Biogeographical Analysis of Chemical Co‐Occurrence Data to Identify Priorities for Mixtures Research

A challenge with multiple chemical risk assessment is the need to consider the joint behavior of chemicals in mixtures. To address this need, pharmacologists and toxicologists have developed methods over the years to evaluate and test chemical interaction. In practice, however, testing of chemical interaction more often comprises ad hoc binary combinations and rarely examines higher order combinations. One explanation for this practice is the belief that there are simply too many possible combinations of chemicals to consider. Indeed, under stochastic conditions the possible number of chemical combinations scales geometrically as the pool of chemicals increases. However, the occurrence of chemicals in the environment is determined by factors, economic in part, which favor some chemicals over others. We investigate methods from the field of biogeography, originally developed to study avian species co‐occurrence patterns, and adapt these approaches to examine chemical co‐occurrence. These methods were applied to a national survey of pesticide residues in 168 child care centers from across the country. Our findings show that pesticide co‐occurrence in the child care center was not random but highly structured, leading to the co‐occurrence of specific pesticide combinations. Thus, ecological studies of species co‐occurrence parallel the issue of chemical co‐occurrence at specific locations. Both are driven by processes that introduce structure in the pattern of co‐occurrence. We conclude that the biogeographical tools used to determine when this structure occurs in ecological studies are relevant to evaluations of pesticide mixtures for exposure and risk assessment.     

Phenomenology‐Based Ethnography for Management Studies and Organizational Analysis

This paper introduces phenomenology‐based ethnography as a novel ethnographic approach for research in management studies and organizational analysis and describes three methods that have been developed from this approach: life‐world analytical ethnography, focused ethnography and go‐along ethnography. Phenomenology‐based ethnography has emerged from developments in sociology that draw on ‘social phenomenology’ developed by Alfred Schütz. These developments involve the use of phenomenology‐based ethnographic methods that shift the focus of research onto participants’ subjective experiences of the field further than has been required by other ethnographic approaches. This paper uses a set of dimensions that allow a comparison of these phenomenology‐based methods’ aims, techniques of data collection and analysis, and required effort. These three methods are then compared with current ethnographic methods used in organizational research and management studies. The paper concludes with a discussion that explores and addresses the critique of how phenomenology‐based ethnography conceives the relationship between the researcher and the research subject.     

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.     

Staffing Call Centers with Uncertain Arrival Rates and Co‐sourcing

In a call center, staffing decisions must be made before the call arrival rate is known with certainty. Once the arrival rate becomes known, the call center may be over‐staffed, in which case staff are being paid to be idle, or under‐staffed, in which case many callers hang‐up in the face of long wait times. Firms that have chosen to keep their call center operations in‐house can mitigate this problem by co‐sourcing; that is, by sometimes outsourcing calls. Then, the required staffing N depends on how the firm chooses which calls to outsource in real time, after the arrival rate realizes and the call center operates as a M/M/N + M queue with an outsourcing option. Our objective is to find a joint policy for staffing and call outsourcing that minimizes the long‐run average cost of this two‐stage stochastic program when there is a linear staffing cost per unit time and linear costs associated with abandonments and outsourcing. We propose a policy that uses a square‐root safety staffing rule, and outsources calls in accordance with a threshold rule that characterizes when the system is “too crowded.” Analytically, we establish that our proposed policy is asymptotically optimal, as the mean arrival rate becomes large, when the level of uncertainty in the arrival rate is of the same order as the inherent system fluctuations in the number of waiting customers for a known arrival rate. Through an extensive numerical study, we establish that our policy is extremely robust. In particular, our policy performs remarkably well over a wide range of parameters, and far beyond where it is proved to be asymptotically optimal.     

Unions and Income Inequality: A Heterogeneous Panel Co‐integration and Causality Analysis

Although a large body of research has examined the effects of unions on the wage distribution, surprisingly little attention has been devoted to the effects of unions on the distribution of income. This paper examines the long‐run relationship between unionization and income inequality for a sample of 20 countries. Using heterogeneous panel co‐integration and causality techniques, we find that (i) unions have, on average, a negative long‐run effect on income inequality, (ii) there is considerable heterogeneity in the effects of unionization on income inequality across countries (in 40 per cent of the cases the effect is positive) and (iii) long‐run causality runs in both directions, suggesting that, on average, an increase in unionization reduces income inequality and that, in turn, higher inequality leads to lower unionization rates.     

