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.

This book contains contemporary discussions on technology, business models, and the adoption of digital manufacturing systems. The book’s initial chapters cover technological details underpinning the digital manufacturing systems, for example, cyber-physical systems and digital twins. Next, the book discusses how organizations modify their business models using concepts such as servitization and platforms to leverage digital manufacturing. The latter chapters focus on how a country’s unique economic and infrastructural context influences digital manufacturing adoption in terms of technology and business models and frameworks to evaluate readiness for digital manufacturing. With perspectives from different continents, the book appeals to academic researchers and industry alike.

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.

Efficient public-procurement systems are critical for ensuring Access to Medicines (ATM) and enabling universal healthcare delivery. This is especially true of India where public healthcare caters to the underprivileged population who have limited access to medicines. However, essential medicine shortage in the Indian public-healthcare system is significant and is exacerbated by inefficiencies in the procurement system. Healthcare policy makers have to constantly contend with delays and non-fulfillment of medicine orders leading to shortages. Pharmaceutical companies supplying orders argue that the current system is not business-viable or fair. To explore these issues in-depth, we distill insights from structured interviews with a Policy Maker and interactions with the pharmaceutical industry to identify supply side issues which lead to medicine shortages. We build a normative model and utilize public medicine procurement data to study how pharmaceutical supplier response and order fulfillment is impacted by orders from multiple Indian states with different procurement conditions. We then employ standard supply chain theory to propose solutions to mitigate some of these issues. We find that the current system can be significantly improved by increased capacity allocation at suppliers for state orders, staggered ordering at the state level, stricter but gradual implementation of penalties and blacklisting and sourcing from suppliers located closer to the state.

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.

Foresight is the ability to visualize alternate futures and shape the direction of future events. Generally considered as an organizational capability, foresight can be the basis of sustained competitive advantage, and needs to be fostered by enabling employees to acquire, assimilate, and analyze information with a future orientation. However, research on what constitutes foresight at the employee (i.e., individual) level is sparse. This paper fills this gap by using a five-stage mixed-mode process to propose an extended foresight measure. This paper has implications for research on the individual aspect of microfoundations of capability building, organizational measures development, and foresight literature.

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.