Determinants of Organizational Flexibility: A Study in an Emerging Economy

This paper examines the processes of organizational adaptation and competitiveness of firms in an emerging economy. The study is set in the Argentinian context of the 1990s when a combination of economic and political change triggered a massive change in the competitive context of indigenous firms. Two highly flexible firms and two less‐flexible firms are studied from the pharmaceutical and edible oil industries and longitudinal data are supplied to explore the determinants of organizational flexibility in those organizations.     

The changing professional organization: A review of competing archetypes

The aim of this paper is to summarize literature relevant to the professional organization and to present a contemporary analysis of the archetype concept in this field. In order to understand recent and ongoing changes in professional organizations, the paper begins with a review of how the professional archetype evolved from the 1960s to 1990. Then, with examples from contemporary accounting, health care and law organizations, it considers the processes by which an institutionalized archetype can change. Forces for change – such as deregulation, competition, technology and globalization – can challenge the interpretive scheme and eventually delegitimize the existing archetype. At the same time, significant environmental changes can override isomorphic pressures and de‐institutionalize the long‐accepted structures. Thus we herald the emergence a new professional archetype – or perhaps several competing archetypes. Finally, the paper reviews the evolving field of professional organization as a whole, and understands the extant archetypes. A typology of professional organizations is proposed that currently seems to have three clusters of organizations – possibly three competing archetypes – namely, the traditional professional partnership, the specialized ‘Star’ form and the corporate global professional network, or GPN.     

Service Personnel,Technology, and Their Interaction in Influencing Customer Satisfaction*

Managing both the technologies and the personnel needed for providing high‐quality, multichannel customer support creates a complex and persistent operational challenge. Adding to this difficulty, it is still unclear how service personnel and these new communication technologies interact to influence the customer's perceptions of the service being provided. Motivated by both practical importance and inconsistent findings in the academic literature, this exploratory research examines the interaction of media richness, represented by three different technology contexts (telephone, e‐mail, and online chat), with six customer service representative (CSR) characteristics and their influences on customer satisfaction. Using a large‐sample customer survey data set, the article develops a multigroup structural equation model to analyze these interactions. Results suggest that CSR characteristics influence customer service satisfaction similarly across all three technology‐mediated contexts. Of the characteristics studied, service representatives contribute to customer satisfaction more when they exhibit the characteristics of thoroughness, knowledgeableness, and preparedness, regardless of the richness of the medium used. Surprisingly, while three other CSR characteristics studied (courtesy, professionalism, and attentiveness) are traditionally believed to be important in face‐to‐face encounters, they had no significant impact on customer satisfaction in the technology‐mediated contexts studied. Implications for both practitioners and researchers are drawn from the results and future research opportunities are discussed.     

Optimal Price and Quantity of Refurbished Products

Many retail product returns can be refurbished and resold, typically at a reduced price. The price set for the refurbished products affects the demands for both new and refurbished products, while the refurbishment and resale activities incur costs. To maximize profit, a manufacturer in a competitive market must carefully choose the proportion of returned products to refurbish and their sale price. We model the sale, return, refurbishment, and resale processes in an open queueing network and formulate a mathematical program to find the optimal price and proportion to refurbish. Examination of the optimality conditions reveals the different situations in which it is optimal to refurbish none, some, or all of the returned products. Refurbishing operations may increase profit or may be required to relieve a manufacturing capacity bottleneck. A numerical study identifies characteristics of the new product market and refurbished products that encourage refurbishing and some situations in which small changes in the refurbishing cost and quality provoke large changes in the optimal policy.     

通过谈判争取成功砝码

回想你上次面临新的领导机遇时,出现了什么情况？不管你是受命领导团队、部门还是整个企业,你很可能通过谈判争取该职位的薪水和待遇,比如：头衔、假期及奖金,但你是否争取过在该职位上取得成功的条件呢?     