Co‐Op Advertising in Dynamic Retail Oligopolies

We study a supply chain in which a consumer goods manufacturer sells its product through a retailer. The retailer undertakes promotional expenditures, such as advertising, to increase sales and to compete against other retailer(s). The manufacturer supports the retailer’s promotional expenditure through a cooperative advertising program by reimbursing a portion (called the subsidy rate) of the retailer’s promotional expenditure. To determine the subsidy rate, we formulate a Stackelberg differential game between the manufacturer and the retailer, and a Nash differential subgame between the retailer and the competing retailer(s). We derive the optimal feedback promotional expenditures of the retailers and the optimal feedback subsidy rate of the manufacturer, and show how they are influenced by market parameters. An important finding is that the manufacturer should support its retailer only when a subsidy threshold is crossed. The impact of competition on this threshold is nonmonotone. Specifically, the manufacturer offers more support when its retailer competes with one other retailer but its support starts decreasing with the presence of additional retailers. In the case where the manufacturer sells through all retailers, we show under certain assumptions that it should support only one dominant retailer. We also describe how we can incorporate retail price competition into the model.     

Facilitating Redundancy‐Oriented Management with Gene‐Therapy‐Oriented Management Against Disaster

This article tests the hypothesis that “if redundancy‐oriented management has negative aspects, then it could be facilitated by gene‐therapy‐oriented management.” Negative aspects include disadvantages, misjudgments, or miscalculations. The article provides a newly revised principle of disaster management by studying gene‐therapy‐oriented management. Based on qualitative analysis, redundancy‐oriented and gene‐therapy‐oriented management are analyzed via five variables: governments, business, volunteers, households, and the international community. The article is valuable because an analytical frame on gene‐therapy‐oriented management is systematically reconceptualized for the field of disaster management via three elements: unhealthy proteins (problems or failed measures), a vector (new or modified solutions), and target cells (positive outcomes). In accepting the hypothesis, the key tenet is that stakeholders have to assist the progress of redundancy‐oriented management with gene‐therapy‐oriented management by paying attention to the genes of each disaster.     

Managing Supply Risk for Vertically Differentiated Co‐Products

The manufacturing complexity of many high‐tech products results in a substantial variation in the quality of the units produced. After manufacturing, the units are classified into vertically differentiated products. These products are typically obtained in uncontrollable fractions, leading to mismatches between their demand and supply. We focus on product stockouts due to the supply–demand mismatches. Existing literature suggests that when faced with product stockouts, firms should satisfy all unmet demand of a low‐end product by downgrading excess units of a high‐end product (downward substitution). However, this policy may be suboptimal if it is likely that low‐end customers will substitute with a higher quality product and pay the higher price (upward substitution). In this study, we investigate whether and how much downward substitution firms should perform. We also investigate whether and how much low‐end inventory firms should withhold to strategically divert some low‐end demand to the high‐end product. We first establish the existence of regions of co‐production technology and willingness of customers to substitute upward where firms adopt different substitution/withholding strategies. Then, we develop a managerial framework to determine the optimal selling strategy during the life cycle of technology products as profit margins shrink, manufacturing technology improves, and more capacity becomes available. Consistent trends exist for exogenous and endogenous prices.     

Analysis and Management of Periodic Review,Order‐Up‐To Level Inventory Systems with Order Crossover

In this article, we investigate the (R, S) periodic review, order‐up‐to level inventory control system with stochastic demand and variable leadtimes. Variable leadtimes can lead to order crossover, in which some orders arrive out of sequence. Most theoretical studies of order‐up‐to inventory systems under variable leadtimes assume that crossovers do not occur and, in so doing, overestimate the standard deviation of the realized leadtime distribution and prescribe policies that can inflate inventory costs. We develop a new analytic model of the expected costs associated with this system, making use of a novel approximation of the realized (reduced) leadtime standard deviation resulting from order crossovers. Extensive experimentation through simulation shows that our model closely approximates the true expected cost and can be used to find values of R and S that provide an expected cost close to the minimum cost. Taking account of, as opposed to ignoring, crossovers leads, on average, to substantial improvements in accuracy and significant cost reductions. Our results are particularly useful for managers seeking to reduce inventory costs in supply chains with variable leadtimes.     

Bootstrap Determination of the Co‐Integration Rank in Vector Autoregressive Models

This paper discusses a consistent bootstrap implementation of the likelihood ratio (LR) co‐integration rank test and associated sequential rank determination procedure of Johansen (1996). The bootstrap samples are constructed using the restricted parameter estimates of the underlying vector autoregressive (VAR) model that obtain under the reduced rank null hypothesis. A full asymptotic theory is provided that shows that, unlike the bootstrap procedure in Swensen (2006) where a combination of unrestricted and restricted estimates from the VAR model is used, the resulting bootstrap data are I(1) and satisfy the null co‐integration rank, regardless of the true rank. This ensures that the bootstrap LR test is asymptotically correctly sized and that the probability that the bootstrap sequential procedure selects a rank smaller than the true rank converges to zero. Monte Carlo evidence suggests that our bootstrap procedures work very well in practice.     

