Generalized Reduced‐Form Auctions: A Network‐Flow Approach

We develop a network‐flow approach for characterizing interim‐allocation rules that can be implemented by ex post allocations. Our method can be used to characterize feasible interim allocations in general multi‐unit auctions where agents face capacity constraints, both ceilings and floors. Applications include a variety of settings of practical interest, ranging from individual and group‐specific capacity constraints, set‐aside sale, partnership dissolution, and government license reallocation.     

A Mechanism‐Design Approach to Speculative Trade

When two parties have different prior beliefs about some future event, they can realize gains through speculative trade. Can these gains be realized when the parties' prior beliefs are not common knowledge? We examine a simple example in which two parties having heterogeneous prior beliefs, independently drawn from some distribution, bet on what future action one of them will choose. We define a notion of “constrained interim‐efficient” best and ask whether they can be implemented in Bayesian equilibrium by some mechanism. Our main result establishes that as the costs of unilaterally manipulating the bet's outcome become more symmetric across states, implementation becomes easier. In particular, when these costs are equal in both states, implementation is possible for any distribution.     

Fixed‐Smoothing Asymptotics in a Two‐Step Generalized Method of Moments Framework

This paper develops the fixed‐smoothing asymptotics in a two‐step generalized method of moments (GMM) framework. Under this type of asymptotics, the weighting matrix in the second‐step GMM criterion function converges weakly to a random matrix and the two‐step GMM estimator is asymptotically mixed normal. Nevertheless, the Wald statistic, the GMM criterion function statistic, and the Lagrange multiplier statistic remain asymptotically pivotal. It is shown that critical values from the fixed‐smoothing asymptotic distribution are high order correct under the conventional increasing‐smoothing asymptotics. When an orthonormal series covariance estimator is used, the critical values can be approximated very well by the quantiles of a noncentral F distribution. A simulation study shows that statistical tests based on the new fixed‐smoothing approximation are much more accurate in size than existing tests.     

A Generalized QMRA Beta‐Poisson Dose‐Response Model

Quantitative microbial risk assessment (QMRA) is widely accepted for characterizing the microbial risks associated with food, water, and wastewater. Single‐hit dose‐response models are the most commonly used dose‐response models in QMRA. Denoting as the probability of infection at a given mean dose d, a three‐parameter generalized QMRA beta‐Poisson dose‐response model, , is proposed in which the minimum number of organisms required for causing infection, K_min, is not fixed, but a random variable following a geometric distribution with parameter . The single‐hit beta‐Poisson model, , is a special case of the generalized model with K_min = 1 (which implies ). The generalized beta‐Poisson model is based on a conceptual model with greater detail in the dose‐response mechanism. Since a maximum likelihood solution is not easily available, a likelihood‐free approximate Bayesian computation (ABC) algorithm is employed for parameter estimation. By fitting the generalized model to four experimental data sets from the literature, this study reveals that the posterior median estimates produced fall short of meeting the required condition of = 1 for single‐hit assumption. However, three out of four data sets fitted by the generalized models could not achieve an improvement in goodness of fit. These combined results imply that, at least in some cases, a single‐hit assumption for characterizing the dose‐response process may not be appropriate, but that the more complex models may be difficult to support especially if the sample size is small. The three‐parameter generalized model provides a possibility to investigate the mechanism of a dose‐response process in greater detail than is possible under a single‐hit model.     

Developing a Human‐centred and Science‐based Approach to Design: The Knowledge Management Platform Project

The gap between theory and practice is a persistent problem in management and organization research. In this respect, several scholars have suggested that ‘design’ is an ideal‐typical form of mode 2 knowledge production. Design research develops knowledge in the service of action and problem solving in organizational settings. In this paper, we connect two perspectives on design that can be considered to be largely complementary but have hardly been combined and integrated in practice: science‐based design drawing on design propositions grounded in research and human‐centred design emphasizing an active and systematic participation by users and other stakeholders. An integrated approach that builds on both perspectives is developed and subsequently applied to designing and developing a portal for mapping competencies in an information technology cluster. This project involves creating problem awareness and articulating design propositions as well as developing scenarios of use, experimenting with prototypes, and organizational transformation. As such, this methodology addresses the dual challenge of rigour and relevance by producing both scientific and practical knowledge.     