Leveraging risk assessment for foodborne outbreak investigations: The Quantitative Risk Assessment-Epidemic Curve Prediction Model

Root cause analysis can be used in foodborne illness outbreak investigations to determine the underlying causes of an outbreak and to help identify actions that could be taken to prevent future outbreaks. We developed a new tool, the Quantitative Risk Assessment-Epidemic Curve Prediction Model (QRA-EC), to assist with these goals and applied it to a case study to investigate and illustrate the utility of leveraging quantitative risk assessment to provide unique insights for foodborne illness outbreak root cause analysis. We used a 2019 Salmonella outbreak linked to melons as a case study to demonstrate the utility of this model (Centers for Disease Control and Prevention [CDC], 2019). The model was used to evaluate the impact of various root cause hypotheses (representing different contamination sources and food safety system failures in the melon supply chain) on the predicted number and timeline of illnesses. The predicted number of illnesses varied by contamination source and was strongly impacted by the prevalence and level of Salmonella contamination on the surface/inside of whole melons and inside contamination niches on equipment surfaces. The timeline of illnesses was most strongly impacted by equipment sanitation efficacy for contamination niches. Evaluations of a wide range of scenarios representing various potential root causes enabled us to identify which hypotheses, were likely to result in an outbreak of similar size and illness timeline to the 2019 Salmonella melon outbreak. The QRA-EC framework can be adapted to accommodate any food–pathogen pairs to provide insights for foodborne outbreak investigations.     

Risk–risk governance in a low-carbon future: Exploring institutional,technological, and behavioral tradeoffs in climate geoengineering pathways

Deliberations are underway to utilize increasingly radical technological options to help address climate change and stabilize the climatic system. Collectively, these options are often referred to as “climate geoengineering.” Deployment of such options, however, can create wicked tradeoffs in governance and require adaptive forms of risk management. In this study, we utilize a large and novel set of qualitative expert interview data to more deeply and systematically explore the types of risk–risk tradeoffs that may emerge from the use of 20 different climate geoengineering options, 10 that focus on carbon dioxide or greenhouse gas removal, and 10 that focus on solar radiation management and reflecting sunlight. We specifically consider: What risks does the deployment of these options entail? What types of tradeoffs may emerge through their deployment? We apply a framework that clusters risk–risk tradeoffs into institutional and governance, technological and environmental, and behavioral and temporal dimensions. In doing so, we offer a more complete inventory of risk–risk tradeoffs than those currently available within the respective risk-assessment, energy-systems, and climate-change literatures, and we also point the way toward future research gaps concerning policy, deployment, and risk management.     

A probabilistic epidemiological model for infectious diseases: The case of COVID-19 at global-level

This study has developed a probabilistic epidemiological model a few weeks after the World Health Organization declared COVID-19 a pandemic (based on the little data available at that time). The aim was to assess relative risks for future scenarios and evaluate the effectiveness of different management actions for 1 year ahead. We quantified, categorized, and ranked the risks for scenarios such as business as usual, and moderate and strong mitigation. We estimated that, in the absence of interventions, COVID-19 would have a 100% risk of explosion (i.e., more than 25% infections in the world population) and 34% (2.6 billion) of the world population would have been infected until the end of simulation. We analyzed the suitability of model scenarios by comparing actual values against estimated values for the first 6 weeks of the simulation period. The results proved to be more suitable with a business-as-usual scenario in Asia and moderate mitigation in the other continents. If everything went on like this, we would have 55% risk of explosion and 22% (1.7 billion) of the world population would have been infected. Strong mitigation actions in all continents could reduce these numbers to, 7% and 3% (223 million), respectively. Although the results were based on the data available in March 2020, both the model and probabilistic approach proved to be practicable and could be a basis for risk assessment in future pandemic episodes with unknown virus, especially in the early stages, when data and literature are scarce.     

The wireless network jamming problem

In adversarial environments, disabling the communication capabilities of the enemy is a high priority. We introduce the problem of determining the optimal number and locations for a set of jamming devices in order to neutralize a wireless communication network. This problem is known as the wireless network jamming problem. We develop several mathematical programming formulations based on covering the communication nodes and limiting the connectivity index of the nodes. Two case studies are presented comparing the formulations with the addition of various percentile constraints. Finally, directions of further research are addressed.