Managing Enterprise Risks of Technological Systems: An Exploratory Empirical Analysis of Vulnerability Characteristics as Drivers of Exploit Publication*

Enterprises experience opportunistic exploits targeted at vulnerable technology. Vulnerabilities in software‐based applications, service systems, enterprise platforms, and supply chains are discovered and disclosed on an alarmingly regular basis. A necessary enterprise risk management task concerns identifying and patching vulnerabilities. Yet it is a costly affair to develop and deploy patches to alleviate risk and prevent damage from exploit attacks. Given the limited resources available, technology producers and users must identify priorities for such tasks. When not overlooked, vulnerability‐patching tasks often are prioritized based on vulnerability disclosure dates, thus vulnerabilities disclosed earlier usually have patches developed and deployed earlier. We suggest priorities also should focus on time‐dependent likelihoods of exploits getting published. We analyze data on software exploits to identify factors associated with the duration between a vulnerability discovery date and the date when an exploit is publicly available, a time window for patching before exploit attack levels may escalate. Actively prioritizing vulnerability patching based on likelihoods of exploit publication may help lessen losses due to exploit attacks. Technology managers might apply the insights to better estimate relative risk levels, and better prioritize protection efforts toward vulnerabilities having higher risk of earlier exploitation.     

Compliance Strategies under Permits for Emissions

We characterize the trade‐offs among firms' compliance strategies in a market‐based program where a regulator interested in controlling emissions from a given set of sources auctions off a fixed number of emissions permits. We model a three‐stage game in which firms invest in emissions abatement, participate in a share auction for permits, and produce output. We develop a methodology for a profit‐maximizing firm to derive its marginal value function for permits and translate this value function into an optimal bidding strategy in the auction. We analyze two end‐product market scenarios independent demands and Cournot competition. In both scenarios we find that changing the number of available permits influences abatement to a lesser extent in a dirty industry than in a cleaner one. In addition, abatement levels taper off with increasing industry dirtiness levels. In the presence of competition, firms in a relatively clean industry can, in fact, benefit from a reduction in the number of available permits. Our findings are robust to changes in certain modeling assumptions.     

Optimal Design of the Online Auction Channel: Analytical,Empirical, and Computational Insights*

The focus of this study is on business‐to‐consumer (B2C) online auctions made possible by the advent of electronic commerce over an open‐source, ubiquitous Internet Protocol (IP) computer network. This work presents an analytical model that characterizes the revenue generation process for a popular B2C online auction, namely, Yankee auctions. Such auctions sell multiple identical units of a good to multiple buyers using an ascending and open auction mechanism. The methodologies used to validate the analytical model range from empirical analysis to simulation. A key contribution of this study is the design of a partitioning scheme of the discrete valuation space of the bidders such that equilibrium points with higher revenue structures become identifiable and feasible. Our analysis indicates that the auctioneers are, most of the time, far away from the optimal choice of key control factors such as the bid increment, resulting in substantial losses in a market with already tight margins. With this in mind, we put forward a portfolio of tools, varying in their level of abstraction and information intensity requirements, which help auctioneers maximize their revenues.     

Linking IT Applications with Manufacturing Strategy: An Intelligent Decision Support System Approach

Research has indicated the importance of matching Information Technology (IT) applications or manufacturing systems with the competitive strategy of a company. Selection of the right type of IT application is, however, a challenging task. When a company with a given dominant process structure emphasizes two or more competitive priorities, such as quality, product flexibility, etc., an unaided manager faces a complex decision problem in choosing from alternative IT applications available in the areas of product design through distribution. In this paper, we develop an Intelligent Decision Support System (IDSS) to assist managers with assessment of the relative importance of competitive priorities in their organization, evaluation of the fit between the competitive priorities and their dominant process structure, and identification of the IT applications that are consistent with both the competitive priorities and the process structure. The IDSS is comprised of an interactive user interface, a knowledge database, a decision model, and a Knowledge-Based System (KBS) that was developed using the 1st Class KBS shell. Validation of the system illustrates that its performance is as good as the human expert, and it has the potential to facilitate effective and swift decision making in the selection of appropriate IT applications that best match an organization's manufacturing strategy. The choice and use of the right type of IT application should provide a company with a competitive edge.     

