Trade-off Decisions Research Articles

Identifying research priorities through decision analysis: A case study for cover crops

IntroductionIn Midwestern maize (Zea-mays L.)-based systems, planting an over-wintering cover crop such as rye (Secale cereale L.) following fall harvests of summer crops maintains continuous soil cover, offering numerous environmental advantages. However, while adoption of cover crops has increased over the past decade, on a landscape-scale it remains low. Identifying where agronomic research could be most impactful in increasing adoption is therefore a useful exercise. Decision analysis (DA) is a tool for clarifying decision trade-offs, quantifying risk, and identifying optimal decisions. Several fields regularly utilize DA frameworks including the military, industrial engineering, business strategy, and economics, but it is not yet widely applied in agriculture.MethodsHere we apply DA to a maize-soybean [Glycine max (L.) Merr.] rotation using publicly available weather, management, and economic data from central Iowa.ResultsIn this region, planting a cover crop following maize (preceding soybean) poses less risk to the producer compared to planting following soybean, meaning it may be a more palatable entry point for producers. Furthermore, the risk of reduced maize yields when planting less than 14 days following rye termination substantially contributes to the overall risk cover crops pose to producers, but also has significant potential to be addressed through agronomic research.DiscussionIn addition to identifying research priorities, DA provided clarity to a complex problem, was performed using publicly available data, and by incorporating risk it better estimated true costs to the producer compared to using input costs alone. We believe DA is a valuable and underutilized tool in agronomy and could aid in increasing adoption of cover crops in the Midwest.

An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems

In today’s uncertain and competitive market, where manufacturing enterprises are subjected to increasingly shortened product lifecycles and frequent volume changes, reconfigurable manufacturing system (RMS) applications play significant roles in the success of the manufacturing industry. Despite the advantages offered by RMSs, achieving high efficiency constitutes a challenging task for stakeholders and decision makers when they face the trade-off decisions inherent in these complex systems. This study addresses work task and resource allocations to workstations together with buffer capacity allocation in an RMS. The aim is to simultaneously maximize throughput and to minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach supports decision makers with knowledge discovery to further understand RMS design. In particular, this study presents a customized SMO approach combined with a novel flexible pattern mining method for optimizing an RMS and conducts post-optimal analyses. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision support and production planning of an RMS.

In search of sustainable strategies for low-cost long-haul airlines

This paper explores whether some guiding principles exist to make the low-cost long-haul airline model work (LCLH). Few airlines using the LCLH model have survived, raising questions about the transferability of the low-cost model to long-haul operations. The article contributes by bringing together strategic thinking and various characteristics of past LCLH attempts. The findings suggest that LCLH airlines should seek cost advantages through optimum stage lengths by employing a hub strategy; to facilitate self-connections of relevant customer segments by reducing passengers' perceived connection risk; to operate efficient single-type aircraft on underserved routes at fares that stimulate the “Southwest Effect”; and to optimize productivity through flexible operations. Achieving and maintaining a fragile cost model like LCLH requires innovation, clear trade-off decisions to sustain fit, managed growth, and a competent workforce that cements the high fit needed.

Dynamic pricing and inventory control of online retail of fresh agricultural products with forward purchase behavior

In this paper we formulate and analyze a novel model on a retailer’s inventory and pricing decisions for fresh agricultural products with consumers’ forward consuming behavior under online channel. We consider a dynamic stochastic setting, where every period consists of two stages, discounting pricing stage and regular sale stage. At the beginning of the period, the retailer decides how much new fresh agricultural products to order and sets discount price for leftover inventories from the previous period which will be disposed otherwise, and determines regular price for fresh products on the second stage, respectively. Since forward purchase consumers may buy the products during discount pricing stage, which may cannibalize future sales at regular price, the retailer needs to make a trade-off decision between regular price and discounting price. We bring forward a dynamic optimization model and use nonlinear programming method of Karush Kuhn Tucker condition to obtain the optimal dynamic strategy, which is comparatively analyzed to dominate the related static strategy. We also show that consumers forward buying behavior will negatively influence the retailer’s profit. When the price is set too low in regular or discounting sales, the profit will show an up-down trend if the inventory exceeds a certain threshold. Meanwhile, when fresh goods returns are allowed and resold in the secondary stage, the retailer’s profit will increase. We finally conduct numerical studies to further characterize the optimal policy, and to evaluate the loss of efficiency under static policies when compared to the optimal dynamic policy.