The Emergence of Service Science: Toward Systematic Service Innovations to Accelerate Co‐Creation of Value

The current growth of the service sector in global economies is unparalleled in human history—by scale and speed of labor migration. Even large manufacturing firms are seeing dramatic shifts in percent revenue derived from services. The need for service innovations to fuel further economic growth and to raise the quality and productivity levels of services has never been greater. Services are moving to center stage in the global arena, especially knowledge‐intensive business services aimed at business performance transformation. One challenge to systematic service innovation is the interdisciplinary nature of service, integrating technology, business, social, and client (demand) innovations. This paper describes the emergence of service science, a new interdisciplinary area of study that aims to address the challenge of becoming more systematic about innovating in service.     

Channel Structure Design for Complementary Products under a Co‐Opetitive Environment

In the high‐tech industry, firms can be partners in one respect (e.g., resellers) and competitors in another. In this article, we investigate the channel structure problem for two firms‐each selling competing products in two complementary markets—who are deciding whether to sell their products to customers directly or distribute one of them through a competitor. The customers are heterogeneous and both firms have products that are horizontally differentiated. When selling products directly, the firm can coordinate the prices of the two complementary products and avoid the inefficiency of double marginalization. However, selling (indirectly) through the competing manufacturer can mitigate competition because the competitor shares the profit of both competing products and therefore does not price its own products aggressively. One might expect that when the externality across the markets is strong, firms would prefer to sell both products directly (rather than through the competitor) in order to take advantage of the complementarity between markets and eliminate the inefficiency of double marginalization. Interestingly, we find that even though the first mover chooses to sell both products directly, the second mover forsakes the opportunity to coordinate the prices of its products and instead opts to distribute one of the products through the first mover.     

Participatory Organizational Research: Examining Voice in the Co‐production of Knowledge

This paper advances participatory methods in management research. We propose the term participatory organizational research to describe this adjunct to action research. We illustrate the potential of the method to allow sometimes unheard organizational members to generate alternative perspectives that can offer the potential for the co‐production of new forms of knowledge that are locally relevant. Participatory methods originate from work with marginalized groups and have been used more commonly in community and organizational development. The aim of such research is, generally, to change the social and organizational conditions within which participants operate by using their perspectives as active participants to develop alternative possibilities. As such, this research method has significant potential for management researchers in providing the means for unheard organizational members to voice their perspectives: a central component, we argue, in knowledge co‐production. Based on a participatory study of care quality in elder care institutions, we examine in detail how participatory organizational research can enable voice and explore some of the structural limitations particularly in respect of research ethics.     

Bootstrap Testing of Hypotheses on Co‐Integration Relations in Vector Autoregressive Models

It is well known that the finite‐sample properties of tests of hypotheses on the co‐integrating vectors in vector autoregressive models can be quite poor, and that current solutions based on Bartlett‐type corrections or bootstrap based on unrestricted parameter estimators are unsatisfactory, in particular in those cases where also asymptotic χ² tests fail most severely. In this paper, we solve this inference problem by showing the novel result that a bootstrap test where the null hypothesis is imposed on the bootstrap sample is asymptotically valid. That is, not only does it have asymptotically correct size, but, in contrast to what is claimed in existing literature, it is consistent under the alternative. Compared to the theory for bootstrap tests on the co‐integration rank (Cavaliere, Rahbek, and Taylor, 2012), establishing the validity of the bootstrap in the framework of hypotheses on the co‐integrating vectors requires new theoretical developments, including the introduction of multivariate Ornstein–Uhlenbeck processes with random (reduced rank) drift parameters. Finally, as documented by Monte Carlo simulations, the bootstrap test outperforms existing methods.     

The Speed of Experience: The Co‐narrative Method in Experience Economy Education

This paper proposes a management learning technique called the co‐narrative method. This approach is seen as a useful means of capturing the subtler nuances of experience economy interactions, as well as learning ethics and corporate social responsibility, by nurturing empathy and compassion. A method is presented based on the example of the idea of slow as fast side of organizational and festival experiences, which is explored through autoethnographic studies of participation in experience economy events. It builds upon insights into improving management education through the use of the humanistic approach. The so‐called co‐narrative method is based on a syzygic mode uniting the two oppositions (while preserving their inherent contradictions). It encourages its users to exercise understanding of the experience of the Other, while teaching about concrete cases and events.     