Facility Dispersion Problems Under Capacity and Cost Constraints

The MAX-MIN dispersion problem, which arises in the placement of undesirable facilities, involves selecting a specified number of sites among a set of potential sites so as to maximize the minimum distance between any pair of selected sites. We consider different versions of this dispersion problem where each potential site has an associated storage capacity and a storage cost. A typical problem in this context is to choose a subset of potential sites so that the total capacity of the chosen sites is at least a given value, the total storage cost is within the specified budget and the minimum distance between any pair of chosen sites is maximized. Since these constrained optimization problems are NP-hard in general, we consider whether there are efficient approximation algorithms for them with good performance guarantees. Our results include approximation algorithms for some versions, approximation schemes for some geometric versions and polynomial algorithms for special cases. We also present results that bring out the intrinsic difficulty of obtaining near-optimal solutions to some versions.     

Selecting IT applications in manufacturing: a KBS approach

The use of the right type of Information Technology (IT) applications or manufacturing systems is expected to usher in a competitive advantage. Selection of the right type of IT application is, however, a challenging task. When a company, with a given dominant process structure, emphasizes two or more competitive priorities, such as quality, product flexibility, etc., an unaided manager faces a complex decision problem in choosing from alternative IT applications available in the areas of product design through distribution. In this paper, we present a Knowledge Based System (KBS) that would assist managers with the identification of IT applications that are consistent with both the competitive priorities and the process structure. Validation of the system illustrates that its performance is consistent with the human experts, and it has the potential to facilitate effective and swift decision-making in the selection of appropriate IT applications that best match an organization's manufacturing strategy.     

Enforcement matters: The effective regulation of labour

The distinction between de jure and de facto regulation is well understood in theory, but has rarely been applied to cross‐country empirical work on the impact of labour regulation on labour market outcomes for lack of data. Policy debate has been based on measures of stringency of law, suggesting a negative correlation between labour regulation and labour market outcomes. This article provides new cross‐country measures of labour law enforcement and evidence of a negative correlation between stringency and intensity of enforcement. Previous results concerning the consequences of labour regulation and the legal origins theory no longer hold when a measure of effective labour regulation is used.     

Sharing Responsibility for Product Recovery Across the Supply Chain

Extended producer responsibility (EPR) programs typically hold the producer—a single actor defined by the regulator—responsible for the environmental impacts of end‐of‐life products. This is despite emphasis on the need to involve all actors in the supply chain in order to best achieve the aims of EPR. In this paper, we examine the economic and environmental implications of product recovery mandates and shared responsibility within a supply chain. We use a two‐echelon model consisting of a supplier and a manufacturer to determine the impacts of product collection and recycling mandates on the incentive to recycle and resulting profits in the integrated and decentralized supply chains. For the decentralized supply chain, we demonstrate how the sharing of responsibility for product recovery between the echelons can improve total supply chain profit and suggest a contract menu that can Pareto‐improve profits. To examine both the economic and environmental performance associated with responsibility sharing, we propose a social welfare construct that includes supply chain profit, consumer surplus, and the externalities associated with virgin material extraction, product consumption, and disposal of nonrecycled products. Using a numerical example, we discuss how responsibility sharing may or may not improve social welfare. The results of this paper are of value to firms either anticipating or subject to product recovery legislation, and to social planners that attempt to balance economic and environmental impacts and ensure fairness of such legislation.     

Remanufacturing and the Component Commonality Decision

Firms often determine whether or not to make components common across products by focusing on the manufacturing and sales of new products only. However, component commonality decisions that ignore remanufacturing can adversely affect the profitability of the firm. In this article we analyze how remanufacturing could reverse the OEM's commonality decision that is based on the manufacturing and sales of new products only. Specifically, we determine the conditions under which the OEM's optimal decision on commonality may be reversed and illustrate how her profit can be significantly higher if remanufacturing is taken into account ex ante. We illustrate the implementation of our model for two products in the Apple iPad family.     

All in the Family: How Do Social Capital and Material Wellbeing Affect Relational Wellbeing?

We use a unique dataset from Italy to investigate the impact of socioeconomic characteristics and social capital on family wellbeing and satisfaction. We assess wellbeing using four dimensions of satisfaction with family life: satisfaction with decision making processes, with relationships with partner and children, and with time spent with children. Social capital is measured through information about membership in organizations, trust, and interactions with others. We find that while socioeconomic characteristics in general do not have strong effects on family wellbeing, social capital matters for family life satisfaction.