Abstract P6-05-15: Standard of Care for HR+/HER2- Breast Cancer: Integrating The “Yale Model Shared Decision-Making Multidisciplinary Team Solution” into the Practice Setting

Abstract Background: Breast cancer patients are faced with treatment choices that can involve complex preference-sensitive decisions. The National Quality Forum initiated a “Call to Action” to integrate shared decision-making (SDM) processes into practice where clinicians and patients work together to make healthcare decisions that align with what matters most to patients. Projects In Knowledge, @Point of Care, Dartmouth and Yale collaborated to develop a pilot educational initiative to address and improve patient-centered care and SDM processes in the institutional cancer care setting. Methods: Training materials co-developed for the Yale Breast Cancer multidisciplinary team (N=11: oncologists, nurses/NPs, pharmacist) address SDM, CDK4/6 Inhibitor treatment of metastatic HR+ HER2- breast cancer, and clinician-patient role play methods implementing SDM in treatment discussions/decisions with patients. Reinforcement training, based on interim interview and case role play assessments, was customized to meet specific needs of the team. Qualitative semi-structured interviews and simulation case role play observational methods, using a two-rater system, were used to assess improved SDM performance. Baseline pre-intervention interviews and case role play assessments were compared to interim post-intervention and end of pilot (EOP) post-reinforcement training intervention interviews and case role play assessments (using a Likert scale 0-4 rating score: 0=0%; 1=25%, 2=50%; 3=75%; 4=100%). Following the training and assessments, a focus group of team members provided insights into the performance of the group, assessed the acceptability, feasibility, and repeatability of the program, and informed future education. Results: Semi-structured interview findings revealed that clinicians learned about nuances of CDK 4/6 inhibitors, crystallized their understanding of SDM through reinforcement training (customized in real time), and felt they were better able to implement SDM as a result of their case role play assessments. Training empowered the Yale Breast Cancer team to show pre- to post-education improvement in SDM case role play scenarios, ranging from 16% to 39%. Areas of greatest improvement: 1) determining decision style preference (+36%); 2) determining patients’ risk/burden tolerance (+32%); 3) determining patients’ activation, engagement, and self-efficacy (+34%), 4) determining trade-off decisions with patients (+39%), and 5) determining patients’ readiness to make a decision (+32%). Future research should explore how best to integrate SDM into the real world time restricted clinical practice. Conclusions: Educational training improved SDM skills for the multidisciplinary Yale Breast Cancer team, which can lead to improved clinician-patient decision-making and patient-centric care. The training process also facilitated team building and encouraged ongoing participation in SDM. Overall Yale Breast Cancer SDM Pilot Case Study Role-Play Assessments (Data reflect findings for 11 participants who completed their case role play) Citation Format: Elaine Rudell, Tarjani Agrawal, Patty Peterson, Michele Fallon Ingram, Brant Oliver, Kerin Adelson. Standard of Care for HR+/HER2- Breast Cancer: Integrating The “Yale Model Shared Decision-Making Multidisciplinary Team Solution” into the Practice Setting [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-05-15.

Understanding Land-Use Trade-off Decision Making Using the Analytical Hierarchy Process: Insights from Agricultural Land Managers in Zambia

Understanding factors that influence trade-offs between agricultural expansion and forest conservation is important in managing competing land-use objectives. This paper applies elements of the Analytical Hierarchy Process (AHP) to distinct farming business ownership models in Zambia to gain insights into factors that agricultural land managers take into account when considering land-use trade-off decisions which involve agricultural expansion into natural habitats. Results showed that the market domain was weighted above other domains, followed by the financial domain. When environmental considerations were pitted against other factors such as markets and finance, agricultural land managers were likely to trade off environmental concerns. Furthermore, availability of input subsidies via the agricultural Food Input Support Programme (FISP) influenced the decision to expand, particularly for the small-scale ownership model. This suggests that agricultural policies and strategies aimed at promoting agricultural productivity may require accompanying measures to safeguard forest ecosystems from agricultural expansion. Key differences in the factors influencing expansion decisions were evident among ownership models suggesting that ownership types do have an impact on factors considered. This highlights the need to advance tailored strategies that address differences in priorities and decision making emanating from variations in farming business ownership models.

