Purpose
This study aims to provide insight into consumer behavior regarding the use of food delivery apps when making purchases. To investigate the primary elements affecting users' intentions to use meal delivery applications, this study suggests an extension to the technology acceptance model through some contextual variable such as “various food choices (VFC),” “trust (TRR),” “perception of COVID-19-related risks (PCR)” and “convenience (CONV)” during the pandemic.
Design/methodology/approach
A cross-sectional data of 407 was collected in the Indian context. This research adopts the covariance-based structural modeling approach to test the hypotheses along with hierarchical regression to predict the efficiency of constructs.
Findings
Considering the outcomes, “perceived usefulness (PU)” was positively influenced by “perceived ease of use (PEOU),” “VFC” and “CONV.” In addition, the attitude (ATT) was positively impacted by “PU,” “TRR” and “PEOU.” Nevertheless, “PCR” negatively influenced ATT. In additional, this research illustrates the positive impact of ATT and PU on behavioral intention to use.
Originality/value
By confirming the technology acceptance model's capacity for explanation in relation to food delivery apps, this study adds to the body of knowledge. The primary focus of this study is on determining the direct impact of the identified determinants on the adoption of food delivery applications within the context of a pandemic situation in developing countries.
Post-demonetization, digital payments in transactions became substantial; nonetheless, these services are still relatively new to Indian customers and are still in their infancy. Investigating the variables that influence customers' intentions to utilise digital payment services in India, particularly among millennials, is necessary to promote the development of computer enabled devices as an alternative payment method. Present study is to do a pilot study on millennial perception towards Mobile Banking/Digital wallets/UPI. The study makes use of Cronbach’s Alpha to see if the data is internally consistent. Millennials will make up 35% of the global workforce just this year. The results based on Cronbach’s Alpha show that the data is internally consistent after dropping each variable for challenges faced and customer satisfaction.
Predicting currency has always been open to doubt because in financial as well as in managerial decisions making process it plays a crucial role and it is not easy to forecast foreign rates with higher accuracy than a naive random walk model. The main goal of this paper is to use the Arima model to forecast the yearly exchange rate, here we use real foreign exchange data to check the suitable level of the Arima model for forecasting and it also shows how suitable the Arima model is to estimate foreign exchange. There has been considerable improvement in profitability of MNC which conducts substantial currency transfer in business courses and forecasts exchange rate accurately. The time series Arima model is applied to forecast the exchange return of SND to INR. To better understand how the Arima model applies within the period 1st February 2011 - 1st February 2021. In this report monthly or daily exchange returns are used for variable inputs. This model is based on a few observations on the Arima model to help predict and solve financial forecasting problems for the best and worst possible situations which results in demonstrating the predictive strength and potential but is still a problematic task.
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