The main objective of this article is to study the perception of the users of cyber cafés and to understand the important factors required to manage the cafés better in a competing environment. This was pursued by interviewing 414 users, in cyber cafés spread over different parts of Delhi. The respondents were chosen in so as to represent both the working and non-working class; those using the cyber cafés in the morning, after-noon, evening and night and belonging to different age groups, sex and education level. The information needed for designing the questionnaire was identified through exploratory research by conducting informal interviews and two focus group discussions of the users. The data was analyzed through the examination of frequency distributions, by conducting a factor analysis and using the chi-square test. Ten factors of perceptions were identified. The study indicated that value added services, state of art technology, effective utilization of time during waiting hours and data production facilities were the most important factors. The article concludes by providing recommendations to cyber café owners.

Strategic alignment of information technology (IT) is required to be included in the firms’ core activity in today’s business environment. The purpose of this study is to understand the impact of ‘IT connectivity, IT infrastructure and IT human resources’ on ‘IT business strategic alignment’ by developing a model, in public and private organizations in India. A questionnaire was used to measure the constructs after its validity and reliability. The findings discovered that firms’ IT strategic alignment was significantly impacted by three IT dimensions, that is, IT connectivity, IT infrastructure and IT human resources (combined as IT capability). The IT investments and expenditures of a firm are aligned with its business objectives and priorities. Furthermore, the significant role of these dimensions in public and private organizations in India was examined.

The study captures the COVID-19 lifecycle in different states of India using predictive analytics. Drawing upon the seminal susceptible–infected–removed (SIR) model of capturing the spread of viral diseases, this study models the spread of COVID-19 in the ten most infected states of India (as on 30 April 2020). Using publicly available state-wise time series data of COVID-19 patients during the period 1–30 April 2020, the study uses the forecasting technique of auto-regressive integrated moving averages (ARIMA) to predict the likely population susceptible to COVID-19 in each state. Thereafter, based on the SIR model, predictive modelling of state-wise COVID-19 data is carried out to determine: (a) the predictive accuracy; (b) the likely number of days it would take for the disease to reach the peak number of infections in a state; (c) the likely number of infections at the peak; and (d) the state-wise end date. The SIR model is implemented by running Python 3.7.4 on Jupyter Notebook and using the package Matplotlib 3.2.1 for visualization. The study offers rich insights for policymakers as well as common citizens.

Purpose The study aims to carry out predictive modeling based on publicly available COVID-19 data for the duration April 01, 2020 to June 20, 2020 pertaining to India and five of its most infected states: Maharashtra, Tamil Nadu, Delhi, Gujarat and Rajasthan. Design/methodology/approach The study leverages the susceptible, infected, recovered and dead (SIRD) epidemiological framework for predictive modeling. The basic reproduction number R0 is derived by an exponential growth method using RStudio package R0. The differential equations reflecting the SIRD model have been solved using Python 3.7.4 on the Jupyter Notebook platform. For visualization, Python Matplotlib 3.2.1 package is used. Findings The study offers insights on peak-date, peak number of COVID-19 infections and end-date pertaining to India and five of its states. Practical implications The results subtly indicate toward the amount of effort required to completely eliminate the infection. It could be leveraged by the political leadership and industry doyens for economic policy planning and execution. Originality/value The emergence of a clear picture about COVID-19 lifecycle is impossible without integrating data science algorithms and epidemiology theoretical framework. This study amalgamates these two disciplines to undertake predictive modeling based on COVID-19 data from India and five of its states. Population-specific granular and objective assessment of key parameters such as reproduction number (R0), susceptible population (S), effective contact rate (ß) and case-fatality rate (s) have been used to generate a visualization of COVID-19 lifecycle pattern for a critically affected population.

In this article, an attempt is made to build an ARIMA model to forecast exports of Indian readymade garments. The monthly data on readymade garments exports for the period April 1991 to December 2000 is used to build the model. The forecasts are obtained for the period January 2001 to December 2001 by using the ARIMA model. The accuracy of ex-post forecast is also tested. The forecasts indicate a slow down in the growth of exports in January to March 2001 as compared to the same months in the year 2000. The article concludes by suggesting a review of government policy towards this industry.