Designing a multi-objective closed-loop supply chain for multi-period multi-generational products with social impacts considerations

This paper assesses a multi-period closed-loop supply chain (CLSC) for making strategic and operational decisions in multi-generation applications. For this purpose, joint decisions on network design, inventory control, production planning, pricing, supplier selection, and service level requirements are determined along with the forward flows of the supply chain. Besides, a segmentation policy is proposed for disassembling and recovering the different quality grades of returned products. All the raised issues have become more challengeable when an inner competition on coexistence goes up between the incumbent and new generations. Accordingly, this paper attempts to bridge the gaps in the literature by proposing a bi-objective mathematical model that involves a demand function with the integration of price drop effects, marketing efforts, and technology desirability to tackle trade-offs decisions between multiple generations. This research aims to simultaneously optimize the total profit and social benefits. To solve the problem, first, the ε-constraint method has been applied to transform the bi-objective problem into a single objective. Then, as a great deal to solve the complex mixed-integer non-linear program model, an outer approximation algorithm is applied based on approximation, relaxation, and decomposition. Finally, a case example in a mobile phone CLSC problem has been investigated to validate the proposed model and provide managerial insights.

Stability of Stated Preferences: Vaccine Priority Setting before and during the First COVID-19 Lockdown.

Discrete choice experiments (DCEs) are frequently used to study preferences and quantify tradeoffs in decision making. It is important to understand how stable their results are. To investigate to what extent an extreme change in context, the COVID-19 pandemic, affected preferences for vaccine priority setting, as observed in an earlier DCE. We replicated a DCE in which participants had to prioritize vaccination programs for public funding. The initial DCE was executed in Flanders (Belgium) right before the onset of the SARS-CoV-2 pandemic (December 2019, N = 1,636). The replicated DCE was executed 6 months later when the population was in lockdown (April 2020, N = 1,127). A total of 612 respondents participated in both waves of the DCE. We used panel mixed logit models to quantify attribute and level importance and compared utility estimates for consistency. The number of vaccine-preventable deaths became less important during the pandemic than before, whereas the influential attributes, the vaccine's contribution to disease eradication and certainty about vaccine effectiveness became even more important. Respondents attached equal importance to the number of patients with transient or permanent morbidity, to the disease's economic impact as well as to its equity profile. Different preferences for vaccine priority setting were observed during the first COVID-19 lockdown as compared with before, although these differences were, given the extreme nature of the changing circumstances, relatively small. We replicated a discrete choice experiment (DCE) about vaccine priority setting during the first COVID-19 lockdown and compared results with those from the original setting.The major attributes, contribution to disease eradication, and scientific certainty about vaccine effectiveness became even more important than they already were, whereas avoidable mortality became less important.Respondents attached equal importance to the number of patients with transient or permanent morbidity, to the disease's economic impact as well as to its equity profile.An extreme change in directly related context to the choice assignment led to changes in stated preferences, although these changes were relatively small, given the extreme change in context.Priorities in the second DCE were even less aligned with cost-effectiveness analysis than those observed initially.

Speed-accuracy tradeoffs in decision making: Perception shifts and goal activation bias decision thresholds.

A fundamental aspect of decision making is the speed-accuracy tradeoff (SAT): slower decisions tend to be more accurate, but because time is a scarce resource people prefer to conclude decisions more quickly. The current research adds to the SAT literature by documenting two previously unrecognized influences on the SAT: perception shifts and goal activation. Decision makers' perceptions of what constitutes a fast or a slow decision, and what constitutes an accurate or inaccurate decision, are based on prior experience, and these perceptions influence decision speed. Similarly, previous experience in a decision context associates the context with a particular decision goal. Thus, in later decisions the decision context will activate this goal, and influence decision speed. Both of these mechanisms contribute to a specific decision bias: decision speeds are biased toward original decision speeds in a decision context. Four experiments provide evidence for the bias and the two contributing mechanisms. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

COVID-19 and sustainable development goals: A bibliometric analysis and SWOT analysis in Malaysian context

