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Waiting is a ubiquitous constituent of many purchase situations. This research investigates the emotional discomfort experienced by customers visiting retail stores in India throughout the checkout process, focusing on three distinct phases: before joining the queue, while waiting, and during billing. The study examines the circumstances and underlying factors that lead to emotional discomfort among customers while they wait at the checkout counter. A cross-sectional survey involving 385 participants was undertaken to evaluate perceptions related to the waiting experience. Structural Equation Modeling (SEM) was utilized to validate the conceptual model. The study uncovers diverse contributors to emotional discomfort during queueing, such as extended wait times, physical constraints, and perceived time prolongation, highlighting the multifaceted nature of this experience. Notably, the absence of a significant link between emotional discomfort and overall satisfaction implies that, while emotional discomfort is a factor, other elements such as pricing, promotions, and product offerings may exert a more significant influence on customers’ comprehensive satisfaction assessments. The research makes a significant contribution by shedding light on the challenges associated with prolonged waiting times at checkout counters in multi-brand food and grocery retailers. The results provide valuable insights into the emotional experiences of customers during waits and their impact on overall shopping satisfaction.

Waiting is a ubiquitous constituent of many purchase situations. This research investigates the emotional discomfort experienced by customers visiting retail stores in India throughout the checkout process, focusing on three distinct phases: before joining the queue, while waiting, and during billing. The study examines the circumstances and underlying factors that lead to emotional discomfort among customers while they wait at the checkout counter. A cross-sectional survey involving 385 participants was undertaken to evaluate perceptions related to the waiting experience. Structural Equation Modeling (SEM) was utilized to validate the conceptual model. The study uncovers diverse contributors to emotional discomfort during queueing, such as extended wait times, physical constraints, and perceived time prolongation, highlighting the multifaceted nature of this experience. Notably, the absence of a significant link between emotional discomfort and overall satisfaction implies that, while emotional discomfort is a factor, other elements such as pricing, promotions, and product offerings may exert a more significant influence on customers' comprehensive satisfaction assessments. The research makes a significant contribution by shedding light on the challenges associated with prolonged waiting times at checkout counters in multi-brand food and grocery retailers. The results provide valuable insights into the emotional experiences of customers during waits and their impact on overall shopping satisfaction. [ABSTRACT FROM AUTHOR]

One of the prominent types of calendar anomalies includes holiday effects, where stocks show abnormally higher mean returns on the days prior to holidays in comparison to other trading days. The current study investigates the existence of holiday effects in the stock exchanges of the Gulf Co-operation Council, namely, Kuwait, Bahrain, Qatar, Oman, Saudi Arabia, and the United Arab Emirates for the period between January 2009 and December 2020. The national holidays that are considered for the study are New Year's Day, Mawlid al-Nabi (Prophet birthday), Eid-Al-Isra Wal Miraj, Eid-Al-Fitr, National Day, Hegire Day (Islamic New Year), and Christmas Day. The study employs descriptive statistics and the non-parametric Mann–Whitney U test. The findings of the study disclosed the significant pre-holiday mean returns for ADSMI, BHSEASI, DFMGI, MSM30, TASI and FTDKUW, whereas significant post-holiday mean returns were found only in MSM30 and TASI. The study provided evidence for the presence of a calendar anomaly like holiday effects in the major indices of the Gulf Co-operation Council and proved the market was not in an efficient form during the study period. [ABSTRACT FROM AUTHOR]

This study aims at developing econometric models to manage the price risk of Dry and Wet Cocoa beans with the help of ARIMA (Autoregressive Integrated Moving Average) and VAR (Vector Auto Regressive). The monthly price of Cocoa beans is collected for the period starting from April 2009 to March 2020 from the office of CAMPCO Limited, Mangalore, and the ICE Cocoa futures price from the website of investing.com. The augmented dickey fuller test is used to test the stationarity of the series. The ACF and PACF correlograms are used to identify the tentative ARIMA model. Akaike information criterion (AIC) and Schwarz criterion (SBIC), Sigma square, and adjusted R2 are used to decide on the optional AR and MA terms for the models. Durbin–Watson statistics and correlograms of the residuals are used to decide on the model's goodness of fit. Identified optimal models were ARIMA (1, 1, 0) for the Dry Cocoa beans price series and ARIMA (1, 1, 2) for the Wet Cocoa beans price series. The multivariate VAR (1) model found that the US and London Cocoa futures prices traded on the ICE platform will influence the price of Dry Cocoa in India. This study will be helpful to forecast the price of Cocoa beans to manage the price risk, precisely for Cocoa traders, Chocolate manufacturers, Cocoa growers, and the government for planning and decision-making purposes. [ABSTRACT FROM AUTHOR]

The trading of natural rubber derivatives in the Indian commodity exchanges was banned several times in the past. Hence, in India, the derivatives on natural rubber are not traded actively and regularly. We have examined the possibility of a forecast model and a cross hedge tool for the natural rubber price by using crude oil futures in India. Results of the Johansen cointegration test proved that there is no cointegration equation in the model; hence, there is no scope to develop long-run models or error correction models. We have developed a vector autoregressive [VAR(2)] model to forecast the rubber price, and we examined the possibility of a cross hedge for natural rubber further by using the Pearson correlation coefficient and Granger causality test. We have extended our research to a structural VAR analysis to examine the effect of crude futures and exchange rate shocks on the natural rubber price. Our results showed that there is a short-term relationship between the crude oil futures price, the exchange rates of the US dollar to the Indian rupee, the Malaysian ringgit to the Indian rupee and the Thai baht to the Indian rupee; and the natural rubber price in India. The effort of policymakers to cause the Indian rupee to appreciate against the Thai baht and Malaysian ringgit may increase the natural rubber price in India. Natural rubber traders, growers and consumers can use crude futures to hedge the price risk. The Indian Rubber Board can suggest the VAR(2) model to predict the short-run price for natural rubber. [ABSTRACT FROM AUTHOR]

