About

Sunder Kekre is the Vasantrao Dempo Professor and a Professor of Operations Management at the Tepper School of Business. His role involves teaching and research in the field of operations management, contributing to the academic leadership of the school. His work is aligned with the school's strategic vision to lead at the intersection of business, technology, and analytics, supporting the development of innovative approaches to business education and organizational problem solving.

Research topics

• Political Science
• Management
• Business
• Economics
• Marketing
• Public relations
• Psychology

Selected publications

• Service Providers’ Decision to Use Ethics Committees and Consultation in Complex Services

  Journal of Marketing Research · 2020 · 13 citations

  • Political Science
  • Public relations
  • Business

  Ethics has long been, and continues to be, a central topic among marketing scholars and practitioners. When providing complex services—multiple interactions over time that are predicated on the evolving needs of customers—service providers face ethical dilemmas, which are often resolved by engaging an ethics committee (EC). Despite the prevalence of ECs, research on service providers’ preference to engage with an EC is sparse. This study examines whether the role that health care providers play, as either task manager or relationship manager, makes a difference in their preference for engaging with and utilizing an EC for resolving ethical dilemmas. Results based on 1,440 observations collected from health care service providers show that service providers’ task or relationship management role, as well as prior experience with an ethics consultation, influences their preference both for engaging an EC and for having the EC prescribe a specific outcome to resolve an ethical dilemma. This study extends prior work on conceptual models examining ethical decision-making processes in marketing.

  PublisherDOI

• Headquarter and Subsidiaries: Analyzing a Social Media Platform Within MNCs

  SSRN Electronic Journal · 2019-01-01

  articleOpen access
  PublisherDOI

• Comparative Analysis of Incumbent and Emerging Liquefied Natural Gas Regasification Technologies

  Industry Studies Working Papers (University of Pittsburgh) · 2018-01-01 · 1 citations

  articleOpen access

  Energy plays a fundamental role in both manufacturing and services, and natural gas is quickly becoming a key energy source worldwide. Facilitating this emergence is the expanding network of ocean-going vessels that enable the matching of natural gas supply and demand on a global scale by transporting it in the form of liquified natural gas (LNG) for eventual regasification at its destination. Until very recently only one type of technology has been available for transporting and regasifying LNG: Conventional LNG vessels and land based LNG regasification. It is now possible to transport and regasify LNG onboard special LNG vessels. Companies such as Excelerate Energy and Höegh LNG are currently developing LNG supply chains based on this new technology. Motivated by this recent development we engaged executives at Excelerate Energy to develop and apply to data an integrated analytic framework to compare these incumbent and emerging technologies. Our analysis brings to light basic principles delineating when to deploy each technology and how to configure the emerging technology. Some of our findings challenge conventional wisdom on the role to be played by the emerging technology; others provide answers to open questions faced by companies currently engaged in the commercial deployment of this technology. In addition, our integrated analytic framework has potential relevance for the evaluation of new technologies beyond this specfiic application.

  OA PDFDOI

• Component-based Technology Transfer: Balancing Cost Saving and Imitation Risk

  Figshare · 2018-06-30 · 4 citations

  articleOpen access

  Technology transfer offers global firms an opportunity to reduce the costs involved in serving emerging markets as well as to source from low-cost locations for their home markets. However, it also poses a potential risk of imitation by local competitors who may enter the market(s). We introduce a component-based technology transfer instrument for the global firm to either deter or accommodate the imitator's entry, by recognizing that components can differ in two dimensions: cost-saving potential and imitation risk. By choosing the range of components to transfer, the global firm's decision has an impact not only on the imitator's fixed entry costs, but also on the post-entry com- petition based on variable costs. Hence, the proposed instrument leads to two different types of deterrence strategies: "barrier-erecting strategy" and "market-grabbing strategy" by transferring a lower or higher amount, respectively, of component technology than in the case of no imitator. Which deterrence strategy the global firm should employ, depends on the level of imitation risk of transferring the components. Some other interesting and counter-intuitive results arise. For example, transferring less technology when the emerging market potential increases can be optimal. Considering a sourcing opportunity for a home market, a larger home market potential makes the deterrence strategy more attractive when the imitation risk is low, but less attractive when the risk is high.

  OA PDFDOI

• Commodity Procurement with Demand Forecast and Forward Price Updates

  Figshare · 2018-06-30 · 4 citations

  articleOpen accessSenior author

  Commodities, ranging from natural gas to memory chips, can be procured both by trading on the date in spot markets and in advance in forward markets. Transaction costs, such as brokerage fees, are typically higher in spot markets than in forward markets. Moreover, the forecast of a ¯rm's commodity requirement (demand) for a given future date typically changes in an uncertain fashion over time. Thus, although the dynamics of forward and spot prices are notoriously uncertain, firms that procure commodities face the dilemma of choosing between early and possibly less expensive commitments with residual demand uncertainty and late and possibly more expensive sourcing of the exact amount needed. We investigate this issue by developing and analyzing a model of commodity procurement for a single future date. Our model generalizes models available in the real options and operations management literature, by simultaneously considering correlated demand forecast and forward price updates in a setting characterized by multiple forward transactions and a single spot transaction. We derive the structure of the optimal procurement policy and discuss its computation in cases of practical interest. In a numerical study, based on applying our model to natural gas data, we offer managerial insights on the effects that demand forecast and forward price updates, both in isolation and combined, have on the value of a firm's procurement policy. We also assess the sensitivities of these effects to parameters of interest and the potential managerial relevance of the combined effect. Our model and results have significance beyond the specific application.

