Decision, Operations & Information Technologies

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Prior to January 4, 2009, this unit was named Decision & Information Technologies.

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The Effect of Perceived Attitude Similarity on Performance Ratings
(1983) Feren, Dena Beatrice; Carroll, Stephen J.; Digital Repository at the University of Maryland; University of Maryland (College Park, Md)
This research consists of a laboratory study designed to test the notion that variance in performance ratings can be accounted for by the perception of the rater that the persons/he is evaluating is attitudinally similar or dissimilar to himself or herself. Student subjects were led to believe that a certain manager either agreed or disagreed with them on a number of attitudinal issues. Subjects then viewed a videotaped performance of the manager conducting a performance review with one of his problem subordinates. Subjects were asked to rate his performance using two different rating instruments -- a trait rating scale and a Behavior Observation Scale -- and to indicate personal liking for the manager. Extent of attitude similarity was manipulated on two levels with a control group . That is, some subjects were led to believe that the ratee was attitudinal l y similar to self, others that the ratee was dissimilar to self, and a third group received no information about the ratee's attitudes. The ratee's performance was manipulated on three levels. Some subjects viewed only a high performin1; manager, others viewed a moderate performer, and a third group viewed a lo w performing manager. Three different vignettes were prepared to represent the three levels of performance. Finally, a hard-performance-data condition was included to test the robustness of the attitude similarity effect. Some subjects received hard performance data, in the form of bar graphs, that was consistent with the level of performance portrayed in their videotaped vignette (i.e., those viewing the low performer received hard data indicative of low performance). It was hypothesized that perceived attitude similarity would have its greatest effect when performance was moderate, and when subjects did not receive hard performance data. The results did not support these predictions. The effect of perceived attitude similarity on performance ratings was not significant under any of the experimental conditions. Perceived similarity had a small, but significant effect on attraction; however, level of performance accounted for a far greater proportion of variance in attraction measures than perceived similarity. It was concluded that the rating task in this experiment failed to create the conditions under which perceived similarity would be most likely to exert an influence on ratings. Specifically, the rating task was not sufficiently ambiguous for student raters.
Sensitivity Analysis for Monte Carlo Simulation of Option Pricing
(1995) Fu, Michael C.; Hu, Jian-Qiang
Monte Carlo simulation is one alternative for analyzing options markets when the assumptions of simpler analytical models are violated. We introduce techniques for the sensitivity analysis of option pricing which can be efficiently carried out in the simulation. In particular, using these techniques, a single run of the simulation would often provide not only an estimate of the option value but also estimates of the sensitivities of the option value to various parameters of the model. Both European and American options are considered, starting with simple analytically tractable models to present the idea and proceeding to more complicated examples. We then propose an approach for the pricing of options with early exercise features by incorporating the gradient estimates in an iterative stochastic approximation algorithm. The procedure is illustrated in a simple example estimating the option value of an American call. Numerical results indicate that the additional computational effort required over that required to estimate a European option is relatively small.
THREE ESSAYS ON MORTGAGE BACKED SECURITIES: HEDGING INTEREST RATE AND CREDIT RISKS
(2003-12-05) Chen, Jian; Fu, Michael C.; Decision and Information Technologies
This dissertation includes three essays on hedging the interest rate and credit risks of Mortgage-Backed Securities (MBS). Essay one addresses the problem of how to efficiently estimate interest rate sensitivity parameters of MBS. To do this in Monte Carlo simulation, we derive perturbation analysis (PA) gradient estimators in a general setting. Then we apply the Hull-White interest rate model and a common prepayment model to derive the corresponding specific PA estimators, assuming the shock of interest rate term structure takes the form of a trigonometric polynomial series. Numerical experiments comparing finite difference (FD) estimators with our PA estimators indicate that the PA estimators can provide better accuracy than FD estimators, while using much lower computational cost. Using the estimators, we analyze the impact of term structure shifts on various mortgage products. Based these analysis, we propose a new product to mitigate interest rate risk. Essay two addresses the problem of how to measure interest rate yield curve shift more realistically, and how to use these risk measures to hedge the interest rate risk of MBS. We use a Principal Components Analysis (PCA) approach to analyze historical interest rate data, and acquire the volatility factors we need in Heath-Jarrow-Morton interest rate model simulation. Then we propose a hedging algorithm to hedge MBS, based on PA gradient estimators derived upon these PCA factors. Our results show that the new hedging method can achieve much better hedging efficiency than traditional duration and convexity hedging. Essay three addresses the application a new regression method on credit spread data. Previous research has shown that variables in traditional structural model have limited explanatory power in credit spread regression. We argue that this is partially due to the non-constancy of the credit spread gradients to state variables. We use a Random Coefficient Regression (RCR) model to accommodate this problem. The explanatory power increases dramatically with the new RCR model, without adding new independent variables. This is the first work to address the dependence between credit spread sensitivities and state variables of structural in a systematic way. Also our estimates are consistent with prediction from Merton’s structural model.
