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849 items in total found

Journal Articles | 2017

Imperatives and challenges in using e-government to combat corruption: A systematic review of literature and a holistic model

Shailendra Palvia, Ambuj Anand, Priya Seetharaman, and Sanjay Verma

Twenty-third Americas Conference on Information Systems

Corruption, both bureaucratic and political, exists in various forms. Causes and effects of corruption have been documented in various academic and practitioner forums. Developing countries are plagued by rampant corruption caused by several economic, cultural, social and regulatory factors and are struggling to make changes to control and combat corruption. e-Government and e-Participation systems can substantially reduce corruption. Through a comprehensive literature review of over 100 published papers, we analyze the different theoretical models, empirical data and conclusions relating to e-government and its role in combating corruption. We decoct and synthesize the review to evolve four dominant themes relating to the association of e-government with corruption and propose a holistic model of the same. We also examine the challenges associated with each of the themes. We believe this model can be validated by researchers in different contexts while such a holistic understanding can help practitioners view potential solutions differently.

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Journal Articles | 2017

Online education worldwide: Current status and emerging trends

Anil Kumar, Poonam Kumar, Shailendra C. Jain Palvia, and Sanjay Verma

Journal of Information Technology Case and Application Research

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Journal Articles | 2017

Automobile dependence and physical inactivity: Insights from the California Household Travel Survey

Saikat Chakraborty and Eun Jin Shin

Journal of Transport and Health

Background

Auto-dependence has been linked to the physical inactivity epidemic across U.S. cities, resulting in unprecedented increases in incidences of obesity, cardiovascular diseases, depression, etc. The search for strategies to pull an overwhelming majority of auto-dependents out of their sedentary lifestyles by encouraging them to use transit, walk and bike continues to challenge planners and policy-makers.

Methods

We use the 2012–13 California Household Travel Survey data for analyzing the auto-dependence and physical inactivity connection. We select a sample of employed individuals with access to car in urban California, and classify them as discretionary transit riders (N=390), active auto-dependents (N=1287), or sedentary auto-dependents (N=8754) based on their self-reported travel mode use and time spent in physical activity over a 24-h period. We investigate factors that are associated with significantly high physical activity among some auto-dependents relative to the sedentary majority. We also revisit the transit-physical activity connection, and explore conditions that make transit use unfeasible for some active auto-dependents.

Results

Discretionary transit use is associated with higher physical activity. However, there is large variation in physical activity within auto-dependents; significantly higher physical activity is associated with factors such as higher income, flexible work schedule, shorter work hours, and mixed land use. Kids, inflexibility of work schedule, low residential density, lack of pedestrian and bicycling friendly street design, and long distance to transit stops prohibit otherwise active auto-dependents from choosing transit. Employment sector influences both physical activity and choice of transit.

Conclusion

To get sedentary auto-dependents out of endemic physical inactivity, our research indicates the need for targeting lower-incomes, incentivizing employers to provide flexible work hours, and to continue dense, mixed-use developments that make active travel feasible. In addition, to get active auto-dependents to use transit, transit managers must focus on retaining immigrant riders and non-Hispanic Asians, and attracting people with children.

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Journal Articles | 2017

Quantifying invariant features of within-group inequality in consumption across groups.

Anindya S. Chakrabarti, Arnab Chatterjee, Tushar Nandi, Asim Ghosh, and Anirban Chakraborti

Journal of Economics interaction and Coordination

We study unit-level expenditure on consumption across multiple countries and multiple years, in order to extract invariant features of consumption distribution. We show that the bulk of it is lognormally distributed, followed by a power law tail at the limit. The distributions coincide with each other under normalization by mean expenditure and log scaling even though the data is sampled across multiple dimension including, e.g. time, social structure and locations. This phenomenon indicates that the dispersions in consumption expenditure across various social and economic groups are significantly similar subject to suitable scaling and normalization. Further, the results provide a measurement of the core distributional features. Other descriptive factors including those of sociological, demographic and political nature, add further layers of variation on the this core distribution. We present a stochastic multiplicative model to quantitatively characterize the invariance and the distributional features.

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Journal Articles | 2017

How to answer some tricky interview questions?

