R P Sundarraj

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.

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.

Purpose: While the adoption of health-analytics (HA) is expanding, not every healthcare organization understands the factors impacting its readiness for HA. An assessment of HA-readiness helps guide organizational strategy and the realization of business value. Past research on HA has not included a comprehensive set of readiness-factors and assessment methods. This study’s objective is to design artifacts to assess the HA-readiness of hospitals. Design/methodology/approach: The information-systems (IS) theory and methodology entail the iterative Elaborated Action Design Research (EADR)method, combined with cross-sectional field studies involving 14 healthcare organizations and 27 participants. The researchers determine factors and leverage multi-criteria decision-making techniques to assess HA-readiness. Findings: The artifacts emerging from this research include: (1) a map of readiness factors, (2) multi-criteria decision-making techniques that assess the readiness levels on the factors, the varying levels of factor-importance and the inter-factor relationships and (3) an instantiated system. The in-situ evaluation shows how these artifacts can provide insights and strategic direction to an organization through collective knowledge from stakeholders. Originality/value: This study finds new factors influencing HA-readiness, validates the well-known and details their industry-specific nuances. The methods used in this research yield a well-rounded HA readiness-assessment (HARA) approach and offer practical insights to hospitals.

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.

Health analytics (HA) technology enables and enhances fact-based decision-making capabilities of hospitals in the clinical, operational, and administrative domains. It is, however, the case that HA success presupposes a hospital's readiness to adopt, absorb, and put the technology to effective use. Currently, there is a paucity of research that focuses on how hospital executives can assess their organization's readiness for HA. The principal objective of our study is to fill this gap. We design HA readiness-assessment artifacts guided by cross-sectional case studies to inform the design and the elaborated action design research approach that extends past artifacts through iterative collaboration with practitioners and industry experts. This article details the design process and artifacts and highlights implications for nascent theory and generalizable knowledge.

In India, private hospitals are at the cusp of adopting health-analytics (HA) technology to manage their organizational performance through data-driven decision-making. Past studies have analyzed the applications and benefits of HA. Our study builds on this descriptive base to investigate the patterns of HA adoption and the institutional factors which impact adoption. We conducted a cross-sectional field study that involved ten Indian private healthcare organizations and four health-ecosystem partners and analyzed the case study data using an institutional theory lens. Our cases reveal that the breadth and depth of HA adoption varies and falls into three distinct patterns: far-sighted, conservative, and niche. We assess how these patterns are influenced by the two key dimensions of institutional environments (material-resource environment and institutional environment). We highlight distinctive factors (management support for building organizational trust on data-driven decisions and IT-Medical practitioner collaboration) that exert important contextual influences on HA adoption. Our study identifies areas of commonalty for HA adoption across national healthcare settings as well as contextual aspects representative of the burgeoning Indian healthcare field.

The cloud computing spot instance is one offering that vendors are leveraging to provide differentiated service to an expanding pay-per-use computing market. Spot instances have cost advantages, albeit at a trade-off of interruptions that can occur when the user's bid price falls below the spot price. The interruptions are often exacerbated since customers often require resources in bundles. For these reasons, customers might have to wait for a long time before their jobs are completed. In this paper, we propose a behavioral-economic model in the form of time-preference-based bids, wherein users are willing to use and bid for services at other times if the vendor cannot provide the resources at the preferred time. Given such bids, we consider the problem of provisioning for such service requests. We develop a time-preference-based optimization model. Since the optimization model is NP-Hard, we develop rule-based genetic algorithms. We have obtained very encouraging results with respect to standard commercial solver as a benchmark. In turn, our results provide evidence for the viability of our approach for online service-provisioning problems.

Cloud-computing facilitates consumers to source online storage-services on a pay-as-you-go basis. The sourcing of storage-as-a-service (STaaS) requires a decision maker to contend with several elements such as pricing structure, application characteristics and quality-of-service requirements, in addition to tactical considerations such as risk propensity and supplier diversification. This makes STaaS sourcing a complex task. Discussion on STaaS sourcing models addressing these aspects and their utility to decision-makers is, however, scant. In this paper, we propose a two-stage holistic approach based on Data Envelopment Analysis (DEA) and Goal programming (GP). We derive insights provided by these models from 10800 runs using market-based data. We also use an experiment to assess our hypothesis concerning the impact of task complexity on decision-quality. Both these experiments have implications for different business scenarios in the STaaS market, and for decision-making. We discuss these managerial implications, and present a prototype Decision Support System (DSS) to aid STaaS selection.

Purpose – This study aims to investigate the decisions related to business aspects of cloud computing and discuss the research density, models/techniques used and identify opportunities for future work. Design/methodology/approach – In this paper, 155 research articles shortlisted through a systematic review were analyzed and a classification framework was developed. Using this framework, the research density is discussed and a detailed review of four widely researched decision themes is provided. Findings – It was found that current research on business aspects is spread across 23 decision themes. The distribution, however, is skewed with 50 per cent pertaining to just four themes, namely, pricing, markets, sourcing and adoption. Simulation appears to be the preferred modeling approach. Decision themes in consumer behavior, sustainability, auditing and culture offer opportunities for future research. Research limitations/implications – The classification framework organizes extant research on applied models and allows researchers to identify potential avenues for application, improvement and development of models to support business decisions. The review is limited to academic articles and does not include industry reports. Practical implications – Practitioners can readily understand various perspectives relevant to a decision theme such as pricing or sourcing, seek and use associated models such as simulation, optimization and game theory to support their decision-making. Originality/value – Most of the extant review paper deal with cloud computing technology. This study is the first systematic review on the models applied to business aspects of cloud computing. This study provides a classification framework and explicitly lists associated decision themes, models/ techniques and opportunities.