Design of Online Auction System with Alternative Currencies
(2004-05-05) Deshpande, Vainateya Suresh; Lucas, Henry; Decision and Information Technologies
The University of Maryland has one of the most popular Basketball programs in the region. About 35,000 students seek 4,000 free student tickets allocated for every home game. An auction-based system provides a procedure to achieve and equitable and fair distribution of a high-demand resource. In an auction-based system, goods being sold end up with the person who values them the most. This is a very desirable scenario for a ticket distribution system that aims at maximizing attendance for home games. People who bid high have high values for the tickets and are more likely to attend a game than someone who receives a ticket through a random draw. The thesis lays out the framework for an auction based system to distribute home game tickets.
EVALUATION OF SETUP ECONOMIES IN CELLULAR MANUFACTURING
(2004-08-04) Kramer, Steven; Assad, Arjang A; Decision and Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This dissertation addresses two research questions relating to the role of setups in discrete parts manufacturing. The first research topic uses a carefully designed simulation study to investigate the role of setup economies in the factory-wide conversion of functional layouts (job shops) to cellular manufacturing. The model-based literature shows a wide dispersion in the relative performance of cellular manufacturing systems as compared to the original job-shop configurations, even when the key performance measure is flow time and the assessment tool used is simulation. Using a standardized framework for comparison, we show how this dispersion can be reduced and consistent results can be obtained as to when the conversion of the job shop is advantageous. The proposed framework standardizes the parameters and operational rules to permit meaningful comparison across different manufacturing environments, while retaining differences in part mix and demand characteristics. We apply this framework to a test bed of six problems extracted from the literature and use the results to assess the effect of two key factors: setup reduction and the overall shop load (demand placed on the available capacity). We also show that the use of transfer batches constitutes an independent improvement lever for reducing flow time across all data sets. Finally, we utilize the same simulation study framework to investigate the benefits of partial transformation, where only a portion of the job shop is converted to cells to work alongside a remainder shop. The second research question examines the role of dispatching rules in the reduction of setups. We use queueing models to investigate the extent of setup reduction analytically. We single out the Alternating Priority (AP) rule since it is designed to minimize the incidence of setups for a two-class system. We investigate the extent of setup reductions by comparing AP with the First-Come-First-Served (FCFS) rule. New results are obtained analytically for the case of zero setup times and extended to the case of non-zero setup time through computational studies.
IMPACT ASSESSMENT OF DYNAMIC SLOT EXCHANGE IN AIR TRAFFIC MANAGEMENT
(2004-12-09) Sankararaman, Ravi; Ball, Michael; Decision and Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Since the inception of Collaborative Decision Making (CDM), the Federal Aviation Administration and the airlines have been striving to improve utilization of critical resources such as arrival slots and reduce flight delays during Ground Delay Programs. Two of the mechanisms that have been implemented for increasing utilization at resource-constrained airports are those of Compression and Slot Credit Substitution (SCS). SCS is a conditional, dynamic means of inter-airline slot exchange while compression can be considered a static means of achieving slot utilization. This thesis will be an attempt to develop theoretical models to understand the performance of compression to slot exchange requests from airlines. This thesis will also address the trends in these slot exchange procedures, the benefits in terms of delay savings realized by the airlines, and avenues for future applications for improving efficiency of the National Airspace System.
