Entrepreneurial organizing activities and nascent venture performance

This research provides an improved understanding of how ventures successfully organize via resource allocations. Conceptually, we apply elements of action theory to account for resource trade-offs that occur as entrepreneurs make decisions about adding staff members to boundary spanning, technical core, and management functions. We then model how these allocation decisions differentially impact nascent venture performance. Empirically, we test our model with a sample of 2484 entrepreneurs captured in the Kauffman Firm Survey, a longitudinal dataset that tracks a random sample of US startups over an 8-year period. Results from dynamic panel estimation reveal evidence of both performance penalties and performance boosts as the result of entrepreneurs adding staff to specific areas, revealing optimality in specific configurations of entrepreneurial organizing elements.

Integrating Keyword Segmentation and Budget Allocation Decisions in Sponsored Search Advertising

Budget constrained sponsored search advertisers must decide how to allocate their advertisement budget across ad campaigns and individual keywords. In this paper, a simulation model that integrates the complex issues involved in keyword segmentation and campaign organization is used to evaluate performance of various budget allocation strategies. Using the buying funnel model as the basis for keyword segmentation and campaign organization, we analyze Volume-based, Cost-based, and Clicks-based budget allocation strategies and evaluate their performance implications for different firms. The simulation model is empirically evaluated using four Fortune 500 companies and their keyword data obtained from a leading provider of keyword research technology. The results and statistical analyses show significant improvements in budget utilization using the proposed allocation strategies over a Baseline commonly used in practice. The study offers useful insights into the budget allocation problem by leveraging a theoretical framework for keyword segmentation and campaign management.

Optimization of the impact measurement of market structure on liquidity and volatility

Liquidity and volatility are the two barometers that allow stock markets to appreciate in terms of attractiveness, profitability and efficiency. Several macroeconomic and microstructure variables condition the level of liquidity that directly impact the asset allocation decisions of different investor profiles − institutional and individuals − and therefore the dynamics of the market as a whole. Volatility is the regulatory component that provides information on the level of risk that characterizes the market. Thus, the appreciation of these two elements is of considerable help to fund managers looking to optimize their equity pockets. In this work, we will use the liquidity ratio as a proxy variable for the liquidity of the Moroccan stock market, to estimate the indicators and factors that determine its short- and long-term variability. The appropriate econometric method would be to estimate an error correction vector model (ECVM) which has the property of determining the long- and short-term relationships between the variables. The volatility of the MASI index will be the subject of a second estimate to capture the shape of the function of its evolution.

Spatial Decision Support System for Efficient School Location-Allocation

In Greece, a lack of a planning strategy was identified in the context of allocating students to schools. Particularly, the Secondary Educational Management of Lesvos Prefecture along with school Principals decide upon student allocation based on empirical knowledge and approximation techniques. As a consequence, during the school season of 2018-2019 capacity and proximity limitations were violated. This study introduces a Spatial Decision Support System (SDSS) to assist school location-allocation decisions in future seasons. The objective of the proposed SDSS is to minimize commute-to-school distance concerning capacity and proximity limitations. For this purpose, a capacitated P-median approach is adopted and formulated as a mixed-integer linear problem. The SDSS is further evaluated using actual data for students' transition from primary to secondary education in the city of Mytilene, Greece. Evaluation of current allocation practices carried out and further compared to those obtained by the SDSS. The results indicate a decrease of 8% in total distance whereas proximity and capacity constraints were respected accordingly. The results may be potentially useful for school planners to assist the allocation decisions in the city of Mytilene.

Resource Allocation with Sigmoidal Demands: Mobile Healthcare Units and Service Adoption