The COVID-19 crisis has been a core threat to the lives of billions of individuals over the world. The COVID-19 crisis has influenced governments’ aims to meet UN Sustainable Development Goals (SDGs); leading to exceptional conditions of fragility, poverty, job loss, and hunger all over the world. This study aims to investigate the current studies that concentrate on the COVID-19 crisis and its implications on SDGs using a bibliometric analysis approach. The study also deployed the Strengths, Weaknesses, Opportunities, and Threats (SWOT) approach to perform a systematic analysis of the SDGs, with an emphasis on the COVID-19 crisis impact on Malaysia. The results of the study indicated the unprecedented obstacles faced by countries to meet the UN's SDGs in terms of implementation, coordination, trade-off decisions, and regional issues. The study also stressed the impact of COVID-19 on the implementation of the SDGs focusing on the income, education, and health aspects. The outcomes highlighted the emerging opportunities of the crisis that include an improvement in the health sector, the adoption of online modes in education, the swift digital transformation, and the global focus on environmental issues. Our study demonstrated that, in the post-crisis time, the ratio of citizens in poverty could grow up more than the current national stated values. We stressed the need to design an international agreement to reconsider the implementation of SDGs, among which, are strategic schemes to identify vital and appropriate policies.

Between benefit and risk: how entrepreneurs evaluate corporate investors

Start-up growth is inevitably dependent on the provision of external resources. Yet, even though corporate venture capital could be an attractive funding source as it provides financial as well as crucial additional resources, corporate venture capitalists (CVCs) are seen as a two-sided sword by entrepreneurs. We, therefore, investigate entrepreneurs’ consideration of potential CVC investors and conceptualize a model of their willingness to approach a CVC investor. Using a conjoint experiment with 1680 investor profiles evaluated by 105 entrepreneurs, we show that entrepreneurs consider the investor’s motivation, deal experience, access to firm-specific resources, and long-term financial commitment of funds. However, entrepreneurs’ evaluation differs depending on their need for specific resources, as well as their fundraising experience. We thereby highlight entrepreneurs’ anticipatory trade-off decisions in the light of resource dependence and help CVC managers to optimize their communication and management efforts to attract the most suitable portfolio companies.

Reconsidering prosocial behavior as intersocial: A literature review and a new perspective

AbstractResearch questions in the prosocial behavior literature focus on the pro aspect of prosocial behavior—that is, how to motivate actions that benefit others. These questions typically employ simplified decision contexts that neglect the intersocial aspect of prosocial behavior—that is, people are embedded in social networks and impacted by interactivity among two or more persons, entities, or societies. These intersocial influences have increased with technology access. Consumers now face richer choice tradeoffs, can access more information on causes, observe others' actions, and choose to make their own choices public. To ask questions that address the nature of prosocial behavior itself rather than consider it merely as another decision context to motivate human behavior in, we call for researchers to conceptualize prosocial behavior as intersocial. This approach can help capture the more realistic decision tradeoffs consumers face, as well as illuminate new research opportunities arising from considering technology‐enabled giving and socially hyperconnected consumers.

Prediction of Cognitive Radio Networks (CNRs) Using COPRAS

"Cognitive Radio (CR) is a wireless form of communication in which a transceiver can intellectually discover which communication channels are in use and which are not. It avoids occupied channels and moves immediately to empty channels without disrupting licensed users. Cognitive sensor networks are capable of intelligently and automatically sensing localized environmental information through the deployment of multiple sensors. Fuzzy COPRAS, using the alternative channel decision, cognitive radio, data trade-off, and multi-criteria decision making (MCTM), is used to evaluate bandwidth, duty cycle, economic cost, and channel termination. The acquisition of data is based on the deployment of multiple sensors, and the result is based on bandwidth and economic cost. The results indicate that economic cost received the lowest rank, while the decision based on bandwidth and economic cost received the top ranking. The COPRAS method is a complex proportionality rating system that was introduced in 1994 by Zavatskas, Kaklauskas, et al. The index increment and decrement effect of attributes is considered separately in the result evaluation. Software-defined radio is the heart of a cognitive radio, and applications that distinguish cognitive radio from software-defined radio require additional hardware in the form of sensors and actuators. This enables more cognitive radio applications, including emergency networks and WLAN high-performance and transfer in spectrum-sensitive cognitive radio, which includes distance extensions. In the alternative decision-making method (MCTM), bandwidth, duty cycle, economic cost, and channel termination are evaluated."

