The Relationship Between Consumer-Brand Identification and Brand Extension

The present research aims at examining the role of consumer-brand identification (CBI) in attitude toward brand extension regarding the congruency between the values of consumers and brands. In this way, the benefits of CBI to consumers and brand managers are outlined. This research has been conducted through designing a survey and collecting data through a questionnaire. For data mining and investigating the model, the SEM approach is implemented. According to the findings, high levels of CBI lead to positive attitudes toward the brand extension, and that value congruity positively impacts this relationship. The results outline that CBI impacts fit and tie in separate ways, so that CBI effect on fit is more substantial. Furthermore, according to the results, in comparison to the role of tie, the role of fit is more significant in predicting attitudes toward brand extension. The sample from university students is appropriate for testing theory, but limits generalizing the results of the research. In addition, this research has studied one product category and is limited in this perspective. The findings have remarkable recommendations for implementing brand extension as they emphasize the role of perceived fit. Moreover, by reexamining CBI in a less known market, the research has outlined its positive outcomes for firms. This research has used perceived tie in a brand extension context and, similar to perceived fit, has implemented it as another factor to predict attitude toward brand extension. In addition, this research is unique, as it has investigated CBI in a new context.     

The anticipated and concurring effects of the EMU: exchange rate volatility, institutions and growth

Reduced exchange rate volatility and higher and less heterogeneous quality of institutional rules and macroeconomic policies are two of the main (anticipated and concurring) effects expected from a currency union.In this paper, we measure the magnitude of these two effects for the Eurozone countries looking at real effective exchange rates (REER) and at different indicators of quality of institutional rules and macroeconomic policies (QIRMP). We find that the first effect is much stronger than the second when we compare relative changes for Eurozone countries and the rest of the world in the relevant period.We further evaluate the impact of both effects on economic growth on a larger sample of countries. Our findings show that both have significant impact on levels (more robust) and on rates of growth (weaker) of per capita GDP.     

Dynamic linkages among equity markets in the Middle East and North African countries

The relaxation of security laws and regulations in emerging markets in the Middle East and North Africa (MENA) provides abundant opportunities for foreign investors. These markets exhibit high-expected returns and substantial volatility. In this paper, we investigate the lead/lag relationship between the MENA countries and regions. We find no market causality or spillover from one country to another in the North Africa region. Our results for the Levant region reveal that there are linkages between stock markets in this region. The results for the Gulf Cooperation Council (GCC) region show that there is more interaction and linkage in the GCC region than in the North Africa and Levant regions. An unexpected result is that UAE's stock market leads all the markets in this region. Finally, we investigate linkages among the three regions. We find that GCC influences the other two regions.     

Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models

The study examines the relative ability of various models to forecast daily stock index futures volatility. The forecasting models that are employed range from naïve models to the relatively complex ARCH-class models. It is found that among linear models of stock index futures volatility, the autoregressive model ranks first using the RMSE and MAPE criteria. We also examine three nonlinear models. These models are GARCH-M, EGARCH, and ESTAR. We find that nonlinear GARCH models dominate linear models utilizing the RMSE and the MAPE error statistics and EGARCH appears to be the best model for forecasting stock index futures price volatility.     

Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach

Water Resources Management - Reference evapotranspiration (ET0) is a crucial element for deriving irrigation scheduling of major crops. Thus, precise projection of ET0 is essential for better...     

Converting Daily Rainfall Data to Sub-daily—Introducing the MIMD Method

One of the most important analysis in many hydrological and agricultural studies is to convert the daily rainfall data into sub-daily (hourly) because in many rainfall stations, only the daily rainfall data are available and for a comprehensive rainfall analysis, these data should be converted to sub-daily. Many experimental and analytical methods are available for this conversion but one of the simplest yet accurate ones has been proposed by the Indian Meteorological Department (IMD). Since the IMD method has shown low accuracy in some regions, in this study, the IMD method is modified to a single parameter equation, called Modified Indian Meteorological Department (MIMD) in order to improve the accuracy of the conversion. For this reason, the parameter is calibrated so that the maximum correlation between observed and estimated values is achieved. Five stations in different regions with different climatic conditions were selected so that the daily and sub-daily rainfall data were available in each of them. Then, the parameter of the MIMD method was derived for each station. The results were compared with both observed data and IMD method and it was shown that the mean correlation coefficient of MIMD and IMD methods were 0.9 and 0.73 respectively for 12-h rainfall depth which indicated that the accuracy of the MIMD method in estimation of sub-daily rainfall depths was significantly increased. Moreover, the results showed that the accuracy of the MIMD method decreases as rainfall duration decreases.