Finding the Value of Information About a State Variable in a Markov Decision Process
(2005-01-13T15:04:40Z) Souza, Gilvan
In this paper we present a mixed–integer programming formulation that computes the optimal solution for a certain class of Markov decision processes with finite state and action spaces, where a state is comprised of multiple state variables, and one of the state variables is unobservable to the decision maker. Our approach is a much simpler modeling alternative to the theory of partially observable Markov decision processes (POMDP), where an information and updating structure about the decision variable needs to be defined. We illustrate the approach with an example of a duopoly where one firm’s actions are not immediately observable by the other firm, and present computational results. We believe that this approach can be used in a variety of applications, where the decision maker wants to assess the value of information about an additional decision variable.
The Optimal Pace of Product Updates
(2005-01-13T15:05:47Z) Souza, Gilvan; Druehl, Cheryl; Schmidt, Glen
Some firms (such as Intel and Medtronics) use a time–pacing strategy for new product development, introducing new generations at regular intervals. If the firm adopts a fast pace (introducing frequently) then it prematurely cannibalizes its old generation and incurs high development costs, while if it waits too long, it fails to capitalize on customer willingness–to–pay for more advanced technology. We develop a model to gain insight into which factors drive the pace. We consider the degree to which a new generation stimulates market growth, the rate at which it diffuses (its coefficients of innovation and imitation), the rate of decline in its margin over time, and the cost of new product development. The optimization problem is non–concave; however we are able to solve it numerically for a wide range of parameters because there is a finite number of possible solutions for each case. Somewhat intuitively, we find that a faster pace is associated with a higher market growth rate and faster margin decay. Not so intuitively, we find that relatively minor differences in the new product development cost function can significantly impact the optimal pace. Regarding the Bass coefficients of innovation and imitation, we find that a higher sum of these coefficients leads to a faster pace but with diminishing effects, and that for relatively higher sums the coefficients are effectively substitutes.
Time Value of Commercial Product Returns
(2005-01-13T15:06:27Z) Souza, Gilvan; Guide, V. Daniel; Van Wassenhove, Luk; Blackburn, Joseph
Manufacturers and their distributors must cope with an increased flow of returned products from their customers. The value of commercial product returns, which we define as products returned for any reason within 90 days of sale, now exceeds US $100 billion annually in the US. Although the reverse supply chain of returned products represents a sizeable flow of potentially recoverable assets, only a relatively small fraction of the value is currently extracted by manufacturers; a large proportion of the product value erodes away due to long processing delays. Thus, there are significant opportunities to build competitive advantage from making the appropriate reverse supply chain design choices. In this paper, we present a simple queuing network model that includes the marginal value of time to identify the drivers of reverse supply chain design. We illustrate our approach with specific examples from two companies in different industries and then examine how industry clockspeed generally affects the choice between an efficient and a responsive returns network.
DATA VISUALIZATION OF ASYMMETRIC DATA USING SAMMON MAPPING AND APPLICATIONS OF SELF-ORGANIZING MAPS
(2005-03-17) Li, Haiyan; Golden, Bruce L.; Decision and Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Data visualization can be used to detect hidden structures and patterns in data sets that are found in data mining applications. However, although efficient data visualization algorithms to handle data sets with asymmetric proximities have been proposed, we develop an improved algorithm in this dissertation. In the first part of the proposal, we develop a modified Sammon mapping approach that uses the upper triangular part and the lower triangular part of an asymmetric distance matrix simultaneously. Our proposed approach is applied to two asymmetric data sets: an American college selection data set, and a Canadian college selection data set which contains rank information. When compared to other approaches that are used in practice, our modified approach generates visual maps that have smaller distance errors and provide more reasonable representations of the data sets. In data visualization, self-organizing maps (SOM) have been used to cluster points. In the second part of the proposal, we assess the performance of several software implementations of SOM-based methods. Viscovery SOMine is found to be helpful in determining the number of clusters and recovering the cluster structure of data sets. A genocide and politicide data set is analyzed using Viscovery SOMine, followed by another analysis on the public and private college data sets with the goal to find out schools with best values.
