E-commerce sellers' ratings: Is user feedback adequate?

The literature on the theory of public procurement points out two well-known informational problems arising out of information asymmetry: (i) adverse selection and (ii) moral hazard. To reduce these issues and foster credibility and trust in the procurement process while maintaining quality and efficiency in public procurement, e-procurement platforms have turned to reputation or rating systems. Therefore, the research and design of such rating systems are crucial. In this study, we discuss the theoretical underpinnings of procurement and employ the information-theoretic, regression analysis, artificial neural network and principal component analysis (PCA) approaches to estimate the weights of the variables entering the rating system. Using real data from Government e-Marketplace, a business-to-business public e-commerce portal, we empirically determine the weights of the rating variables derived from the transaction-level and user feedback data for sellers. The weights obtained from the PCA are the most applicable compared with the other three methods. We compare the old rating system with the newly proposed design using the Wilcoxon signed-rank test. This results in a statistically significant difference between the two ratings. The canonical correlation and Wilks' trial reveal that the ratings derived from transaction-level data and user feedback are uncorrelated to a great extent. Hence, considering only transaction-level data or user feedback is unlikely to divulge sellers' intrinsic worth. E-commerce platforms can use this approach to quickly implement methods to obtain rating scores on a real-time basis for sellers on online platforms.     

Estimation of population ratio in cluster sampling over successive sampling in the presence of non-response

Quality & Quantity - In this paper, we have discussed the problem of estimating the population ratio in cluster sampling over two occasion successive sampling in the presence of non-response....     

A new theoretical framework of shopping motives and channel preference behaviour in the digital era

The unprecedented dissemination of digital technology has changed people's psychology including their shopping behaviour in the last two decades. Smartphone led digital applications and advancements have disrupted consumers' shopping processes, purchase decisions, and priorities as well as increased their exposure, aspirations and expectations inevitably. Therefore, it is imperative to examine the relevance of various elements of shopping motives holistically. Therefore, the present study aims to develop a new theoretical framework based on significant elements of shopping motives for physical products in the digital technology era. For this purpose, an exploratory study, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modelling (SEM) were used. EFA generated five latent factors by grouping 18 independent variables. CFA validated latent factor construct and measured model fit. SEM visualized the path analysis and portrayed the pattern of relations between latent factors into a single factor structural model (consumer shopping motives framework). Results show that consumers' shopping priorities are changing as 3 conventional variables (‘role-playing’, ‘status and authority’, and ‘pleasure of bargaining’) became obsolete and 3 new (‘anywhere and any-time shopping’, ‘safe and secure digital transaction’, and ‘unbiased reviews and ratings”) came into existence prominently. The format and perspective of ‘fun, entertainment and recreation’ ‘social interaction and communication’, and ‘exposure to new and latest trends' have been changing. Consumers are becoming more technology-dependent in their shopping processes and purchase decisions. The validation of the framework on channel preference behaviour revealed that consumers largely prefer offline channels for the fulfilment of risk-free and social sub-motives, and online channels for convenience sub-motive. Therefore, broad structural change and clarity, specification of priorities, a shift in the format and perspective of few elements of shopping motives, intrinsic passion for the use of digital technology and web service in the shopping journey, and simplification of antecedents for growing popularity of multi-channel shopping paradigm are the key novelty of this study.     

Impact of product market competition on real activity manipulation: Moderating role of managerial ability

In this study, we examine whether managerial ability moderates the association between product market competition and real earnings management. Prior literature largely supports the disciplinary effect of competition, suggesting that competition reduces real activity manipulation. We argue that this association is different depending on the level of managerial ability. Based on the reputation hypothesis, we argue that able managers negatively moderate the association between competition and real activity manipulation. Using a sample of US listed companies from 1997 to 2016, we find evidence to support our moderating assertion. A battery of robustness tests validates our original findings.     

Determinants and consequences of student satisfaction in Australian Universities: Evidence from QILT Surveys

We examine the determinants and consequences of student satisfaction, measured by satisfaction scores reported in the QILT surveys from 2012 to 2017. We find that university-level profitability determines overall student satisfaction, where a positive relationship exists between student satisfaction and university performance. This association is more pronounced for Group of Eight (Go8) universities and those with higher academic expenditure. These findings have important implications for higher education providers as the Australian Government is contemplating the use of QILT student satisfaction in allocating public funding for higher education.     