公选面试巧用『印象管理』策略

公开选拔党政机关领导干部是近年来我国深化干部人事制度改革、建设高素质干部人才队伍的重要举措,在全国各地得到了深入、广泛的开展.在公选考试中经常可以看到这样的局面:有的参选者虽然基础较扎实,笔试成绩也遥遥领先,但由于面试准备不足,答辩中出现重大失误,以致痛失良机,名落孙山.     

Revenue Management with End‐of‐Period Discounts in the Presence of Customer Learning

Consider a firm that sells identical products over a series of selling periods (e.g., weekly all‐inclusive vacations at the same resort). To stimulate demand and enhance revenue, in some periods, the firm may choose to offer a part of its available inventory at a discount. As customers learn to expect such discounts, a fraction may wait rather than purchase at a regular price. A problem the firm faces is how to incorporate this waiting and learning into its revenue management decisions. To address this problem we summarize two types of learning behaviors and propose a general model that allows for both stochastic consumer demand and stochastic waiting. For the case with two customer classes, we develop a novel solution approach to the resulting dynamic program. We then examine two simplified models, where either the demand or the waiting behavior are deterministic, and present the solution in a closed form. We extend the model to incorporate three customer classes and discuss the effects of overselling the capacity and bumping customers. Through numerical simulations we study the value of offering end‐of‐period deals optimally and analyze how this value changes under different consumer behavior and demand scenarios.     

Bayesian Inventory Management with Potential Change‐Points in Demand

We consider the inventory management problem of a firm reacting to potential change points in demand, which we define as known epochs at which the demand distribution may (or may not) abruptly change. Motivating examples include global news events (e.g., the 9/11 terrorist attacks), local events (e.g., the opening of a nearby attraction), or internal events (e.g., a product redesign). In the periods following such a potential change point in demand, a manager is torn between using a possibly obsolete demand model estimated from a long data history and using a model estimated from a short, recent history. We formulate a Bayesian inventory problem just after a potential change point. We pursue heuristic policies coupled with cost lower bounds, including a new lower bounding approach to non‐perishable Bayesian inventory problems that relaxes the dependence between physical demand and demand signals and that can be applied for a broad set of belief and demand distributions. Our numerical studies reveal small gaps between the costs implied by our heuristic solutions and our lower bounds. We also provide analytical and numerical sensitivity results suggesting that a manager worried about downside profit risk should err on the side of underestimating demand at a potential change point.     

Dose‐Response Modeling with Summary Data from Developmental Toxicity Studies

Dose‐response analysis of binary developmental data (e.g., implant loss, fetal abnormalities) is best done using individual fetus data (identified to litter) or litter‐specific statistics such as number of offspring per litter and proportion abnormal. However, such data are not often available to risk assessors. Scientific articles usually present only dose‐group summaries for the number or average proportion abnormal and the total number of fetuses. Without litter‐specific data, it is not possible to estimate variances correctly (often characterized as a problem of overdispersion, intralitter correlation, or “litter effect”). However, it is possible to use group summary data when the design effect has been estimated for each dose group. Previous studies have demonstrated useful dose‐response and trend test analyses based on design effect estimates using litter‐specific data from the same study. This simplifies the analysis but does not help when litter‐specific data are unavailable. In the present study, we show that summary data on fetal malformations can be adjusted satisfactorily using estimates of the design effect based on historical data. When adjusted data are then analyzed with models designed for binomial responses, the resulting benchmark doses are similar to those obtained from analyzing litter‐level data with nested dichotomous models.     

Justifying Social Impact as a Form of Impression Management: Legitimacy Judgements of Social Enterprises’ Impact Accounts

This paper investigates how social enterprises construct accounts to gain legitimacy from the social impact generated by their products and operations. The paper finds that social impact accounts are framed to appeal to two distinct forms of judgement about legitimacy: cognitive and evaluative. Cognitive forms of judgement qualify how well an enterprise shares attributes with an individual's schemas of established actors or roles in society. Evaluative forms of judgement tend to operate more analytically to make comparisons of the relative appropriateness and desirability of multiple enterprises to achieve an audience's goals. The findings show that although legitimizing the social aspects of an enterprise involved justifications aimed at both forms of judgement, legitimizing an enterprise's professionalism relied almost exclusively on evaluative judgements. Moreover, the justifications created to appeal to evaluative judgements relied almost exclusively on financial and operational data, using operational scale as a proxy.