Reviewing Industry Life‐cycle Theory: Avenues for Future Research

This paper explicates the key mechanisms and research themes of industry life‐cycle theory and assesses the extent to which empirical evidence supports such an approach. It is based on a review of 216 industry life‐cycle studies. The review of the theory is organized around industry emergence and transition to industry maturity. The key research themes concern changes in industry structure, changes in the nature of innovation, and the determinants of survival. Three potential special cases, i.e. services, complex products and systems, and cultural industries, are identified, and five research questions for future enquiry are presented.     

Reviewing Leadership Styles: Overlaps and the Need for a New ‘Full‐Range’ Theory

A central topic in leadership research concerns the impact of leadership style – the pattern of attitudes that leaders hold and behaviors they exhibit. Since the year 2000, several new leadership styles have been proposed to capture important missing aspects beyond the dominant charismatic/transformational and transactional framework. The authors review the emerging literature on these new styles – ideological leadership, pragmatic leadership, authentic leadership, ethical leadership, spiritual leadership, distributed leadership, and integrative public leadership – as well as the recent work on servant leadership. They also comment on the Ohio State studies on leadership, and then discuss the ways in which these many styles overlap with transformational leadership and each other, and issue a call to leadership researchers to collectively develop a new ‘full‐range’ model of leadership that encompasses and distills what is unique about these various styles. The authors argue that such an integrated full‐range model is necessary for research on leadership style to progress.     

A Nonatomic‐Game Approach to Dynamic Pricing under Competition

We study a revenue management problem involving competing firms. We assume the presence of a continuum of infinitesimal firms where no individual firm has any discernable influence over the evolution of the overall market condition. Under this nonatomic‐game approach, the unanimous adoption of an equilibrium pricing policy by all firms will yield a market‐condition process that in turn will elicit the said policy as one of the best individual responses. For both deterministic‐ and stochastic‐demand cases, we show the existence of equilibrium pricing policies that exhibit well‐behaving monotone trends. Our computational study reveals many useful insights, including the fact that only a reasonable number of firms are needed for our approach to produce near‐rational pricing policies.     

The SERVICE Framework: A Public‐service‐dominant Approach to Sustainable Public Services

In this paper we argue that the new public management has been a flawed paradigm for public services delivery that has produced very internally efficient but externally ineffective public service organizations. Subsequently we develop the SERVICE framework for sustainable public services and public service organizations. This framework is rooted within the public‐service‐dominant business logic and emphasizes the need for a focus on external value creation rather than internal efficiency alone.     

Integrating a Top‐Down and a Bottom‐Up Approach: Formal and Informal Risk‐Handling Strategies in a Utility Company

In this study, we bring together a top‐down and a bottom‐up approach of risk handling. We do so by conceptualizing and qualitatively and quantitatively measuring formal and informal risk‐handling strategies in a Dutch utility company. We conceive of formal risk handling as regulating, training, and educating safety and enforcing rule compliance, while we distinguish three different informal risk‐handling strategies: discretionary specialization, tacit knowledge, and taking personal responsibility. We show that the formal risk‐handling strategy and the three informal risk‐handling strategies can be measured separately. Hence, we have validated the measurement of all four strategies derived from two different risk‐handling approaches. Moreover, we have demonstrated that the perceived use of the four strategies has different effects on unsafe behavior: formal risk handling and tacit knowledge decrease it, discretion increases it, and taking personal responsibility has no effect on unsafe behavior.     

