. Whilst prior knowledge, recreational motivation and omnivore-univore cultural capital positively affect visitors’ level of engagement, there is no significant relationship between reflective motivation and level of engagement. These findings contribute to a better understanding of visitor engagement in tourist attractions. A series of managerial implications are also proposed

Co-Optimization of Damage Assessment and Restoration: A Resilience-Driven Dynamic Crew Allocation for Power Distribution Systems

This study introduces a mixed-integer linear programming (MILP) model, effectively co-optimizing patrolling, damage assessment, fault isolation, repair, and load re-energization processes. The model is designed to solve a vital operational conundrum: deciding between further network exploration to obtain more comprehensive data or addressing the repair of already identified faults. As information on the fault location and repair timelines becomes available, the model allows for dynamic adaptation of crew dispatch decisions. In addition, this study proposes a conservative power flow constraint set that considers two network loading scenarios within the final network configuration. This approach results in the determination of an upper and a lower bound for node voltage levels and an upper bound for power line flows. To underscore the practicality and scalability of the proposed model, we have demonstrated its application using IEEE 123-node and 8500-node test systems, where it delivered promising results