Somnath Mukhopadhyay

Improving Revenue Management Decision Making for Airlines by Evaluating Analyst‐Adjusted Passenger Demand Forecasts*

To maximize revenue, airline revenue management analysts (RMAs) attempt to protect the right number of seats for late-booking, high-revenue-generating passengers from low-valued leisure passengers. Simulation results in the past showed that a major airline can generate approximately

2013 Special Issue: Methods for pattern selection, class-specific feature selection and classification for automated learning

500 million per year through efficient RM operations. Accurate passenger demand forecasts are required, because reduced forecast error significantly improves revenue. RMAs often adjust the system forecasts to improve revenue opportunity. Analysis of system forecast performance and analyst adjustment is complex, because one must account for all unseen demands throughout the life of a flight. This article proposes a method to account for unseen demand and evaluate forecast performance (adjusted or unadjusted) through a forecast monitoring system. Initial results from one major airlines origin-destination market data justify the value of RMA forecasting adjustments.

Measuring internet-Commerce success: What factors are important?

This paper presents methods for training pattern (prototype) selection, class-specific feature selection and classification for automated learning. For training pattern selection, we propose a method of sampling that extracts a small number of representative training patterns (prototypes) from the dataset. The idea is to extract a set of prototype training patterns that represents each class region in a classification problem. In class-specific feature selection, we try to find a separate feature set for each class such that it is the best one to separate that class from the other classes. We then build a separate classifier for that class based on its own feature set. The paper also presents a new hypersphere classification algorithm. Hypersphere nets are similar to radial basis function (RBF) nets and belong to the group of kernel function nets. Polynomial time complexity of the methods is proven. Polynomial time complexity of learning algorithms is important to the field of neural networks. Computational results are provided for a number of well-known datasets. None of the parameters of the algorithm were fine tuned for any of the problems solved and this supports the idea of automation of learning methods. Automation of learning is crucial to wider deployment of learning technologies.

A bi-national examination of gender and IT adoption

ABSTRACT Determining the attributes that influence the success of e-commerce is difficult due to a limited conceptual basis necessary for success measures. Keeney (1999) proposed twenty-five objectives separated into two sets-fundamental objectives (the important goals of customers) and means objectives (important goals to be achieved by e-businesses)–for successful e-transactions. Based on Keeneys research, Torkzadeh and Dhillon (2002) developed two instruments that together measure the factors that influence e-commerce success. This research uses these instruments to empirically investigate the Internet shopping behavior of college students (undergraduate and graduate). The research determines the adequacy of multivariate linear relationships between the variables of the two instruments through canonical correlation and redundancy analyses. The research formalizes and tests a series of research hypotheses on Internet shopping behaviors. The article also summarizes the research results with important conclusions and future research directions.

Predicting global Internet growth using augmented diffusion, fuzzy regression and neural network models

Innovation diffusion theory indicates that cultural and economic factors, affect the adoption and diffusion of information technology. This research examines demographic factors for two populations on either side of an international border, to compare IT penetration. A border region was chosen to ensure that differences in technology availability, shipping costs and other factors were minimised. The research found that women had fewer computers at home than men. Further, women born in the USA and living in the USA had fewer computers than either women born in Mexico and living in the USA or women born in Mexico and living in Mexico.

The impact of organisational strategy, culture, people and technology management on organisational practice and performance: an empirical analysis

Quantitative models explaining and forecasting the growth of new technology like the Internet in global business operation appear infrequently in the literature. This paper introduces two artificial intelligence (AI) models such as the neural network and fuzzy regression along with an augmented diffusion model to study and predict the Internet growth in several OECD nations. First, a linear version of an augmented diffusion model is designed. An augmented diffusion model is constructed by including an economic indicator, gross domestic product per capita, into the model. In the next step, two soft AI models are calibrated from the augmented diffusion model. Performance measures of predictions from these models on new samples show that these soft models provide improved forecast accuracy over the augmented diffusion model. The results confirm the major contribution of this research in predicting global Internet growth.

Forecasting global Internet growth using fuzzy regression, genetic algorithm and neural network

Many believe that better people management, technology management, organisational culture, and strategies lead to better organisational practices and performance. However, there is no reliable evidence to support this assertion. This paper employs structural equation modelling (SEM) to analyse data from a nationwide survey of US manufacturing firms. Findings provide strong support for the above belief.

Modelling mobile technology growth using diffusion models and Neural Networks

This paper introduces several soft models such as the genetic algorithm, neural network and fuzzy regression to study and predict the Internet growth in several OECD nations. First a linear version of an augmented diffusion model is designed. The augmented diffusion model is designed by including the impact of an economic indicator, GDP per capita in to the model. In the next stage the soft models are built, using the augmented diffusion model as the base model. Performance and forecasting measures from these soft models show that these soft models provide improvements over the augmented diffusion model. We also discuss how the information from the models can be reused and integrated.

Does Social Communicability Mediate the Role of Trust in Mobile Phone Adoption? An Individual Level Multi-nation Exploratory Study

It is important for market planners and managers of Multi-National Enterprises (MNEs) to forecast global adoption of information technology for efficient market planning. This study builds two pure diffusion models, two popular time-series forecasting models, and one simple Neural Network (NN) model to predict mobile technology growth in 30 Organisation of European Council for Development (OECD) countries, the European Economic and Monetary Union (EMU) of the European Union (EU), and four non-OECD emerging nations. We compare the performances of all models on new samples. The results show that the NN model is superior to all other models.

Lumpy demand forecasting using neural networks

This paper investigates the role of Social Communicability Index (SCI) in the relationship between trust and Mobile Phone Adoption. Using individual-level secondary data from various nations from a reputable data base and controls such as age, gender and education level, we ran regressions and PLS and found that SCI of individuals does moderate the role of trust in mobile phone adoption for most nations. We conclude with future research direction.