STOCHASTIC OPTIMIZATION: ALGORITHMS AND CONVERGENCE
(2005-03-23) Xiong, Xiaoping; Fu, Michael C.; Decision and Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Stochastic approximation is one of the oldest approaches for solving stochastic optimization problems. In the first part of the dissertation, we study the convergence and asymptotic normality of a generalized form of stochastic approximation algorithm with deterministic perturbation sequences. Both one-simulation and two-simulation methods are considered. Assuming a special structure on the deterministic sequence, we establish sufficient conditions on the noise sequence for a.s. convergence of the algorithm and asymptotic normality. Finally we propose ideas for further research in analysis and design of the deterministic perturbation sequences. In the second part of the dissertation, we consider the application of stochastic optimization problems to American option pricing, a challenging task particularly for high-dimensional underlying securities. For options where there are a finite number of exercise dates, we present a weighted stochastic mesh method that only requires some easy-to-verify assumptions and a method to simulate the behavior of underlying securities. The algorithm provides point estimates and confidence intervals for both price and value-at-risk. The estimators converge to the true values as the computational effort increases. In the third part, we deal with an optimization problem in the field of ranking and selection. We generalize the discussion in the literature to a non-Gaussian correlated distribution setting. We propose a procedure to locate an approximate solution, which can be shown to converge to the true solution asymptotically. The convergence rate is also provided for the Gaussian setting.
Supply Chain Coordination for False Failure Returns
(2005-04-11T13:17:49Z) Souza, Gilvan; Ferguson, Mark; Guide, V. Daniel, Jr.
False failure returns are products that are returned by consumers to retailers with no functional or cosmetic defect. The cost of a false failure return includes the processing actions of testing, refurbishing if necessary, repackaging, the loss in value during the time the product spends in the reverse supply chain (a time that can exceed several months for many firms), and the loss in revenue because the product is sold at a discounted price. This cost is significant, and is incurred primarily by the manufacturer. Reducing false failure returns, however, requires effort primarily by the retailer, for example informing consumers about the exact product that best fits their needs. We address the problem of reducing false failure returns via supply chain coordination methods. Specifically, we propose a target rebate contract that pays the retailer a specific dollar amount per each unit of false failure returns below a target. This target rebate provides an incentive to the retailer to increase her effort, thus decreasing the number of false failures and (potentially) increasing net sales. We show that this contract is Pareto–improving in the majority of cases. Our results also indicate that the profit improvement to both parties, and the supply chain, is substantial.
INTEGRATED PRODUCTION-DISTRIBUTION SCHEDULING IN SUPPLY CHAINS
(2005-05-09) Pundoor, Guruprasad; Chen, Zhi-Long; Decision and Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
We consider scheduling issues in different configurations of supply chains. The primary focus is to integrate production and distribution activities in the supply chain in order to optimize the tradeoff between total cost and service performance. The cost may be based on actual expenses such as the expense incurred during the distribution phase, and service performance can be expressed in terms of time based performance measures such as completion times and tardiness. Our goal is to achieve the following objectives: (i) To propose various integrated production-distribution scheduling models that closely mirror practical supply chain operations in some environments. (ii) To develop computationally effective optimization based solution algorithms to solve these models. (iii) To provide managerial insights into the potential benefits of coordination between production and distribution operations in a supply chain. We analyze four different configurations of supply chains. In the first model, we consider a setup with multiple manufacturing plants owned by the same firm. The manufacturer receives a set of distinct orders from the retailers before a selling season, and needs to determine the order assignment, production schedule, and distribution schedule so as to optimize a certain performance measure of the supply chain. The second model deals with a supply chain consisting of one supplier and one or more customers, where the customers set due dates on the orders they place. The supplier has to come up with an integrated production-distribution schedule that optimizes the tradeoff between maximum tardiness and total distribution cost. In the third model, we study an integrated production and distribution scheduling model in a two-stage supply chain consisting of one or more suppliers, a warehouse, and a customer. The objective is to find jointly a cyclic production schedule at each supplier, a cyclic delivery schedule from each supplier to the warehouse, and a cyclic delivery schedule from the warehouse to the customer so that the customer demand for each product is satisfied fully at minimum total production, inventory and distribution cost. In the fourth model, we consider a system with one supplier and one customer with a set of orders placed at the beginning of the planning horizon. Unlike the earlier models, here each order can have a different size. Since the shipping capacity per batch is finite, we have to solve an integrated production-distribution scheduling and order-packing problem. Our objective is to minimize the number of delivery batches subject to certain service performance measures such as the average lead time or compliance with deadlines for the orders.
