Suvankar Ghosh

Complex compound option models – Can practitioners truly operationalize them?

While there is practitioner interest in real options (RO), there are significant difficulties in practitioner use of complicated RO models, such as compound options pricing models (OPMs) of multistage investments. Drawing upon theories of learning and knowledge, we propose a general framework whereby practitioners can successfully operationalize complex OPMs. From an epistemological perspective, most academic articles on OPMs are essentially propositional knowledge representations that attest to model veracity, and they require a deep background in the analytics of options pricing to comprehend the model which many practitioners lack. A key element of our framework is that the propositional knowledge representation of complex OPMs must be accompanied by a layer of abstraction translating the propositional into procedural knowledge for using the model. Secondly, while this layer of abstraction can be embodied in a software tool for using the OPM, this must be transparently done in order to build trust in the software tool. Thirdly, in the tradition of constructivist learning, using the tool must be illustrated in the context of some contemporary business problem. Finally, a continuous engagement loop must be established which makes routine the application of the RO methodology using this tool. We demonstrate these steps in the context of Geske compound option models for multistage investments. We also show how to apply our tool to the major business problem of enterprise integration. This article therefore provides academics with prescription on fostering practitioner embrace of complex OPMs and practitioners with a tool for operationalizing n-fold compound option models.

A Real Options Model for Generalized Meta-Staged Projects---Valuing the Migration to SOA

This paper develops an innovative real options RO model for valuing multistage information technology IT projects that can be viewed as comprising meta stages. In RO literature, multistage investment programs have been treated as either interproject or intraproject programs, with intraproject programs being evaluated using n-fold Geske compound options and interproject programs valued using the so-called “subsidy-to-exercise price” logic. Our innovative RO model integrates the Geske compound option model with the subsidy-to-exercise price approach to value sequential investment programs that are neither purely interproject nor purely intraproject in nature but are composed of meta-stages. A meta-stage as a whole can be considered an interproject stage resulting in cash flows, but internally it consists of several intraproject stages that do not result in cash flows. We show that a key problem in IT, which is migrating to a Service-Oriented Architecture SOA for integrating a firms many disparate applications, systems, data, and business processes, is best viewed as an investment program comprising meta-stages. Examining SOA migration from an RO lens is particularly apt at this time not only because of the importance of SOA but also because doubts have surfaced about the value of SOA. We illustrate our RO model by applying it to the simulated case of a firm migrating to SOA. We also develop a software tool based on the MathematicaTM computational platform so that practitioners can easily apply our innovative options pricing model to determine the true value of SOA in their business contexts.

An empirical method for assessing the research relevance gap

There has been much debate on the relevance to firms of the academic research produced by business schools. However, what has not received as much attention is how the relevance of the research to businesses should be measured in a systematic and empirical way. We develop a systematic method to test for the relevance of academic research to businesses. Our method models as a vector autoregressive process the interests of the academic and practitioner communities in some new topic, as expressed by the number of articles published in the academic and the practitioner literature on that topic per calendar quarter, and then studies Granger causality between the academic and practitioner interest processes. This method can be used by academics to empirically demonstrate the impact of their intellectual contributions on practitioners and thence on the business world. We employ our approach to two relatively new and important topics, Real Options and Economic Value Added.

Power-Dependence and Reseller Influence on SMEs' Continued Use of Online Direct Sales Channels: An Empirical Study

A dual-channel model with a physical sales channel and an online direct sales channel (ODSC) frequently causes channel conflicts. Small- and medium-sized enterprises (SMEs) using such a model may be forced to suspend ODSC to ease conflicts and maintain traditional resellers. From a channel conflict perspective, this study investigates a few critical factors underlying SMEs intention to continue with an existing ODSC. We develop a research model by integrating power-dependence theory and the technology acceptance model. Then we construct and administer a survey to a sample of US SMEs currently using the dual-channel model. Partial least squares regression is employed to analyze the data and evaluate the impact of four key factors on SMEs continuance of ODSCs: perceived business value of ODSCs, perceived ease of continuance with ODSCs, reseller dependence, and reseller forceful actions. Findings of the study contribute to the understanding of supplier-reseller relationships and continued use of information technologies among SMEs.

An RBV and real options-based soft OR heuristic for solution strategising

This paper develops a heuristic for applying the abstract and complex theories of real options and the resource-based view (RBV) to provide managerial prescription on promising technology solutions to business problems. We draw upon the tradition of soft operational research (OR) with its reliance on graphical techniques to develop a decision-making matrix called the CUReO Grid for identifying preferred solutions under different conditions of decision context uncertainty and the capability of the firm to exploit the technologies under consideration. Our grid is obtained by a two-stage mapping of the real options available in a technology solution, such as options for altering scale and scope or for strategic growth, to a two-dimensional space defined by contextual uncertainty and firm capability. We illustrate our general methodology by applying it to two very different but major contemporary problems: enterprise integration in information technology and lean production in operations management.

An adoption decision model of emerging capital budgeting methodologies

Two emerging methodologies for capital budgeting are Real Options (RO) and Economic Value Added (EVA). Embracing EVA or RO is not a small undertaking for a firm. Given the extent of the change involved, we recommend that firm adopt these new approaches sequentially. We develop a Methodology Adoption Decision Model (MADM) for determining which methodology a firm should adopt first. The MADM focuses on the theoretical soundness and the practical applicability of the methodology. We conclude that EVA is more likely to be embraced by the industry than RO. We therefore recommend that management first focus on EVA adoption.

A real options model of phased migration to cellular manufacturing

The literature is replete with models that examine various aspects of cellular manufacturing (CM), such as optimisation of cell layouts. However, many firms may realise zero to marginal returns from CM. Given this uncertainty, the manager should first determine the value of CM to the firm before deploying it. Although traditional valuation models employing discounted cash flow analysis allow for uncertainty, they treat future investments as fixed when computing the investment’s present value. The real options (RO) logic of valuation allows the manager to exercise the option to invest in or abandon a project based on expected outcomes. Future investments are thus options. This paper presents an RO model for CM migration that addresses whether a firm should migrate to CM; and it prescribes the sequence of cell deployment, which has not been addressed in the literature. Our model is also much more transparent and accessible to practitioners, with an accompanying software tool for prospective users. Finally, we use simulation extensively to discover the drivers of the optimal cell deployment sequence. Our results show that there is a complex interplay between net present value, speed of cellularisation, inter-cell learning and volatility in terms of their influence on the cell sequence.