     

A Probabilistic Multiperiod Simulation–Optimization Approach for Dynamic Coastal Aquifer Management

Combined simulation–optimization (CSO) schemes are common in the literature to solve different groundwater management problems, and CSO is particularly well-established in the coastal aquifer management literature. However, with a few exceptions, nearly all previous studies have employed the CSO approach to derive static groundwater management plans that remain unchanged during the entire management period, consequently overlooking the possible positive impacts of dynamic strategies. Dynamic strategies involve division of the planning time interval into several subintervals or periods, and adoption of revised decisions during each period based on the most recent knowledge of the groundwater system and its associated uncertainties. Problem structuring and computational challenges seem to be the main factors preventing the widespread implementation of dynamic strategies in groundwater applications. The objective of this study is to address these challenges by introducing a novel probabilistic Multiperiod CSO approach for dynamic groundwater management. This includes reformulation of the groundwater management problem so that it can be adapted to the multiperiod CSO approach, and subsequent employment of polynomial chaos expansion-based stochastic dynamic programming to obtain optimal dynamic strategies. The proposed approach is employed to provide sustainable solutions for a coastal aquifer storage and recovery facility in Oman, considering the effect of natural recharge uncertainty. It is revealed that the proposed dynamic approach results in an improved performance by taking advantage of system variations, allowing for increased groundwater abstraction, injection and hence monetary benefit compared to the commonly used static optimization approach.

     

The effect of interfirm dependence structures on governance mechanisms

Despite recent developments regarding the study of interdependence structures, previous research has rarely investigated the simultaneous effect of both interdependence magnitude and interdependence asymmetry on governance mechanisms among exchange parties. A survey of manufacturing companies has been used to test a theory about the interactive effect of buyer dependence and supplier dependence on inter-firm governance. The analysis of the survey supported the proposition that both buyers and suppliers that are highly dependent on each other (high interdependence magnitude and low interdependence asymmetry) may choose to rely on monitoring and the norm of information sharing. However, when a buyer's dependence is low regardless of the supplier's dependence level, the buyer does not rely on the norm of information sharing (buyer's relative interdependence advantage, buyer's relative interdependence disadvantage, and low interdependence magnitude). On the other hand, when the supplier's dependence on the buyer is low, the buyer relies on monitoring regardless of its dependence level (buyer's relative interdependence disadvantage and low interdependence magnitude). However, this study did not empirically measure performance. Further research should be done on the effect of congruence between the governance mechanism and its antecedents on buying performance.     

Temporal Hydrologic Alterations Coupled with Climate Variability and Drought for Transboundary River Basins

Climate change (CC) and drought episode impacts linked with anthropogenic pressure have become an increasing concern for policy makers and water resources managers. The current research presents a comprehensive methodology but simple approach for predicting the annual streamflow alteration based on drought indices and hydrological alteration indicators. This has been achieved depending on the evaluation of drought severity and CC impacts during the human intervention periods to separate the influence of climatic abnormality and measure the hydrologic deviations as a result of streamflow regulation configurations. As a representative case study, the Lesser Zab River Basin in northern Iraq has been chosen. In order to analyse the natural flow regime, 34 hydrological years of streamflow (1931–1965) prior to the main dam construction were assessed. The Indicators of Hydrologic Alteration (IHA) method has been applied to quantify the hydrological alterations of various flow characteristics. In addition, an easy approach for hydrological drought prediction in relatively small basins grounded on meteorological parameters during the early months of the hydrological year has been presented. The prediction was accomplished by implementing the one-dimensional drought examination and the reconnaissance drought index (RDI) for evaluating the severity of meteorological drought. The proposed methodology is founded on linear regression relations connecting the RDI of 3, 6, and 12 months and the streamflow drought index (SDI). The results are critical for circumstances where an early exploration of meteorological drought is obtainable. Outcomes assist water resources managers, engineers, policy makers and decision-makers responsible for mitigating the effects of CC.     

Effect of Extraordinary Large Floods on at-site Flood Frequency

The peak flow of extraordinary large floods that occur during a period of systematic record is a controversial problem for flood frequency analysis (FFA) using traditional methods. The present study suggests that such floods be treated as historic flood data even though their historical period is unknown. In this paper, the extraordinary large flood peak was first identified using statistical outlier tests and normal probability plots. FFA was then applied with and without the extraordinary large floods. In this step, two goodness-of-fit tests including mean absolute relative deviation and mean squared relative deviation were used to identify the best-fit probability distributions. Next, the generalized extreme value (GEV), three-parameter lognormal (LN3), log-Pearson type III (LP3), and Wakeby (WAK) probability distributions were used to incorporate and adjust the extraordinary large floods with other systematic data. Finally, procedures with and without historical adjustment were compared for the extraordinary large floods in terms of goodness-of-fit and flood return-period quantiles. The results of this comparison indicate that historical adjustment from an operational perspective was more viable than without adjustment procedure. Furthermore, the results without adjustment were unreasonable (subject to over- and under-estimation) and produced physically unrealistic estimates that were not compatible with the study area. The proposed approach substantially improved the probability estimation of rare floods for efficient design of hydraulic structures, risk analysis, and floodplain management.