Stochastic Gradient Estimation
(2005-07-01T12:31:02Z) Fu, Michael C.
We consider the problem of efficiently estimating gradients from stochastic simulation. Although the primary motivation is their use in simulation optimization, the resulting estimators can also be useful in other ways, e.g., sensitivity analysis. The main approaches described are finite differences (including simultaneous perturbations), perturbation analysis, the likelihood ratio/score function method, and the use of weak derivatives.
Multi-Echelon Models for Repairable Items: A Review
(2005-07-01T12:31:37Z) Diaz, Angel; Fu, Michael C.
We review multi-echelon inventory models for repairable items. Such models have been widely applied to the management of critical spare parts for military equipment for around three decades, but the application to manufacturing and service industries seems to be much less documented. We feel that the appropriate use of models in the management of spare parts for heavily utilized equipment in industry can result in significant cost savings, in particular in those settings where repair facilities are resource constrained. In our review, we provide a strategic framework for making these decisions, place the modeling problem in the broader context of inventory control, and review the prominent models in the literature under a unified setting, highlighting some key relationships. We concentrate on describing those models which we feel are most applicable for practical application, revisiting in detail the Multi-Echelon Technique for Recoverable Item Control (METRIC) model and its variations, and then discussing a variety of more general queueing models. We then discuss the components which we feel must be addressed in the models in order to apply them practically to industrial settings.
A Large Deviations Analysis of Quantile Estimation with Application to Value at Risk
(2005-07-01T12:31:49Z) Jin, Xing; Fu, Michael C.
Quantile estimation has become increasingly important, particularly in the financial industry, where Value-at-Risk has emerged as a standard measurement tool for controlling portfolio risk. In this paper we apply the theory of large deviations to analyze various simulation-based quantile estimators. First, we show that the coverage probability of the standard quantile estimator converges to one exponentially fast with sample size. Then we introduce a new quantile estimator that has a provably faster convergence rate. Furthermore, we show that the coverage probability for this new estimator can be guaranteed to be 100% with sufficiently large, but finite, sample size. Numerical experiments on a VaR example illustrate the potential for dramatic variance reduction.
Note: An Application of the EOQ Model with Nonlinear Holding Cost to Inventory Management of Perishables
(2005-07-19T20:53:40Z) Souza, Gilvan; Ferguson, Mark; Jayaraman, Vaidy
We consider a variation of the economic order quantity (EOQ) model where cumulative holding cost is a nonlinear function of time. This problem has been studied by Weiss (1982), and we here show how it is an approximation of the optimal order quantity for perishable goods, such as milk, and produce, sold in small to medium size grocery stores where there are delivery surcharges due to infrequent ordering, and managers frequently utilize markdowns to stabilize demand as the product’s expiration date nears. We show how the holding cost curve parameters can be estimated via a regression approach from the product’s usual holding cost (storage plus capital costs), lifetime, and markdown policy. We show in a numerical study that the model provides significant improvement in cost vis-à-vis the classic EOQ model, with a median improvement of 40%. This improvement is more significant for higher daily demand rate, lower holding cost, shorter lifetime, and a markdown policy with steeper discounts.
The Value of IT-Enabled Retailer Learning: Can Personalized Product Recommendations (PPRs) Improve Customer Store Loyalty in Electronic Markets?
(2005-08-24) Zhang, Tongxiao; Agarwal, Ritu; Lucas, Hank; Decision and Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Personalization is a strategy that has been widely adopted by online retailers to enhance their customers' shopping experience, with the ultimate goal of building a strong and enduring customer relationship. Personalized product recommendations (PPRs) are product recommendations adapted to individual customers' preferences and taste. So far, very few empirical studies have ever investigated the impact of PPRs from a consumer behavior perspective. Whether PPRs generate any value for consumers and ultimately, retailers, is still an open question. To fill this gap in the literature, in this study, drawing upon the household production function model in the consumer economics literature, I develop a theoretical framework that explains the mechanism through which PPRs influence customer store loyalty in electronic markets. Online shopping can be viewed as a household production process and customer store loyalty is driven by shopping efficiency. Building upon retailer learning, higher quality PPRs can increase consumers' online product brokering efficiency, which in turn increases their repurchase intention. A two-phase lab experiment was conducted among 253 undergraduate students in the business school. The subjects completed a simulated purchase at Amazon.com and the quality of PPRs they received was manipulated. Empirical analyses indicate that higher quality PPRs improve consumers' online product brokering quality, which in turn increases their repurchase intention. Consumers make higher quality purchase decisions and experience more fun during the online product brokering process. A surprising finding is that higher quality PPRs increase consumer online product brokering cost. Consumers spend more time on decision making and have more difficulty reaching a purchase decision. Implications, limitations, and contributions of this study are discussed and areas for future research are suggested.
