<p> A majority of current leadership programs are failing to deliver a comprehensive approach to leadership development by not providing middle and frontline managers the skills to enhance their potential to develop others. In failing to generate a comprehensive system, animosity towards all types of leadership has been festering for over 40 years as first identified by Greenleaf in 1977. The purpose of the study was to establish a link between the theoretical paradigms of servant leadership and self-leadership using the lens of emotional intelligence to generate an integral leadership development framework. The conceptual framework used Goleman et al.&rsquo;s version of emotional intelligence, Spears&rsquo;s model of servant leadership, and Manz&rsquo;s concepts of self-leadership. The research question examined the interrelationship between the three theoretical paradigms and used the analysis to create a theoretical framework. A paradigm and systematic word search phrase yielded an initial sample of 1356 research articles. Using text scrutinization to achieve saturation, I used 342 articles to evaluate the gap between the three theoretical paradigms. The analysis of the secondary data used Edwards&rsquo;s approach to metatheory-building. The results yielded the beginnings of a new theory of self-perpetuating leadership style called sustainable leadership. Also noted based on the literature a serious absence of ethics, morality, or spirituality in leadership development. This study is important because it uses a holistic framework based on development techniques found in three theoretical leadership paradigms to help aspiring leaders to develop others. The positive social change that may result is an improvement in leadership skills, over time, through a comprehensive approach to leadership development for aspiring leaders.</p><p>

<p> This dissertation is concerned with service operations systems with considerations of incentives, information asymmetries and bounded rationalities. Chapter 1 provides an overview of the dissertation. </p><p> In Chapter 2, motivated by the information service operations for the agricultural sectors in the developing economies, we propose a Cournot quantity competition model with price uncertainty, wherein the marketing boards of farmers' cooperatives have the options to obtain costly private information, and form information sharing coalitions. We study the social value of market information and the incentives for information sharing among farmers. </p><p> In Chapter 3, we offer a behavioral (bounded rationality) theory to explain product/technology adoption puzzle: Why superior investment goods are not widely purchased by consumers? We show that present-bias encourages procrastination, but discourages strategic consumer behavior. Advance selling is beneficial not only to the consumers as a commitment device, but also to the seller as a price discrimination instrument. </p><p> In Chapter 4, motivated by the fresh-product delivery industry, we propose a model of service operations systems in which customers are heterogeneous both in terms of their private delay sensitivity and taste preference. The service provider maximizes revenue through jointly optimal pricing strategies, steady-state scheduling rules, and probabilistic routing policies under information asymmetry. Our results guide service mechanism design using substitution strategies. </p><p> In Chapter 5, motivated by the puzzle of excessively long queue for low quality service in tourism and healthcare industries, we study the customers&rsquo; learning behaviors in the service operations systems, when they hold incorrect beliefs about the population distribution. We highlight a simple behavioral explanation for the blind ``buying frenzy'' in service systems with low quality: The customers under-estimate others' patience and are trapped in a false optimism about the service quality. </p><p> Chapter 6 concludes the dissertation with a summary of the main results and policy recommendations.</p><p>

<p> This praxis explores a new approach to the problem of estimating the uncertainty attributable to inconsistent pairwise comparison judgments in the Analytic Hierarchy Process (AHP), a prominent decision-making methodology used in numerous fields, including systems engineering and engineering management. Based on insights from measurement theory and established error propagation equations, the work develops techniques to estimate the uncertainty of aggregated priorities for decision alternatives based on measures of inconsistency for component pairwise comparison matrices. This research develops two formulations for estimating the error: the first, more computationally intensive and accurate, uses detailed calculations of parameter errors to estimate the aggregated uncertainty, while the second, significantly simpler, uses an estimate of mean relative error (MRE) for each pairwise comparison matrix to estimate the aggregated error. This paper describes the derivation of both formulations for the linear weighted sum method of priority aggregation in AHP and uses Monte Carlo simulation to test their estimation accuracies for diverse problem structures and parameter values. The work focuses on the two most commonly used methods of deriving priority weights in AHP: the eigenvector method (EVM) and the geometric mean method (GMM). However, the approach of estimating the propagation of measurement errors can be readily applied to other hierarchical decision support methodologies that use pairwise comparison matrices. The developed techniques provide analysts the ability to easily assess decision model uncertainties attributable to comparative judgment inconsistencies without recourse to more complex optimization routines or simulation experiments described previously in the professional literature.</p><p>