Asha Kaul

HBR Ascend

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Journal Articles | 2017

Do celebrities have it all? Context collapse and the networked publics

Asha Kaul and Vidhi Chaudhri

Journal of Human Values

With the advent of social media and increase in networked publics, context collapse has emerged as a critical topic in the discussion of imagined audiences and blurring of the private and the public. The meshing of social contexts portends problematic issues as messages inadvertently reach unimagined audiences causing shame and leading to loss of ‘face’. In this article, we specifically study the impact of context collapse on some celebrities ‘who had it all’ yet, lost ‘it some’ to the world of networked public. The article examines celebrities sharing identity information across multiple contexts and explores situations of lost fame when ‘face’ is threatened, usage falters and breaks some of the well-established norms of interactivity. It concludes that lack of prudence in separating social contexts, loss of ‘face’ and social approval can dampen online celebrity presence. It proposes the use of ‘polysemy’ to simultaneously appeal to audiences from different contexts.

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Journal Articles | 2017

Family deviance, self-control, deviant lifestyles, and youth violent victimization: A latent indirect effects analysis

Margit Wiesner and Kathan Shukla

Victims & Offenders

Research increasingly explores more complex relations of low self-control and context factors, such as structural constraints that limit behavioral lifestyle options, with violent victimization. The authors extend extant research by examining indirect effects of low self-control and family deviance on violent victimization via deviant lifestyles. The hypothesized full indirect effects model is tested for 233 African American and Hispanic 11th-grade students using latent variable analysis. Results offer strong support for the full indirect effects hypothesis. Results generally support the utility of an integrative framework that includes structural constraints arising from the family setting.

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Journal Articles | 2017

Through the looking Glass: Role of construal level on description-intensive reviews

Swagato Chatterjee and Aruna Divya T

Advances in Consumer Research

Focus on consumer engagement has led service providers to explore contextual factors influencing consumers’ satisfaction. In this paper, we draw insights from Construal Level Theory to identify the conditions when own vs. others’ experiences along with Process vs. Outcome attributes of services become more important in overall service evaluation

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Journal Articles | 2017

Distribution of Traffic Accident Times in India - Some Insights using Circular Data Analysis

Arnab Kumar Laha, Pravida Raja A.C., and Dilip Kumar Ghosh

International Journal of Business Analytics and Intelligence

Traffic accidents are a major hazard for travellers on Indian roads. These are caused by a variety of reasons including the bad condition of roads, traffic density, lack of proper training of drivers, slack in enforcement of traffic rules, poor road lighting etc. It is further known that certain times of the day are more prone to traffic accidents than others. In this paper we investigate the distribution of traffic accident times using the data published annually by the National Crime Records Bureau (NCRB) over the period 2001-2014 using the tools of circular data analysis. It is seen that the observed distribution of the traffic accident times in most years is bimodal. Thus, several modelling strategies for bimodal distributions are tried which include fitting of mixture of von-Mises distributions and mixture of Kato-Jones distribution. It is seen from this analysis that the distribution of the traffic accident times are changing over the years. Notably, the proportion of accidents happening in late night has reduced over the years while the same has increased for late evening hours. Some more insights obtained from this analysis are also discussed.

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Journal Articles | 2017

A novel sandwich algorithm for empirical Bayes analysis of rank data

Arnab Kumar Laha, Somak Dutta, and Vivekananda Roy

Statistics and its interface

Rank data arises frequently in marketing, finance, organizational behavior, and psychology. Most analysis of rank data reported in the literature assumes the presence of one or more variables (sometimes latent) based on whose values the items are ranked. In this paper we analyze rank data using a purely probabilistic model where the observed ranks are assumed to be perturbed versions of the true rank and each perturbation has a specific probability of occurring. We consider the general case when covariate information is present and has an impact on the rankings. An empirical Bayes approach is taken for estimating the model parameters. The Gibbs sampler is shown to converge very slowly to the target posterior distribution and we show that some of the widely used empirical convergence diagnostic tools may fail to detect this lack of convergence. We propose a novel, fast mixing sandwich algorithm for exploring the posterior distribution. An EM algorithm based on Markov chain Monte Carlo (MCMC) sampling is developed for estimating prior hyperparameters. A real life rank data set is analyzed using the methods developed in the paper. The results obtained indicate the usefulness of these methods in analyzing rank data with covariate information.

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