What drives customers toward proximity payment services? An integrated theory of planned behavior perspective

Proximity mobile payment (PMP) services facilitate mobile payments between payer and payee who are at same location thorough a proximity technology such as QR codes, Bluetooth, and near-field communication. It has potential to drastically change consumers' payment methods in developing countries where traditional finance infrastructure is lacking. However, there are various challenges to adopting PMP in such countries. This study aims to explore users' opinions on and motivations for using PMP by proposing an extended theory of planned behavior (TPB) model. It also seeks to resolve inconsistencies in PMP adoption studies regarding the role of user's trust in PMP services. A survey instrument was used to collect data from PMP users in a developing country. The data were analyzed using structural equation modeling. This study highlights trust as a mitigator of the negative effect induced by the perceived risk of using PMP services. It also validates that the TPB, coupled with other contextual factors, explains the adoption of PMP services in developing countries.     

On the Good and Bad of Natural Resource,Corruption, and Economic Growth Nexus

Environmental and Resource Economics - The empirical finding that countries endowed with vast reserves of natural resources are expected to experience slower economic growth – the resource...     

Adoption of electronic banking services in India: an extension of UTAUT2 model

Indian banks have paid sufficient attention to recent innovations in banking services delivery such as e-banking, mobile banking, mobile payment, e-wallet, and e-money services, still the acceptance of these services among consumers is sluggish. Therefore, the present study aims to identify the intention of consumers to adopt various e-banking services. The study adopted the UTAUT2 model (Unified Theory of Acceptance and Use of Technology) and extended it with constructs such as consumer innovativeness, perceived risk, and security information availability. The extended research model was tested using a questionnaire-based response collected from 721 consumers. CB-SEM (Covariance-based structural equation modeling) was used to examine the hypotheses. An empirical examination of the model helped explain the impact of the UTAUT2 model's constructs in predicting adoption intention toward e-banking services. The study also revealed the importance and impact of newly incorporated variables in explaining consumers' adoption intention toward e-banking services. The study has provided some useful insight into the factors influencing consumers' intention to adopt e-banking services to shed new light.

     

DETERMINANTS OF DEMAND FOR FIJI'S EXPORTS: AN EMPIRICAL INVESTIGATION

Fiji is no exceptin to the rule that exports are an important source of growth and development. In this light, it is important to know the determinants of exports. However, there is no empirical study on Fiji's export demand. This paper uses the modern econometric techniques—in particular, the autoregressive distributed lag approach to cointegration—to investigate whether the standard export demand variables, viz., trading partner income, export price, and competitor price, have a long‐run cointegration relationship with Fiji's real exports for the period 1970 to 1999. In addition, the long‐run results are also estimated by using the dynamic ordinary least squares and the fully modified ordinary least squares. The empirical results indicate the existence of a cointegration relationship among the variables. The long‐run foreign income, own‐price, and cross‐price elasticities are found to be 0.7 to 0.8, −1.3 to −1.5, and 2.1 to 2.2, respectively.     

Forecasting Commodity Market Returns Volatility: A Hybrid Ensemble Learning GARCH-LSTM based Approach

This study investigates the advantage of combining the forecasting abilities of multiple generalized autoregressive conditional heteroscedasticity (GARCH)-type models, such as the standard GARCH (GARCH), exponential GARCH (eGARCH), and threshold GARCH (tGARCH) models with advanced deep learning methods to predict the volatility of five important metals (nickel, copper, tin, lead, and gold) in the Indian commodity market. This paper proposes integrating the forecasts of one to three GARCH-type models into an ensemble learning-based hybrid long short-term memory (LSTM) model to forecast commodity price volatility. We further evaluate the forecasting performance of these models for standalone LSTM and GARCH-type models using the root mean squared error, mean absolute error, and mean fundamental percentage error. The results highlight that combining the information from the forecasts of multiple GARCH types into a hybrid LSTM model leads to superior volatility forecasting capability. The SET-LSTM, which represents the model that combines forecasts of the GARCH, eGARCH, and tGARCH into the LSTM hybrid, has shown the best overall results for all metals, barring a few exceptions. Moreover, the equivalence of forecasting accuracy is tested using the Diebold–Mariano and Wilcoxon signed-rank tests.