<p> Crowdsourcing is an increasingly common method used for new product development in large engineering-focused companies. While effective at generating a large number of ideas, previous research has noted that there is not an efficient mechanism to sort ideas based on the sponsor's desired outcomes. Without such a mechanism, the sponsor is left to evaluate ideas individually in a labor-intensive effort. This paper evaluates the extent to which information revealed by the crowd during the course of a crowdsourcing event can be used to accurately predict sponsor selection of submitted ideas. The praxis reviews current literature relevant to new product development, innovation management, and crowdsourcing as well as methods for efficient sorting. Using a quantitatively-based methodology, the author develops and evaluates several predictive models using various attributes of the crowd reaction to crowdsourced ideas. Ultimately, the praxis proposes a model that can significantly reduce the burden of sorting through submissions and determining the submissions which merit further review. </p><p>

<p> As organizations shift more work to outsourced partners, a problem for management is how to accomplish not only short-term/tactical performance but also how to leverage network relationships for long-term/strategic advantage. Outsourced manufacturing supply chains represent a unique context for study as internal and external participants share a common goal for supply chain performance but also have separate and independent goals. Trust and collaboration are among the inputs that can influence supply chain outcomes, but there is a gap in understanding these variables with respect to strategic outcomes like innovation. This research uses systematic review of peer reviewed literature to examine the role of trust and collaboration in outsourced manufacturing supply chains, and specifically the potential for these variables to shape relationships for advancing innovation. Two conditions are found that derive from the presence of trust: 1) <i>willingness</i> to engage, and 2) <i>commitment</i> for long-term relationship and to overcome failures. Three conditions are found that derive from the presence of collaboration: 1) <i>awareness</i> of capability, 2) <i>sharing </i> information, and 3) <i>integration</i> of resources. These conditions shape an underlying mindset that can either advance or diminish innovation, and together create either transactional, operational, serendipitous, or strategic orientations. The conclusion is that a strategic orientation promotes the path for innovation and arises from high willingness, commitment, awareness, sharing, and integration that are shaped by trust and collaboration. The findings have implication for organizations that seek to foster interactions for innovation and to go beyond what is necessary to accomplish short-term operational objectives.</p><p>

Recently, tremendous advancements have been made in the solution of the set partitioning problem (SPP), which finds application in numerous real-world industrial scheduling problems such as fleet assignment and airline crew scheduling. Two major thrusts in the development of solution procedures for SPP have been variable reduction and the use of, either inherent or enforced, special structures in the element-set incidence matrix on which the problem is defined. This dissertation demonstrates the use of hidden network structures for the solution of SPP. The hidden network structures in the element-set incidence matrix are revealed via preprocessing. The importance of problem preprocessing in the development of efficient computational techniques has been well established. We develop heuristic procedures to extract hidden network submatrices in a (0,1) matrix. The heuristics are based on Fujishige's PQ-graph algorithm (1980), which is one of the most computationally efficient algorithms for testing the graph realizability of a (0,$\pm$1) matrix. The computational implementation of the algorithm is the first of its kind and the computational experience in this dissertation validates the almost linear time computational complexity of graph realizability. Fujishige's algorithm lends itself to modification for the development of heuristic procedures to identify submatrices that transform to pure network. The heuristics we develop are based on rules that use Fujishige's PQ-graph characteristics so that the computational efficiencies afforded by PQ-graphs are retained. By finding an embedded network row submatrix, SPP is transformed to a network with side constraints. Flow conditions on the revealed pure network are then used in a procedure for effecting variable reduction. Variable reduction has been recognized to be essential for efficient solution of SPP. By finding an embedded network column submatrix SPP is transformed to a network with side columns. The resulting formulation is used in finding a feasible solution for SPP quickly. For the purpose of obtaining optimal and suboptimal solutions to SPP we use a reformulation of the problem. By repetitive application of the heuristic to obtain an embedded network column submatrix, SPP is reformulated by transforming the element-set incidence matrix to a concatenation of pure network submatrices. This reformulation is to be used within an enumerative procedure which allows significant variable reduction. The research is summarized as follows: (i) Fujishige's PQ-graph algorithm is implemented by supplementing algorithmic details which have never been published; (ii) Heuristic procedures to extract hidden network submatrices are developed using rules which have been developed to maintain Fujishige's PQ-graph characteristics; (iii) The use of embedded network structures is demonstrated for the solution of SPP via variable reduction procedures and procedures for efficient generation of a feasible solution; (iv) Procedure that use a reformulation to find optimal solutions for set partitioning problems is developed.