  OA PDFDOI

• Valuation of the Real Option to Store Liquefied Natural Gas at a Regasification Terminal

  Research Showcase @ Carnegie Mellon University (Carnegie Mellon University) · 2018-06-30 · 9 citations

  article

  A global liquefied natural gas (LNG) market is quickly emerging, with several significant development projects very recently completed or underway; these projects consist of extraction, liquefaction, shipping, regasification, and storage facilities. Exact valuation of the real option to store LNG at the downstream terminal of an LNG value chain is computationally intractable. Thus, we develop a novel and tractable model for the heuristic valuation of this real option. This model uses a shipping model to represent upstream LNG production and shipping to the downstream regasification facility; a reduced form model of the evolution of the spot price in the wholesale natural gas market where regasified LNG is sold; a stochastic dynamic programming model to determine a policy for inventory control at the storage facility and sale into this market; and a final Monte Carlo simulation step to estimate the value of this policy. The basestock type structure that we prove for our model's LNG inventory release policy is central to make the final simulation step computationally efficient; this makes our model practical. We incorporate real and estimated data to quantify the value of the real option to store LNG at a regasification terminal, the dependence of this value on the level of stochastic variability in the shipping model and the type of natural gas price model used, and the relative value of this option for different parties involved in an LNG value chain. We also develop an upper bound, based on sample path optimization, to assess the effectiveness of our heuristic and find that our method is highly accurate. Our model has the potential to be used to value the real option to store other commodities in storage facilities located downstream of a commodity production or transportation stage, or the real option to store the input used in the production of a commodity.

  PublisherDOI

• An Integrated Framework for the Analysis of New Technology Selection for an Application to the LNG Industry

  2018-01-01 · 1 citations

  article

  A fundamental issue in the management of technology innovation, both in manufacturing and service industries, is the comparative evaluation of emerging and incumbent technologies. This evaluation entails the juxtaposition of multiple aspects including process configuration and operational and financial performance. In this paper we present an integrated analytic framework for technology selection that models the relation between these three critical dimensions. We apply our framework in the context of the liqueed natural gas industry, in which new o shore vessel-based regasification technology has recently been developed as an alternative to conventional onshore terminal-based regasification. We analyze the impact of process configuration and operational and financial performance on technology selection, and identify the conditions under which a specific regasification technology and its configuration is appropriate for adoption. We also investigate how the insights we derive may depend on how one models stochastic variability in the relevant processing times.

  PublisherDOI

• Leveraging Big Data to Balance New Key Performance Indicators in Emergency Physician Management Networks

  Production and Operations Management · 2017-12-16 · 19 citations

  articleCorresponding

  Managing emergency physicians is a complex task and has increasingly intensified with the recent consolidation of many emergency departments (EDs). Large‐scale physician groups are facing challenges in resource deployment and performance evaluation. To objectively evaluate physicians across facilities, we leverage big data from an emergency physician management network and propose data‐driven metrics using a large‐scale database consisting of 84 hospitals, 1,079 physicians, and 10,615,879 patient visits in 14 states over 600,000 clinical shifts from 2010 to 2014. To ensure physicians are fairly evaluated and compensated within diverse facilities, we propose an index system and use clustering to help identify factors which might impact physician performance. The proposed indices benchmark physicians from the perspectives of revenue potential, patient volume, patient complexity, and patient experience by controlling for exogenous factors at the facility level. We empirically show the volume and complexity indices are key elements of the revenue potential index, and use two‐stage least squares regression to relate volume and complexity and uncover their drivers. Revenue potential and patient experience are found to be positively correlated, which suggests productive physicians are often liked by their patients. Through implementing the proposed evaluation system, administrators can better manage and incentivize physicians and provide directions for performance improvement, while controlling for location idiosyncrasies. The proposed framework can also be adapted to non‐medical professional settings such as value chains, where employees often provide services in various profit‐ and cost‐centers.

  PublisherDOI

• Managing Complex-Negative Services: Service Providerss Use of Ethics Committees and Consultation in Healthcare Institutions

  SSRN Electronic Journal · 2016-01-01

  articleOpen access
  PublisherDOI

• The Squeaky Wheel Gets the Grease—An Empirical Analysis of Customer Voice and Firm Intervention on Twitter

  Marketing Science · 2015-05-27 · 248 citations

  articleSenior author

  Firms are increasingly engaging with customers on social media. Despite this heightened interest, guidance for effective engagement is lacking. In this study, we investigate customers’ compliments and complaints and firms’ service interventions on social media. We develop a dynamic choice model that explicitly accounts for the evolutions of both customers’ voicing decisions and their relationships with the firm. Voices are driven by both the customers’ underlying relationships and other factors such as redress seeking. We estimate the model using a unique data set of customer voices and service interventions on Twitter. We find that redress seeking is a major driver of customer complaints, and although service intervention improves relationships, it also encourages more complaints later. Because of this dual effect, firms are likely to underestimate the returns on service intervention if measured using only voices. Furthermore, we find an “error-correction” effect in certain situations, where customers compliment or complain when others voice the opposite opinions. Finally, we characterize the distinct voicing tendencies in different relationship states, and show that uncovering the underlying relationship states enables effective targeting. We are among the first to analyze individual customer level voice dynamics and to evaluate the effects of service intervention on social media.

  PublisherDOI

Frequent coauthors

• Sham Kekre

  Carnegie Mellon University Qatar

  14 shared

• Srikant M. Datar

  12 shared

• Tridas Mukhopadhyay

  Carnegie Mellon University

  11 shared

• Uday S. Karmarkar

  11 shared

• Alan Scheller‐Wolf

  Carnegie Mellon University

  8 shared

• Nicola Secomandi

  Rice University

  8 shared

• Kannan Srinivasan

  Carnegie Mellon University

  8 shared

• Surendra Rajiv

  7 shared