India is the major producer of Areca nut in the world. Volatile demand and price are the major challenges for the Areca nut growers in India. The use of time series models to manage the price risk has become the interest of academicians today. This paper deals with developing an appropriate model to predict the prices of a new variety of Areca nut in Karnataka using monthly price data for the period January 2009 to December 2018. Box Jenkins ARIMA methodology is used to develop the model. Along with ARIMA estimates, log-likelihood, Akaike's information criterion (AIC) and Bayesian (BIC) information criterion statistics are also estimated to decide on the appropriate model. ACF and PACF correlograms for residuals of ARIMA are used to do the diagnostic check of the selected ARIMA model. Appropriate model to forecast the new variety Areca nut price is ARIMA (3, 1, 3). [ABSTRACT FROM AUTHOR]

The remarkable growth of the Indian retail landscape over the last decade is reflected in the proliferation of supermarkets, departmental stores and hypermarkets in India. Evolving consumption patterns, raising living standards has sparked a huge demand in the food and grocery retailing. Impulse buying is a time-tested tactic by which retailers grab customer's attention and boost average purchase value. Prior research has deliberated extensively on impulse buying in the store and its determinants. However, little effort has been made to examine the impulse buying behaviour, particularly at the retail checkout. To bridge this gap, we conducted an empirical study in the leading food and grocery modern retail stores in selected Tier I and Tier II cities in the state of Karnataka, India. The data was collected from 385 respondents using a structured questionnaire. The responses were analysed using confirmatory factor analysis and multiple regression. Our study shows that impulse buying at the store checkout area is minimal and sporadic for most of the product categories at the checkout. Impulse buying at the checkout is instigated by factors such as store environment, credit card availability, momentary mood, in-store promotion, offers and discounts and large merchandise. The study has important implications for retail stores by emphasising on the choice of merchandise offered for sale at the checkout area. Further, the investigation reveals that Indian shoppers are health-conscious and cautious about their purchase at the checkout rather than being impulsive. [ABSTRACT FROM AUTHOR]

The main of the study is to comprehend how the mean variance efficient frontier method may be used in conjunction with Markowitz portfolio theory to produce an optimal portfolio. The study uses daily observations 8 pharma companies closing price namely Auropharma, Granules, Glaxo, Lauruslabs, Pfizer, Sanofi and Torntpharma. Further, Nifty pharma index is considered as benchmark index to check the performance of the chosen companies. The study chosen the reference period from 2020 to 2023 and required data has been extracted from the National Stock Exchange (NSE). This research is based on implementing a stochastic method for efficient portfolio optimisation employing a blended Monte Carlo and Markowitz model. In order to forecast the price of these indices in the future and to determine the likelihood of profit or loss while investing in a portfolio of stocks representing the aforementioned indices, the study also uses Monte Carlo simulation. The study involves two algorithms, namely the deterministic optimisation algorithm, which uses Markowitz Portfolio Theory, and the probabilistic optimisation algorithm, which uses Monte Carlo simulation. The study employed correlation matrix to find the exist relationship between the chosen companies and benchmark index. Also, expected return and volatility has been identified with the help of standard deviation using Python. The study found that the NIFTY Pharma index offers a higher return of 14.35. In addition to this, NIFTY Pharma portfolio's volatility is considerably higher. The study concludes that the NIFTY pharma portfolio is more suitable for those investors who have an appetite for risk. [ABSTRACT FROM AUTHOR]

Maximising financial performance while maintaining adequate liquidity is a crucial and ongoing challenge for bank management, particularly in emerging markets. This study focuses on the relationship between capital structure and financial performance in Jordanian banks, with the mediating role of liquidity risk. Using panel data from 13 central Jordanian banks over the 2015–2022 period, we employ structural equation modelling (SEM) to analyse how capital structure ratios (equity-to-asset, debt-to-loan, and deposit-to-asset) influence financial performance metrics (return on assets and net income-to-expenditure ratio). Our findings reveal a significant positive association between capital structure and financial performance. However, liquidity risk fully mediates this effect. Capital structure primarily impacts performance by influencing a bank's liquidity risk profile. Furthermore, the strength of this mediating effect is noteworthy—capital structure exhibits a statistically more robust association with liquidity risk than its direct impact on performance. This highlights the crucial role of managing liquidity risk within the complex dynamics of bank operations. This research makes a significant contribution to the existing literature by demonstrating the positive impact of capital structure on performance using the underlying mechanism through which this effect occurs. The insights of this research provide several implications for practice in the context of banking industries. [ABSTRACT FROM AUTHOR]

A variety of factors have an influential impact on how an enterprise develops. This study examined factors that operate as market barriers to entrepreneurship in the United Arab Emirates. These factors are fear of failure, risk aversion, lack of social networking, lack of resources, political instability, and economic instability. A survey-based approach framework was used. This survey recruited 150 participants, including UAE students, business owners, and budding entrepreneurs. Multiple-item survey questionnaires and secondary data were used in the data analysis process to identify and evaluate the barriers preventing people from starting new businesses and becoming entrepreneurs in the United Arab Emirates. According to the research results, fear of failure, risk aversion, and lack of resources in the United Arab Emirates make it difficult for business owners to launch brand new business ventures. [ABSTRACT FROM AUTHOR]