Multi‐Echelon Inventory Management under Short‐Term Take‐or‐Pay Contracts

We extend the Clark–Scarf serial multi‐echelon inventory model to include procuring production inputs under short‐term take‐or‐pay contracts at one or more stages. In each period, each such stage has the option to order/process at two different cost rates; the cheaper rate applies to units up to the contract quantity selected in the previous period. We prove that in each period and at each such stage, there are three base‐stock levels that characterize an optimal policy, two for the inventory policy and one for the contract quantity selection policy. The optimal cost function is additively separable in its state variables, leading to conquering the curse of dimensionality and the opportunity to manage the supply chain using independently acting managers. We develop conditions under which myopic policies are optimal and illustrate the results using numerical examples. We establish and use a generic one‐period result, which generalizes an important such result in the literature. Extensions to cover variants of take‐or‐pay contracts are included. Limitations are discussed.     

Transformational Leadership Education and Agency Perspectives in Business School Pedagogy: A Marriage of Inconvenience?

We critique transformational leadership education in university business schools based on a literature review, a study of the websites of 21 leading business schools, and an analysis of two presentations to business school students at Massachusetts Institute of Technology and Stanford University by the former CEO of General Electric, Jack Welch. Our critique draws attention to the unresolved tension between two motivating ideas that underpin much teaching in business schools: collective interest ideas that permeate transformational leadership education; and self-interest ideas derived from agency theory. Transformational leadership tends to be depicted as a process by which leaders exert a 'top-down' influence over the activities of others, while simultaneously asserting that their organizations have a common purpose and pursue a collective interest. We highlight the risk that business schools are producing graduates who will attempt to appeal to common needs (guided by precepts of transformational leadership) but who will simultaneously enact contradictory performance management systems (guided by agency theory). We encourage business school educators in leadership to adopt approaches which are more critical, relational and reflexive. We suggest some general directions for an alternative leadership prospectus, based on followership, the promotion of critical upward communication within organizations, and the recognition of leadership as a contested, discursive and co-constructed phenomenon.     

Pathways to social value and social change: An integrative review of the social entrepreneurship literature

Social entrepreneurship has emerged as an important means of addressing grand challenges. Although research on the topic has accelerated, scholars have yet to articulate an overarching framework that links the different pathways taken by social entrepreneurs with the positive effects of these efforts. To address this shortcoming, we conducted a systematic literature review which enabled us to conceptually differentiate between social value and social change as distinct outcomes of social entrepreneurship and identify seven pathways for achieving these outcomes. Building on our analysis, we outline a research agenda for questions pertaining to: the dynamics between social value and social change; how contextual factors and social entrepreneurs influence various pathways; design principles of business models and innovations that facilitate social value and social change; and defining, measuring, and ensuring accountability for social value and social change.     

Bootstrap Methods for Markov Processes

The block bootstrap is the best known bootstrap method for time‐series data when the analyst does not have a parametric model that reduces the data generation process to simple random sampling. However, the errors made by the block bootstrap converge to zero only slightly faster than those made by first‐order asymptotic approximations. This paper describes a bootstrap procedure for data that are generated by a Markov process or a process that can be approximated by a Markov process with sufficient accuracy. The procedure is based on estimating the Markov transition density nonparametrically. Bootstrap samples are obtained by sampling the process implied by the estimated transition density. Conditions are given under which the errors made by the Markov bootstrap converge to zero more rapidly than those made by the block bootstrap.