Uzzi (1996, p. 674) highlighted that there is a “growing need to understand how social structure assists or impedes economic performance.” In this dissertation, I contribute to this understanding by constructing dynamic supernetwork models that explicitly integrate social networks with economic network models and that rigorously capture the role that relationship levels play. The research in this dissertation is motivated by the growing literature that empirically and theoretically highlights the importance of relationships in supply chains (cf. Cannon and Perreault (1999), Bernardes and Fensterseifer (2004), and Baker and Faulkner (2004)) and financial transactions (cf. Berger and Udell (1995), Anthony (1997), and Uzzi (1999)). As this literature shows, the existence of appropriate social networks can affect not only the risk associated with the transactions but also transaction costs. By explicitly including the role that relationships play in economic transactions, I extend the previous research on supply chain network models (see, for example, Nagurney, Dong, and Zhang (2002), Nagurney, Cruz, and Matsypura (2003), and Nagurney and Matsypura (2005)). Furthermore, I extend the literature on financial network models (see, for example, Nagurney and Ke (2001, 2003) and Nagurney and Cruz (2003a,b, 2004)). Specifically, I first develop a model consisting of an integrated supply chain and social network. I then construct a model consisting of an integrated financial and social network. Finally, I extend both these models to an international setting. The social networks consist of relationships of different strength as they have been described in the papers by Granovetter (1973), Freeman, Borgatti, and White (1991), and Golicic, Foggin, and Mentzer (2003). The “supernetwork” models describe how the behavior of the multicriteria decision-makers and induced flows influence the co-evolution of social and economic networks. Numerical examples highlight the unique ability of this framework to analyze the interaction between the social network and the economic network. The models are based on variational inequality theory for the study of the equilibrium states (cf. Nagurney (1999)) and projected dynamical systems theory for the study of the associated dynamics (cf. Nagurney and Zhang (1996a)).

We study the effect that consumer returns have on the coordination of a two-echelon supply chain with a single manufacturer and a single retailer that faces stochastic demand and a certain proportion of consumer returns for a single product. Returned goods command a full refund and result in reverse logistics costs for both retailer and manufacturer. The manufacturer sets a wholesale price for the product and may also set a repurchase price at which she will buy any product left over at the retailer at the end of the selling season. The selling price for the product may be either given exogenously or a variable under the retailer's control given stochastic, price-dependent demand. The retailer sets the order quantity and in the latter case also the selling price. We analyze the optimal centralized and decentralized profit maximizing solutions and compare the optimal actions and profits with those associated with the classical model that ignores consumer returns. The results we obtain are counterintuitive: (1) higher profits and better coordination can be achieved when the players acting in a decentralized fashion do not consider any information about consumer returns as they make their pricing and ordering decisions, (2) retailer, manufacturer and total supply chain profits increase as the retailer faces a larger share of the logistics costs associated with consumer returns, (3) buy-back contracts may be detrimental to supply chain coordination if consumer returns are ignored in the decision-making process. We also study the case in which the retailer postpones his pricing decision until after demand uncertainty is resolved when commercial returns are present in the system. We observe that also, under the presence of commercial returns, the price postponement strategy leads to larger expected profits for both, manufacturer and retailer, and thus, a better coordination of the supply chain. In the last part of this dissertation, we study the Returns Allowance Credit Contract. This new type of contract is implemented when commercial returns are present in the supply chain and the retailer bears all the logistics costs associated with the returns. The manufacturer offers certain returns allowance credit in order not to lose retailer's good will.

