Analysing customers' reviews and ratings for online food deliveries: A text mining approach

The purpose of this study was to explore the relationship between online reviews and ratings through text mining and empirical techniques. An Indian food delivery portal ( Zomato.com ) was used, where 50 restaurants on Presence Across Nation (PAN) basis were selected through stratified random sampling. A total of 2530 reviews were collected, scrutinized, and analysed. Using the NVivo software for qualitative analysis, seven themes were identified from collected reviews, out of which, the ‘delivery’ theme was explored further for identifying sub-themes. Linear regression modelling was used to identify the variables affecting delivery ratings and sentiment analysis was also performed on the identified sub-themes. Regression results revealed that hygiene and pricing (delivery subthemes) demonstrated lower delivery ratings. These variables can be established as indicators for restaurants and related online food delivery services to build their business model around them. Similarly, negative sentiments were observed in pricing and hygiene sub-themes. Restaurants and online food services can enhance hygiene levels of their food delivery process in order to receive higher delivery ratings. Similarly, pricing of food items can be modified such that customers are not deterred from ordering the items—food and ordering service do not become cost-prohibitive. This study devised a standardized methodology for analysing vast amounts of online user-generated content (UGC). Findings from this study can be extrapolated to other sectors and service industries such as, tourism, cleaning, transportation, hospitals and engineering especially during the pandemic.     

The synergistic effect of insurance and banking sector activities on economic growth in Africa

It is widely understood that the insurance and banking sectors of every economy perform some functions in driving economic growth. What is not yet well documented is whether their roles are complimentary or substitutive. With the aid of the dynamic panel generalized method of moments (GMM) estimation technique, this paper evaluates the synergistic effect of both sectors on economic growth in a panel of 10 African countries that are responsible for most of the activities in the continent’s financial sector. The insurance-banking-growth nexus was also examined through bootstrap panel causality tests. The results show that the life insurance market and the banking sector, as well as the non-life insurance market and the banking sector, are complimentary. We find that, overall, the relationship between the insurance and banking sectors in Africa is a complimentary one and that their synergistic impact on economic growth is positive. The feedback hypothesis was also confirmed in the relationship between the insurance sector and economic growth and between the banking sector and economic growth.     

Structural breaks and GARCH models of stock return volatility: The case of South Africa

This paper investigates the empirical relevance of structural breaks in forecasting stock return volatility using both in-sample and out-of-sample tests applied to daily returns of the Johannesburg Stock Exchange (JSE) All Share Index from 07/02/1995 to 08/25/2010. We find evidence of structural breaks in the unconditional variance of the stock returns series over the period, with high levels of persistence and variability in the parameter estimates of the GARCH(1,1) model across the sub-samples defined by the structural breaks. This indicates that structural breaks are empirically relevant to stock return volatility in South Africa. However, based on the out-of-sample forecasting exercise, we find that even though there structural breaks in the volatility, there are no statistical gains from using competing models that explicitly accounts for structural breaks, relative to a GARCH(1,1) model with expanding window. This could be because of the fact that the two identified structural breaks occurred in our out-of-sample, and recursive estimation of the GARCH(1,1) model is perhaps sufficient to account for the effect of the breaks on the parameter estimates. Finally, we highlight that, given the point of the breaks, perhaps what seems more important in South Africa, is accounting for leverage effects, especially in terms of long-horizon forecasting of stock return volatility.     

An intelligent and secure system for predicting and preventing Zika virus outbreak using Fog computing

Zika virus is a mosquito-borne disease that spreads very quickly in different parts of the world. In this article, we proposed a system to prevent and control the spread of Zika virus disease using integration of Fog computing, cloud computing, mobile phones and the Internet of things (IoT)-based sensor devices. Fog computing is used as an intermediary layer between the cloud and end users to reduce the latency time and extra communication cost that is usually found high in cloud-based systems. A fuzzy k-nearest neighbour is used to diagnose the possibly infected users, and Google map web service is used to provide the geographic positioning system (GPS)-based risk assessment to prevent the outbreak. It is used to represent each Zika virus (ZikaV)-infected user, mosquito-dense sites and breeding sites on the Google map that help the government healthcare authorities to control such risk-prone areas effectively and efficiently. The proposed system is deployed on Amazon EC2 cloud to evaluate its performance and accuracy using data set for 2 million users. Our system provides high accuracy of 94.5% for initial diagnosis of different users according to their symptoms and appropriate GPS-based risk assessment.     