Executives’ Views of Factors Affecting Governance Change in a Not‐for‐Profit Setting

Knowing the factors that executives deem critical to governance change can improve our understanding of how such changes come about and can help us evaluate those changes. Interviews with business and finance executives at 11 colleges reveal the importance to governance change of chief executive and board member leadership and interactions, as well as executive communication style. Costs are clear constraints to action, particularly since benefits are not quantified and are difficult to describe. Efforts to discuss governance with internal stakeholders require persistence to overcome narrow, individualized concerns. Communication about governance to external stakeholders is rare and represents a missed opportunity for stakeholder feedback and the development of trust. Executives appear willing to adopt governance forms without considering the idiosyncrasies of their institutional field, limiting the working definition of governance and its potential. For corporations and not‐for‐profit enterprises these findings hold implications for the context in which leadership is exercised and the shape of governance structures. They also pose a fundamental ethical dilemma for leaders to address.     

A Reliability‐Based Capability Approach

This article proposes a rigorous mathematical approach, named a reliability‐based capability approach (RCA), to quantify the societal impact of a hazard. The starting point of the RCA is a capability approach in which capabilities refer to the genuine opportunities open to individuals to achieve valuable doings and beings (such as being mobile and being sheltered) called functionings. Capabilities depend on what individuals have and what they can do with what they have. The article develops probabilistic predictive models that relate the value of each functioning to a set of easily predictable or measurable quantities (regressors) in the aftermath of a hazard. The predicted values of selected functionings for an individual collectively determine the impact of a hazard on his/her state of well‐being. The proposed RCA integrates the predictive models of functionings into a system reliability problem to determine the probability that the state of well‐being is acceptable, tolerable, or intolerable. Importance measures are defined to quantify the contribution of each functioning to the state of well‐being. The information from the importance measures can inform decisions on optimal allocation of limited resources for risk mitigation and management.     

A Load‐based and Due‐date‐oriented Approach to Order Review/Release in Job Shops*

In this paper, a new order review/release (ORR) method is proposed for shop floor control systems. The proposed method utilizes both job due date and shop load information to improve the effectiveness of the ORR function in production systems. The performance of the new method is compared to those of a few well‐known ORR methods under four experimental conditions. The results of extensive simulation experiments indicate that the proposed method is superior with respect to the mean absolute deviation measure. In general, it is also better than existing methods for the other performance measures. Furthermore, we show that the proposed method is more robust to variations in system load and processing times than the other ORR methods examined.     

A Data‐Driven Approach to Assessing Supply Inadequacy Risks Due to Climate‐Induced Shifts in Electricity Demand

The U.S. electric power system is increasingly vulnerable to the adverse impacts of extreme climate events. Supply inadequacy risk can result from climate‐induced shifts in electricity demand and/or damaged physical assets due to hydro‐meteorological hazards and climate change. In this article, we focus on the risks associated with the unanticipated climate‐induced demand shifts and propose a data‐driven approach to identify risk factors that render the electricity sector vulnerable in the face of future climate variability and change. More specifically, we have leveraged advanced supervised learning theory to identify the key predictors of climate‐sensitive demand in the residential, commercial, and industrial sectors. Our analysis indicates that variations in mean dew point temperature is the common major risk factor across all the three sectors. We have also conducted a statistical sensitivity analysis to assess the variability in the projected demand as a function of the key climate risk factor. We then propose the use of scenario‐based heat maps as a tool to communicate the inadequacy risks to stakeholders and decisionmakers. While we use the state of Ohio as a case study, our proposed approach is equally applicable to all other states.     

Managing Co‐creation Design: A Strategic Approach to Innovation

Co‐creation offers firms and their network of actors significant opportunities for innovation, as each actor offers access to new resources through a process of resource integration. However, despite the significant advantages that co‐creation can offer, there is surprisingly little research providing a strategic approach for identifying the most advantageous co‐creation opportunities, especially when many possible options are available. Recently, scholars have called for research that develops tools and processes related to co‐creation. This study addresses these priorities, making two contributions. First, in contrast to previous work considering co‐creation more generally, or focusing on one specific form only, e.g. co‐production, this paper offers a detailed and granular approach to co‐creation design. A co‐creation design framework is developed, which incorporates multiple design dimensions and categories that can reveal new co‐creation opportunities. Second, the research extends the application of a design approach, specifically within the context of co‐creative activities. The authors use field‐based research with senior executives to develop a framework that includes key co‐creation design elements. A morphological approach is used to explore how a lead firm can identify attractive co‐creation opportunities. An innovation solution in one organization provides an illustration of how the co‐creation design framework can be applied.     