Increasing Biomanufacturing Yield with Bleed–Feed: Optimal Policies and Insights

Problem definition: Bleed–feed is a novel technology that allows biomanufacturers to skip intermediary bioreactor setups. However, the specific time at which bleed–feed is performed is critical for success. The process is stringently regulated, and its implementation involves unique tradeoffs in operational decision making. Our analysis formalizes the operational challenges related to bleed–feed decisions, and our results inform biomanufacturers and policymakers on the potential impact of this technology on current practice. Academic/practical relevance: Operations management (OM) methodologies have not yet been widely adopted in the biomanufacturing industry. This research presents one of the first attempts to demonstrate how OM can complement biomanufacturing to improve operational decisions. We present a practically relevant problem and a rigorous solution approach that is relevant to both OM and biomanufacturing. Methodology: We develop a finite-horizon, discrete-time Markov decision processes model and analyze the structural characteristics of optimal bleed–feed policies. Moreover, we characterize the behavior of the value function as a function of regulatory restrictions. As a salient feature, the MDP model captures both the biological dynamics of fermentation and the operational tradeoffs in biomanufacturing. Results: We show that optimal bleed–feed policies have a three-way control-limit structure under mild conditions that were validated with industry data. Moreover, our analysis reveals that the marginal benefits of bleed–feed diminish as additional bleed–feeds are performed. As a practically relevant benchmark, we consider a risk-averse heuristic and identify sufficient conditions for its optimality. Managerial implications: Our analysis (supported with an industry case study) shows that bleed–feed implementation can provide benefits. Real-world implementation at MSD resulted in an 82.5% improvement in the batch yield per setup (using one bleed–feed). We find that low-risk fermentation systems benefit the most from bleed–feed implementation. We also find that the performance gap between optimal policies and the risk-averse heuristic is higher when the failure risks or the critical biomass levels are lower. Funding: This work was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO-VENI scheme. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1163 .

Treatment Preference for Alzheimer's Disease: A Multicriteria Decision Analysis with Caregivers, Neurologists, and Payors.

Alzheimer's disease (AD) is a chronic neurodegenerative disorder associated with a high burden of illness. New therapies under development include agents that target amyloid-beta (Aβ), a key component in AD pathogenesis. Understanding the decision-making process for new AD drugs would help determine if such therapies should be adopted by society. Multicriteria decision analysis (MCDA) was applied to three key stakeholder groups to assess treatment alternatives for AD based on a multitude of decision trade-offs covering main components of care. AD caregivers (n = 117), neurologists (n = 90), and payors (n = 90) from the USA received an online survey. The decision problem was broken down into four decision criterion and 12 subcriteria for two treatment scenarios: an Aβ-targeted therapy vs. the standard of care (SOC). Respondents were asked to indicate how much they preferred one option over another on a scale from 1 (equal preference) to 9 (high preference) based on each criterion and subcriterion. The decision criteria and subcriteria were weighted and presented as partial utility scores (pUS), with higher scores suggesting an increased preference for that decision-making component. Caregivers and payors applied the highest value to need for intervention (mean pUS = 0.303 and 0.259) and clinical outcomes (mean pUS = 0.286 and 0.377). In contrast, neurologists placed the highest value on clinical outcomes and types of benefits (mean pUS = 0.436 and 0.248). When decision subcriteria were examined, efficacy (mean pUS = 0.115, 0.219, and 0.166) and the type of patient benefits (mean pUS = 0.135, 0.178, and 0.126) were among the most valued by caregivers, neurologists, and payors. All groups placed the highest value on drug efficacy and types of benefit derived by patients. In contrast, cost implications were among the least important aspects in their decision-making.