The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US

This article provides out-of-sample forecasts of linear and nonlinear models of US and four Census subregions’ housing prices. The forecasts include the traditional point forecasts, but also include interval and density forecasts, of the housing price distributions. The nonlinear smooth-transition autoregressive model outperforms the linear autoregressive model in point forecasts at longer horizons, but the linear autoregressive and nonlinear smooth-transition autoregressive models perform equally at short horizons. In addition, we generally do not find major differences in performance for the interval and density forecasts between the linear and nonlinear models. Finally, in a dynamic 25-step ex-ante and interval forecasting design, we, once again, do not find major differences between the linear and nonlinear models. In sum, we conclude that when forecasting regional housing prices in the United States, generally the additional costs associated with nonlinear forecasts outweigh the benefits for forecasts only a few months into the future.     

Time-varying linkages between tourism receipts and economic growth in South Africa

The causal link between tourism receipts and GDP has recently become a major focus in the tourism economics literature. Results obtained in recent studies about the causal link appear to be sensitive with respect to the countries analysed, sample period and methodology employed. Considering the sensitivity of the causal link, we use rolling window and time-varying coefficient estimation methods to analyse the parameter stability and Granger causality based on a vector error correction model (VECM). When applied to South Africa for the period 1960–2011, the findings are as follows: results from the full-sample VECM indicate that there is no Granger causality between tourism receipts and GDP, while the findings from the time-varying coefficients model based on the state-space representation show that tourism receipts have positive-predictive content for GDP for the entire period, with the exception of the period between 1985 and 1990. Full-sample time-varying causality tests show bidirectional strong causality between tourism receipts and GDP.     

Nominal vs real adjustment in demand for money functions

Recently, Fair (1987) has addressd an interesting question in the areas of demand for money, namely, whether the adjustment of actual to desired demand for money is in nominal or real terms. His conclusion, based on time-seies analysis for twenty-seven countries, including a few developing countries, was that the evidence was overwhelmingly in favour of the nominal hypothesis.

The aim of this paper is to address the same issue for eleven Asian countries. In the process, a reformulation of the test procedure used by Fair is suggested. In addition, the question is also examined within the framework of the error correction model. The models are also tested for structural stability.

The scheme of the paper is as follows. The models and the tests are specified in Section I. The data and the estimation issues are dealt with in Section II. Section III presents the results. The paper is concluded with a brief summary of the major findings.     

“Opt out” or kept out? The effect of stigma,structure, selection,and sector on the labor force participation of married women in India

Using the 61st and 68th rounds of National Sample Survey data, we investigate the role of stigma, the economy’s structure, potential selection bias, and sectoral differences in explaining the low labor force participation (LFP) of middle and secondary educated women in India relative to low‐ and high‐educated women (the “U‐shape”). Estimating LFP regressions on a sample of prime‐aged married women, we show that, controlling for background characteristics, if a woman’s husband works in a white‐collar job in a region with a high share of blue‐collar jobs, she is less likely to participate in the labor market (stigma). We also find a positive effect of an increase in an index of white‐collar job growth (structure) on married women’s LFP. These effects are present in both rural and urban sectors but are strongest in the rural sector. However, middle and secondary educated women are still found to have substantially lower LFP than low‐ and graduate‐educated women in both sectors. Indeed, over time, the U‐shape persists in the rural sector and deepens in the urban sector. Because unobservables are quite large in the urban sector, we use the method of instrumental variables and find an increasing return at middle levels but stagnation at higher levels.     

Oil shocks and volatility jumps

Review of Quantitative Finance and Accounting - In this paper, we analyse the role of oil price shocks, derived from expectations of consumers, economists, financial market, and policymakers, in...     

Do Countries Matter More in Determining the Relationship Between Employee Welfare and Financial Performance?

Extant literature suggests that employee‐friendly practices influence corporate decision‐making. Using a bargaining framework, we examine the role of country‐level determinants in influencing employee‐friendly practices and their impact on value creation. Utilizing a comprehensive sample of 25,483 firm‐year observations from 56 countries, we find that firms domiciled in countries with weak employment protection and regulations can potentially benefit by voluntarily undertaking employee‐friendly practices. Also, in countries with flexible labor market conditions, firm value is enhanced by undertaking employee‐friendly practices. Furthermore, the importance of employee‐friendly practices in value creation is observed strongly in countries that provide better infrastructure, productivity, and incentives.