IT Design for Sustaining Virtual Communities: an Identity-based Approach
(2005-08-31) Ma, Meng; Agarwal, Ritu; Lucas, Henry; Decision and Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
A variety of information technology (IT) artifacts, such as those supporting reputation management and digital archives of past interactions, are commonly deployed to support virtual communities. Despite the ubiquity of these artifacts, research on the impact of various IT-based features on virtual community communication is still limited. Without such research, the mechanisms through which information technologies influence community success are not well understood, limiting the design of community infrastructures that can enhance interaction in the community and minimize dysfunction. This dissertation proposes that identity management is a critical imperative in virtual communities and concerns related to communication of identity serve to shape an individual's interactions and perceptions in the community. Sensitivity to this perspective can help in drawing design guidelines for the IT infrastructure supporting the community. Drawing upon the social psychology literature, I propose an identity-based view to understand how the use of IT-based features in virtual communities can improve community sustainability. Specifically, identity consonance, defined as the perceived fit between a focal person's belief of his or her identity and the recognition and verification of this identity by other community members, is proposed as a core construct that mediates the relationship between the use of community IT artifacts and member satisfaction and knowledge contribution. To test the theoretical model, I surveyed two online communities: Quitnet.com and myIS.com. The former is an online community for people who wish to quit smoking, and the latter is a site for Lexus IS300 sport sedan enthusiasts. The results from surveys support the positive effects of community IT artifacts on identity consonance. The empirical study also finds that a high level of identity consonance is linked to member satisfaction and knowledge contribution. This dissertation offers a fresh perspective on virtual communities and suggests important implications for the design of the supporting IT infrastructure.
Strategic IT Partnerships in Transformational Outsourcing as a Distinctive Source of IT Value: A Social Capital Perspective
(2005-09-07) Ye, Fei; Agarwal, Ritu; Decision and Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Firms increasingly acquire needed information technology (IT) products and services from external sources through the formation of partnerships. In spite of the ubiquity of IT outsourcing practice in today's organizations however, theoretical understanding of IT partnerships in outsourcing is limited. Extant research has largely focused on the economic or strategic aspects of IT outsourcing, using transaction cost economics (TCE) and the resource-based view (RBV) as dominant theoretical frameworks. This dissertation adopts a social perspective to examine the IT outsourcing phenomenon. It focuses on IT partnerships in transformational outsourcing relationships that are interorganizational engagements formed to rapidly and substantially improve performance at the organizational level. By synthesizing the knowledge based view of the firm with the concept of social capital, I attempt to explain how IT outsourcing relationships generate value for organizations. I argue that IT outsourcing partnerships constitute a form of social capital for the firm that chooses to outsource, that facilitates knowledge exchange and transfer. The increased knowledge stock as a result of knowledge exchange and transfer, in turn, forms the foundation for IT value, which is manifested as success in business operations and IT-enabled innovation. To empirically test the theoretical model, I surveyed 151 client firms and 79 outsourcing service providers in China. Results suggest that both social capital and knowledge acquisition are crucial to the success of IT outsourcing. Evidence from the survey responses also indicates that different aspects of social capital play different roles in the process of IT value creation. Specifically, the structural dimension (partner resource endowment) and the cognitive dimension of social capital (shared vision and shared cognition) have a strong impact on knowledge acquisition; whereas the relational dimensions of social capital (social interaction and trust) has strong direct effects on successful outcomes of IT outsourcing. This study presented evidence that helps further our understanding of the IT outsourcing phenomenon through an alternative theoretical lens, and emphasizes the value other than immediate cost-related benefits that organizations may garner through IT outsourcing partnerships.

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