R P Sundarraj

Investing in cloud computing technology is one of the latest trends in IT. This is a multi criteria investment decision involving many stakeholders and a sequence of coordinated assessment activities that are strategic, qualitative (technical) and quantitative (financial) in nature. This paper integrates an information system (IS) success model with preference elicitation techniques drawn from the multi-criteria decision-making literature. We also show how this decision model can be extended as a vendor negotiation tool. Finally, we describe a prototype Decision Support System (DSS) featuring this model.

Healthcare organizations (HCO) are under pressure to adopt emerging solutions with a view to improving the quality and efficiency of their operations, patient care and clinical decisions. In the literature, while recommendations have been put forth to follow an organizational approach to the adoption and implementation of analytics, there is a paucity of research into what specifically constitutes healthcare-analytics (HA), as well the antecedents that can explain and predict its adoption-decisions. In this paper, we fill this gap, by first proposing a typology for HA and an adoption-decision model that integrates the TOE framework with DeLone and McLean IS Success Model. We use a case study for the initial validation of the typology and the model. Our study reveals that the HCO studied has the same types of the data proposed in our typology, although the usage of the data and current/future adoption of analytics depends on many factors beyond simply technology.

Smartphone applications are emerging as popular media for promoting one's products through in-app advertisements. Today, there are a number of organizations, known as supply-side-platforms (SSP), who aggregate and auction these ad-spaces from different suppliers/publishers. Advertisers (or their intermediaries) place bid for these spaces based on different relevance criteria (e.g., the location and device of the app-user, the app's IAB category etc.), the impression value, clickthrough value, and the conversion value. After the received ads are filtered based on relevance, the SSP is often still faced with a number of options for ad-placement, each having different revenues owing to differences in clickthrough rates etc. Moreover, the SSP has to decide on the ad-placement in real-time. In this paper, we consider the SSP's ad-placement problem in the aforementioned situation. We propose an optimization model to maximize the SSP's revenues. Based on computational experience with this model, we develop a rule-based online algorithm that appears to be viable as a real-time solution.

With the proliferation of new innovative technologies, electronic gadgets like smartphones loaded with sophisticated user friendly apps are getting popularity among large proportion of phone users. As a result, advertisers are using apps as a medium of communication to showcase their products through in-app advertising. These advertisements are managed by ad aggregators through DSPs & by ad-space aggregators through SSPs. We need to allow most profitable ads to be pushed into appropriate apps so that overall SSP's revenue is maximized.Inapp advertising helps both the SSPs as well as DSPs to earn revenue. In this paper, we aim to maximize the revenue of SSP by developing machine learning based rule inferring technique from the output of mathematical model. Our initial computational results show the efficacy of online algorithm over previous research.

In recent times, spot pricing - a dynamic pricing scheme - is becoming increasingly popular for cloud services. This new pricing format, though efficient in terms of cost and resource use, has added to the complexity of decision making for typical cloud computing users. To recommend bidding strategies in spot markets, we use a simulation study to understand the implications that provider-recommended strategies have for cloud users. We use data based on Amazon's Elastic Compute Cloud spot market to provide users with guidelines when considering tradeoffs between cost, wait time, and interruption rates.

The role of anger in negotiation is explored in considerable depth in many papers in the literature. In the electronic negotiation situation, one way to express anger (in addition to plain textual messages) is through the use of emoticons and para-linguistic cues. Cue usage by angry negotiators under different levels of anger is unexplored in negotiation literature. In this paper, we address this gap by conducting a distributive electronic negotiation experiment and studying the usage of cues (statements and para-linguistic cues including emoticons) by angry negotiators while interacting with their counterpart (computer). We report that participants tend use more para-cues, especially emoticons, as their anger intensity increases and that emoticons have the ability to replace other para-cues while composing angry messages. The findings provide promising inputs on design of user interfaces for electronic negotiation systems.

Electronic Procurement Systems (EPS) are being acknowledged by researchers as promising technological enablers for achieving a responsive supply chain, and thereby, for gaining a competitive advantage in today’s global marketplace. A number of empirical studies have focused on the adoption of EPS in different countries. There is, however, a scarcity of work related to EPS adoption in India, even though information technology and the Internet play a significant role in that country. To fill this gap, we first discuss the potential supply chain benefits of EPS, especially as they relate to large multinational companies. Then, we specifically consider the Indian context. We highlight several firms whose innovative logistics operations permit the respective supply chains to function, uniquely blending Indian customs with modern business practices.We report on an empirical survey, as well as three case-studies relating to the importance and impact of EPS adoption in India.

Decision-making under risk or uncertainty is quite common in various environments. According to decision-maker's preferences, there are three types of attitudes toward risk: Risk averse, risk prone or risk neutral. The aim of this work is to perform a cross-cultural analyzes of the risk preferences of subjects who belong to two different cultures. A questionnaire is applied with Brazilians and Indians to capture their preferences in order to observe the attitudes towards risk and check if they act in different way. Using the concept of Certainty Equivalent, it was found they present the same attitude when facing risk situations, being risk averse. At the end of this step, the information serves as an input to consider when negotiating with a counterpart from different cultures.

Today, smartphone-based in-app advertisement forms a substantial portion of the online advertising market. In-app publishers go through ad-space aggregators known as Supply-Side Platforms (SSPs), who, in turn, act as intermediaries for ad-agency aggregators known as demand-side platforms. The SSPs face the twin issue of making ad placement decisions within an order of milliseconds, even though their revenue streams can be optimized only by a careful selection of ads that elicit appropriate user responses regarding impressions, clicks, and conversions. This article considers the SSP's perspective and presents an online algorithm that balances these two issues. Our experimental results indicate that the decision-making time generally ranges between 20 ms and 50 ms and accuracy from 1% to 10%. Further, we conduct statistical analysis comparing the theoretical complexity of the online algorithm with its empirical performance. Empirically, we observe that the time is directly proportional to the number of incoming ads and the number of online rules.