Multi-Objective Optimisation of Urban Form: A Framework for Selecting the Optimal Solution

The complexity associated with the design of urban tissues is driven by the multitude of design goals that influence urban development and growth. This complexity is amplified by the design goals being inherently conflicting, necessitating preference-based decisions within the design process—an approach that results in predetermined design solutions driven by personal biases. The utility of population-based optimisation algorithms addresses this by allowing for the examination of multiple conflicting objectives within the same design problem, negating the need for trade-off decisions between the design goals. The application of these algorithms is associated with three primary steps. The first is the formulation of the design problem, the second is the application of the algorithm, and the third is selecting the most optimal solution from the algorithm’s output. This paper examines the third step in this process, in which various methods are employed to facilitate data-driven selection mechanisms that are both objective as well as subjective in their formulation. The selection mechanisms are demonstrated on a speculative urban tissue that examines the potential of inhabiting interstitial spaces, through various morphological interventions, within the urban fabric. The results present a scalable and adaptable framework that assists designers employing multi-objective evolutionary algorithms (MOEAs) to select the optimal solution from their generated populations, a challenge commonly associated with the application of MOEAs in design.

Adaptive Decision Method in C3I System.

As an important system in the national defense and military information construction, the command, control, communication, and intelligence (C3I) system urgently needs to establish an adaptive process to deal with the dynamic operating environment and changeable task requirements to ensure the long-term effective and stable operation of the system. As an important part of this process, the adaptive decision method should have the ability of online trade-off decision. Therefore, this paper presents an adaptive decision method based on parallel computing and optimization theory. This method combines operational requirements and commander preference to achieve the parallel adaptive decision solution. The experimental results show that the presented decision method can generate online trade-off strategies to deal with typical command and control scenarios of damage replacement in a simulated environment, effectively guide the system to carry out adjustment behavior, and achieve the goal of dynamic response to environmental changes and task changes.

Trade-off decisions in a novel deep reinforcement learning for energy savings in HVAC systems

This paper presents Model-based Reinforcement Learning (MB-RL) techniques to control the indoor air temperature, and CO2 concentration level, and minimize the energy consumption of the heating, ventilating, and air conditioning (HVAC) systems, simultaneously. For this purpose, a trade-off is made between maintaining indoor comfort levels and minimizing energy consumption. The control of the HVAC system is performed using the Deterministic Policy RL (DP-RL) method. Moreover, the nonlinear autoregressive exogenous neural network (NARX-NN) is employed as an approximation function with DP-RL method to provide a hybrid DP-NARX-RL controller. By applying the DP-RL and DP-NARX-RL controllers to the HVAC system of a typical building, parameters such as the indoor comfort levels, the electrical power, and energy consumed, and the energy costs at various pricing schemes are evaluated for two case studies. In both cases, the results show the better performance of DP-NARX-RL compared to DP-RL, RL, and PID controllers.

Let’s Get Personal: How CEO Personality Effects Organizational Innovation via Corporate Venturing

We develop theory to explain the relationship between CEO personality and inter- and intraorganizational learning mechanisms with a focus on corporate venture capital (CVC). We provide an integrated perspective and consider CVC investments as individual innovation practice and relative to research and development (R&D) efforts. Using a novel linguistic tool to measure the personality traits of 1,360 CEOs of S&P 500 firms, we find that CEO consciousness and CEO neuroticism are negatively, and CEO openness positively associated with CVC engagement. Regarding the trade-off decision between inter- and intraorganizational learning mechanisms, we find that CEO consciousness and CEO openness each have a positive effect on CVC relative to R&D. Our results extend upper echelons research by not only highlighting the importance of CEO personality in driving organizational innovation, but also indicating how CEO personality either enhances or inhibits interorganizational learning in both absolute and relative terms.

Who Contributes to Successful Urban Governance?

In three subsequent studies, we identified stakeholders that are considered influential in urban governance processes and uncovered certain stakeholders’ characteristics (‘attributes’) that are regarded as more assertive in trade-off situations. We chose our research setting in Vienna, Austria, given its consistently high position in various livability rankings. Our first study consisted of a large representative survey (n= 1,787) and probed for citizens’ opinions on the stakeholders that are influential to make Vienna a livable city. To narrow our research focus, we examined in our second study thirty-nine conflicts between five stakeholder types. Using an abductive media analysis, we find certain stakeholder characteristics reemerge and win over dilemma situations. In line with stakeholder theory, we find that respondents in our third study, five choice-based conjoint experiments (n = 1,354), show a sizeable preference for stakeholders with attributes of power, legitimacy, and urgency. Our research contributes to the theoretical understanding of urban governance structures and stakeholder theory, and provides insights on the role of different stakeholders and their trade-off decisions.