With supply chains spanning the globe, and with increasing time-sensitivity for various products in many markets, timely deliveries are becoming a strategy, as important as productivity, quality, and even innovation (see, e.g., Gunasekaran, Patel, and McGaughey (2004), Christopher (2005), and Nagurney (2006)). A product is considered to be time-sensitive, if there is a strict time requirement regarding that product, either as a characteristic of the product itself or on the demand side. In particular, a time-sensitive product must have at least one of the following two properties: (1) the product loses its value rapidly, due to either obsolescence or perishability, which can lead to extra waste and cost, if unused; (2) the demand for it is sensitive to the elapsed time for the order fulfillment; the failure to satisfy the demand on-time may result in the loss of potential market share, or, even worse, additional injuries or death as in times of crises. This dissertation formulates, analyzes, and solves a spectrum of supply chain network problems for time-sensitive products, ranging from fast fashion to food to pharmaceuticals. Specifically, I first develop a model that captures the trade-offs between the operational costs and time issues in the apparel industry. I then construct a sustainable fashion supply chain network model under oligopolistic competition and brand differentiation. I, subsequently, capture the deterioration of fresh produce along the entire supply chain through arc multipliers with time decay. Finally, I consider the supply chain network design problem for critical needs products, as in times of crises and humanitarian relief operations. I also develop a supply chain network design/redesign model with multiple products, with particular relevance to healthcare. This dissertation consists of advances in the modeling, analysis, and design of supply chain networks for time-sensitive products, all unified through the methodology of variational inequality theory (see Nagurney (1999)), coupled with network theory and multicriteria decision-making. The framework captures the underlying behavior associated with the operation and management of the associated supply chains, whether that of central optimization or competition, allows for the graphical depiction of the supply chain network structures, and efficient and effective solution.

We study a class of stochastic resource allocation problems that specifically deals with effective utilization of resources in the interest of social value creation. These problems are treated as a separate class of problems mainly due to the nonprofit nature of the application areas, as well as the abstract structure of social value definition. As part of our analysis of these unique characteristics in societal resource allocation, we consider two major application areas involving such decisions. The first application area deals with resource allocations for foreclosed housing acquisitions as part of the response to the foreclosure crisis in the U.S. Two stochastic dynamic models are developed and analyzed for these types of problems. In the first model, we consider strategic resource allocation decisions by community development corporations (CDCs), which aim to minimize the negative effects of foreclosures by acquiring, redeveloping and selling foreclosed properties in their service areas. We model this strategic decision process through different types of stochastic mixed-integer programming formulations, and present alternative solution approaches. We also apply the models to real-world data obtained through interactions with a CDC, and perform both policy related and computational analyses. Based on these analyses, we present some general policy insights involving tradeoffs between different societal objectives, and also discuss the efficiency of exact and heuristic solution approaches for the models. In the second model, we consider a tactical resource allocation problem, and identify socially optimal policies for CDCs in dynamically selecting foreclosed properties for acquisition as they become available over time. The analytical results based on a dynamic programming model are then implemented in a case study involving a CDC, and social return based measures defining selectivity rates at different budget levels are specified. The second application area involves dynamic portfolio management approaches for optimization of surgical team compositions in robotic surgeries. For this problem, we develop a stochastic dynamic model to identify policies for optimal team configurations, where optimality is defined based on the minimum experience level required to achieve the maximum attainable performance over all ranges of feasible experience measures. We derive individual and dependent performance values of each surgical team member by using data on operating room time and team member experience, and then use them as inputs to a stochastic programming based framework that we develop. Several insights and guidelines for dynamic staff allocation to surgical teams are then proposed based on the analytical and numerical results derived from the model.