Problem definition: Achieving broad access to health services (a target within the sustainable development goals) requires reaching rural populations. Mobile healthcare units (MHUs) visit remote sites to offer health services to these populations. However, limited exposure, health literacy, and trust can lead to sigmoidal (S-shaped) adoption dynamics, presenting a difficult obstacle in allocating limited MHU resources. It is tempting to allocate resources in line with current demand, as seen in practice. However, to maximize access in the long term, this may be far from optimal, and insights into allocation decisions are limited. Academic/practical relevance: We present a formal model of the long-term allocation of MHU resources as the optimization of a sum of sigmoidal functions. We develop insights into optimal allocation decisions and propose pragmatic methods for estimating our model’s parameters from data available in practice. We demonstrate the potential of our approach by applying our methods to family planning MHUs in Uganda. Methodology: Nonlinear optimization of sigmoidal functions and machine learning, especially gradient boosting, are used. Results: Although the problem is NP-hard, we provide closed form solutions to particular cases of the model that elucidate insights into the optimal allocation. Operationalizable heuristic allocations, grounded in these insights, outperform allocations based on current demand. Our estimation approach, designed for interpretability, achieves better predictions than standard methods in the application. Managerial implications: Incorporating the future evolution of demand, driven by community interaction and saturation effects, is key to maximizing access with limited resources. Instead of proportionally assigning more visits to sites with high current demand, a group of sites should be prioritized. Optimal allocation among prioritized sites aims at equalizing demand at the end of the planning horizon. Therefore, more visits should generally be allocated to sites where the cumulative demand potential is higher and counterintuitively, often those where demand is currently lower.

Conservatism Gets Funded? A Field Experiment on the Role of Negative Information in Novel Project Evaluation

The evaluation and selection of novel projects lies at the heart of scientific and technological innovation, and yet there are persistent concerns about bias, such as conservatism. This paper investigates the role that the format of evaluation, specifically information sharing among expert evaluators, plays in generating conservative decisions. We executed two field experiments in two separate grant-funding opportunities at a leading research university, mobilizing 369 evaluators from seven universities to evaluate 97 projects, resulting in 761 proposal-evaluation pairs and more than $250,000 in awards. We exogenously varied the relative valence (positive and negative) of others’ scores and measured how exposures to higher and lower scores affect the focal evaluator’s propensity to change their initial score. We found causal evidence of a negativity bias, where evaluators lower their scores by more points after seeing scores more critical than their own rather than raise them after seeing more favorable scores. Qualitative coding of the evaluators’ justifications for score changes reveals that exposures to lower scores were associated with greater attention to uncovering weaknesses, whereas exposures to neutral or higher scores were associated with increased emphasis on nonevaluation criteria, such as confidence in one’s judgment. The greater power of negative information suggests that information sharing among expert evaluators can lead to more conservative allocation decisions that favor protecting against failure rather than maximizing success. This paper was accepted by Alfonso Gambardella, business strategy.

Ethical Guidance for Hard Decisions: A Critical Review of Early International COVID-19 ICU Triage Guidelines

This article provides a critical comparative analysis of the substantive and procedural values and ethical concepts articulated in guidelines for allocating scarce resources in the COVID-19 pandemic. We identified 21 local and national guidelines written in English, Spanish, German and French; applicable to specific and identifiable jurisdictions; and providing guidance to clinicians for decision making when allocating critical care resources during the COVID-19 pandemic. US guidelines were not included, as these had recently been reviewed elsewhere. Information was extracted from each guideline on: 1) the development process; 2) the presence and nature of ethical, medical and social criteria for allocating critical care resources; and 3) the membership of and decision-making procedure of any triage committees. Results of our analysis show the majority appealed primarily to consequentialist reasoning in making allocation decisions, tempered by a largely pluralistic approach to other substantive and procedural values and ethical concepts. Medical and social criteria included medical need, co-morbidities, prognosis, age, disability and other factors, with a focus on seemingly objective medical criteria. There was little or no guidance on how to reconcile competing criteria, and little attention to internal contradictions within individual guidelines. Our analysis reveals the challenges in developing sound ethical guidance for allocating scarce medical resources, highlighting problems in operationalising ethical concepts and principles, divergence between guidelines, unresolved contradictions within the same guideline, and use of naïve objectivism in employing widely used medical criteria for allocating ICU resources.

The Effect of Ballot Characteristics on the Likelihood of Voting Errors

In the United States, people are asked to vote on a myriad of candidates, offices, and ballot questions. The result is lengthy ballots that are time intensive and complicated to fill out. In this paper, we utilize a new analytical technique harnessing ballot scanner data from a statewide midterm election to estimate the effects of ballot complexity on voting errors. We find that increases in ballot length, increases in the number of local ballot questions, and increases in the number of candidates listed for single offices significantly increase the odds of encountering ballot marking and scanning errors. Our findings indicate that ballots’ characteristics can help election administrators make Election Day planning and resource allocation decisions that decrease ballot errors and associated wait times to vote while increasing the reliability of election results and voter confidence in the electoral process.