Search results

Health-care marketing typically entails a coordinated set of outreach and communications designed to attract consumers (patients in the health-care context) who require services for a better health outcome and guide them throughout their health-care journey to achieve a higher quality of life. The purpose of this study is to understand the progress and trends in healthcare marketing strategy (HMS) literature between 2018 and 2022, with a special emphasis on the pre- and post-Covid-19 periods.

The authors examine 885 HMS-related documents from the WOS database between 2018 and 2022 that were extracted using a keyword-based search strategy. After that, the authors present the descriptive statistics related to the corpus. Finally, the authors use author co-citation analysis (ACA) and bibliographic coupling (BC) techniques to examine the corpus.

The authors present the descriptive statistics as research themes, emerging sub-research areas, leading journals, organisations, funding agencies and nations. Further, the bibliometric analysis reveals the existence of five thematic clusters: Cluster 1: macroeconomic and demographic determinants of healthcare service delivery; Cluster 2: strategies in healthcare marketing; Cluster 3: socioeconomics in healthcare service delivery; Cluster 4: data analytics and healthcare service delivery; Cluster 5: healthcare product and process innovations.

This study provides an in-depth analysis of the advancements made in HMS-related research between 2018 and 2022. In addition, this study describes the evolution of research in this field from before to after the Covid-19 pandemic. The findings of this study have both research and practical significance.

To the best of the authors’ knowledge, this is the first study of its kind to use bibliometric analysis to identify advancements and trends in HMS-related research and to examine the pattern before and after Covid-19 pandemic.

The manoeuvring of everyday spaces to nudge the population towards a physically active lifestyle or active living has been the hallmark of the policy modes of instrumental thinking for combating physical inactivity, particularly in urban spaces across the world. Thus, the active promotion of fitness activities by the postcolonial state signifies the centrality of the body in disciplining docile and inactive individuals to produce fit and active citizens. Such a population-based approach has often raised concerns about social and spatial justice and expressions of identity, liberty, and surveillance, even as everyday spaces in cities continue to exhibit elements of colonial governmentality. In the midst of this, the body continues to be central to the ways in which such a population makes sense of being physically active and ‘being healthy’ in their everyday lives. By employing a multi-sited ethnography conducted over a period of 10 months in different public parks in Delhi, the present chapter aims to understand the ways in which fitness activities are performed, produced, and practised in the city. While public parks themselves are a product of colonial urban governmentalities in Delhi, this chapter argues that active bodies engaged in everyday sports in the parks also emerge as the critical site for the bodily inscription of global standards of physical activity. Driven by Western fitness regimes, individuals tend to understand themselves as entrepreneurial selves that can bring to life this imagination of the body ideal even while being engaged in various fitness and leisure activities aimed at being ‘healthy’.

With the onset of globalization in developing economies, policymakers express serious concerns about the role of the informal economy, a concern also mirrored in the United Nations (UN) sustainable development goals (SDGs). Numerous attempts have been made to analyse the general equilibrium consequences of globalization in terms of foreign capital inflow on the informal sector in a developing economy. These studies examined the impact of foreign capital inflow through the channels of resource reallocation across sectors and adjustment in the factor and commodity prices. Nevertheless, the efficacy of these channels is contingent upon the assumption of perfectly competitive product markets that is pertinent in the majority of the studies. This chapter attempts to incorporate imperfect competition in the informal economy in a Heckscher–Ohlin-type multi-factor, multi-sector general equilibrium setup. We assume the existence of imperfection in both a homogeneous good-producing industry and a product-differentiating industry and examine how foreign capital inflow in the presence of imperfect competition affects the informal workers, industrial and firm output, product diversity, national income, and welfare. We also analyse how the consequences of foreign capital inflow on the informal economy can vary with the degree of product market imperfection. It is obtained that varying degrees of product market imperfection in the informal economy have only quantitative (magnitude) effects; however, qualitative (directional) effects remain unchanged.

This paper aims to map key strategic concerns that Commonwealth Caribbean States will face in combating economic crimes and strengthening financial integrity in the post-pandemic era.

Horizon scanning was used to conduct a qualitative policy analysis of key regulatory developments in international anti-money laundering and combating the financing of terrorism (AML/CFTP) and tax governance, from the perspective of Commonwealth Caribbean countries.

This paper finds that the COVID-19 pandemic might widen several fault lines, along the Global North/South axis, in international AML/CFTP and tax regulatory governance. These include the “sustainable development” gap in AML/CFTP norm-making; making the Financial Action Task Force fit-for-purpose; renewed campaigns against “harmful tax competition”; and international commitment to scaling up technical assistance to combat economic crimes in developing countries. It questions the sustainability of the prevailing “levelling the playing field” regulatory approach to AML/CFTP and tax matters and whether serious consideration ought not to be given to mainstreaming “differential treatment” in international AML/CFTP and tax standards, for resource-strapped Caribbean countries.

To the best of the author’s knowledge, this paper is the first attempt to assess the strategic policy risks and challenges that will arise from balancing economic recovery and fighting economic crimes by small and vulnerable Commonwealth Caribbean States in the post-pandemic era.

The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.”

Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016).

While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%).

The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices.

The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence.

To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India.

Governments around the world instituted guidelines for calculating energy efficiency of vehicles not only by models, but by the whole universe of new vehicles registered. This paper compiles Multi-criteria decision-making (MCDM) studies related to automotive industry. We applied a Systematic Literature Review on MCDM studies published until 2015 to identify patterns on MCDM applications to design vehicles more fuel efficient in order to achieve full compliance with energy efficiency guidelines (e.g., Inovar-Auto). From 339 papers, 45 papers have been identified as describing some MCDM technique and correlation to automotive industry. We classified the most common MCDM technique and application in the automotive industry. Integrated approaches were more usual than individual ones. Application of fuzzy methods to tackle uncertainties in the data was also observed. Despite the maturity in the use of MCDM in several areas of knowledge, and intensive use in the automotive industry, none of them are directly linked to car design for energy efficiency. Analytic Hierarchy Process was identified as the common technique applied in the automotive industry.