A Context‐sensitive Approach to Analysing Talk in Strategy Meetings

The talk of managers in meetings is central to organizational life and crucial to research in strategic management, as well as managerial and organizational cognition, sensemaking and decision‐making. To achieve full understanding, both the text and the context of discussion require systematic analysis, but most approaches treat context as everything that is known and observed beyond the immediate text. This obscures different readings of the text of meetings. To resolve this problem, the discourse historical approach (DHA) to critical discourse analysis is outlined as a framework within which researchers can analyse the text and context of talk in meetings. The primary contribution of this paper is to isolate four ‘levels of context’ as a heuristic framework within which discursive practices, strategies and texts can be located. By making explicit the levels of contextual analysis that are implicit in other methods, and illustrating the DHA using an episode of strategic discussion from a multinational company, this paper shows how researchers can use the approach to analyse the naturally occurring talk of senior managers in meetings, which is arguably the most important but yet under‐explored venue for strategizing.     

Likelihood‐Based Estimation of Latent Generalized ARCH Structures

GARCH models are commonly used as latent processes in econometrics, financial economics, and macroeconomics. Yet no exact likelihood analysis of these models has been provided so far. In this paper we outline the issues and suggest a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a Bayesian solution in O(T) computational operations, where T denotes the sample size. We assess the performance of our proposed algorithm in the context of both artificial examples and an empirical application to 26 UK sectorial stock returns, and compare it to existing approximate solutions.     

A Wavelet‐Based Approach to Preserve Privacy for Classification Mining*

Despite the commercial success of data mining, a major drawback has been acknowledged across academic, industry, and government sectors, namely, the issue of violating the privacy of individuals. We propose a data transformation method based on wavelets to disguise private data while preserving the original classification patterns. Wavelet transformations have been used extensively in signal processing for data reduction, multiresolution analysis, and removing noise from data. In our implementation, two commonly used wavelet transforms, the Haar and the Daub‐4 transforms, are tested for pattern and privacy preservation in classification mining tasks. Empirical results confirm that the Haar and the Daub‐4 transforms preserve the classification patterns and preserve the privacy for real valued data.     

A Profit‐Maximizing Approach to Disposition Decisions for Product Returns*

Motivated by the asset recovery process at IBM, we analyze the optimal disposition decision for product returns in electronic products industries. Returns may be either remanufactured for reselling or dismantled for spare parts. Reselling a remanufactured unit typically yields higher unit margins. However, demand is uncertain. A common policy in many firms is to rank disposition alternatives by unit margins. We propose a profit‐maximization approach that considers demand uncertainty. We develop single period and multiperiod stochastic optimization models for the disposition problem. Analyzing these models, we show that the optimal allocation balances expected marginal profits across the disposition alternatives. A detailed numerical study reveals that our approach to the disposition problem outperforms the current practice of focusing exclusively on high‐margin options, and we identify conditions under which this improvement is the highest. In addition, we show that a simple myopic heuristic in the multiperiod problem performs well.     

Subnational Location Capital: The Role of Subnational Institutional Actors and Socio‐spatial Factors on Firm Location

Firms do not simply locate, but rather seek to accrue location‐based advantages such as knowledge, market insidership and resource utilization. Adopting the lens of social capital, this paper explores how subnational institutional actors facilitate location capital for firms. Using qualitative case study analysis of six multinational companies (MNCs), we highlight the important role of subnational institutional actors in fostering three dimensions of subnational location capital – structural, relational and cognitive. We show that subnational location capital, defined as the economic and social assets accessible through relationships within a subnational location, enable firms to derive advantages via subnational engagement. These findings contribute to the growing literature on the dynamic interaction of firms with subnational location, particularly the nuanced role of subnational institutional actors with MNCs.