Consumption Trade-off Research Articles

Noncompete Agreements and the Welfare of Consumers

Employee spin-offs harm incumbent firms by increasing competition (benefiting consumers) and preventing firm owners from making beneficial investments in workers who may later spin off (harming consumers). We model noncompete agreements (NCAs) as solutions for the firm and analyze the resulting trade-off for consumers. We show that market structure and the nature of investment play large roles. Counterintuitively, increased investment benefits have the potential to harm consumers such that industries where firms value NCAs the most are those where harm is greater. Finally, we draw two analogies between NCAs and antitrust and show how those areas inform NCA policy. (JEL D42, D43, J41, K21, L26, M13, M53)

The Goodness of Nesting Containers in Virtual Machines for Server Consolidation

Virtualization and server consolidation are the technologies that govern today’s data centers, allowing both efficient management at the functionality level as well as at the energy and performance levels. There are two main ways to virtualize either using virtual machines or containers. Both have a series of characteristics and applications, sometimes being not compatible with each other. Not to lose the advantages of each of them, there is a trend to load data centers by nesting containers in virtual machines. Although there are good experiences at a functional level, the performance and energy consumption trade-off of these solutions is not completely clear. Therefore, it is necessary to study how this new trend affects both energy consumption and performance. In this work, we present an experimental study aimed to investigate the behavior of nesting containers in virtual machines while executing CPU-intensive workloads. Our objective is to understand what performance and energy nesting configurations are equivalent or not. In this way, administrators will be able to manage their data centers more efficiently.

Quality–Quantity Tradeoffs in Consumption

Abstract Tradeoffs between quality and quantity are widespread in consumer decision-making. While existing research has focused on situational and contextual factors driving choices of higher-quality or higher-quantity purchases, in the current work, we find that consumers possess generalized preferences for quality or quantity across purchase categories. Some consumers systematically prefer quality over quantity, and others systematically prefer quantity over quality. In 32 studies (N = 24,404) that use correlational, experimental, and longitudinal designs, and proprietary data from the Federal Reserve Bank, the current research introduces quality–quantity preferences as a novel facet of consumer decision-making. Studies 1–3 demonstrate quality–quantity preferences as an individual difference, develop the “quality-quantity tradeoffs” scale to measure it, and demonstrate that it is different from related existing constructs. Studies 4A–5 show that consumers who prefer quantity over quality spend more money, borrow more, and accrue more debt, indicating that quality–quantity preferences are consequential. Taken together, our findings underscore the importance of quality–quantity preferences as a driver of consumer behavior and pave the way for future research investigating the causes and consequences of consumers’ dispositions toward quality or quantity.

The Electric Vehicle Routing and Overnight Charging Scheduling Problem on a Multigraph

In the electric vehicle (EV) routing and overnight charging scheduling problem, a fleet of EVs must serve the demand of a set of customers with time windows. The problem consists in finding a set of minimum cost routes and determining an overnight EV charging schedule that ensures the routes’ feasibility. Because (i) travel time and energy consumption are conflicting resources, (ii) the overnight charging operations take considerable time, and (iii) the charging infrastructure at the depot is limited, we model the problem on a multigraph where each arc between two vertices represents a path with a different resource consumption trade-off. To solve the problem, we design a branch-price-and-cut algorithm that implements state-of-the-art techniques, including the ng-path relaxation, subset-row inequalities, and a specialized labeling algorithm. We report computational results showing that the method solves to optimality instances with up to 50 customers. We also present experiments evaluating the benefits of modeling the problem on a multigraph rather than on the more classical 1-graph representation. History: Accepted by Andra Lodi, Area Editor for Design and Analysis of Algorithms—Discrete. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada through the Discovery grants [Grant RGPIN-2023-03791]. It was also partially funded by HEC Montréal through the research professorship on Clean Transportation Analytics. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0404 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0404 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

URLLC-aware and energy-efficient data offloading strategy in high-mobility vehicular mobile edge computing environments

The integration of Internet of Things (IoT) technologies into the vehicular industry has initiated a new era of connected and autonomous vehicles, revolutionizing transportation systems. However, this transformation introduces significant challenges, especially in 5 G networks, such as achieving Ultra-Reliable Low-Latency Communications (URLLC) and maintaining energy efficiency within the high mobility of vehicular environments. These are essential for supporting sustainable and environmentally friendly computing practices. To address these challenges, this paper introduces a URLLC-aware and energy-efficient data offloading strategy, utilizing the Asynchronous Advantage Actor-Critic (A3C) algorithm to navigate the complex dynamics of vehicular Mobile Edge Computing (MEC) environments. Our proposed method balances latency and energy consumption trade-offs while ensuring robust communication reliability. Technical evaluations reveal that our approach significantly outperforms other algorithms, achieving up to 8.2 % energy savings and a reduction of over 29 % in latency.

Common ownership and information exchange in a duopoly

I analyze effects of common ownership or cross-ownership on the incentives of Cournot competitors with close substitutes to exchange perfectly correlated information. Consistent with empirical findings, firms have stronger incentives to exchange information for greater degrees of common ownership. There is a trade-off for consumers. On the one hand, more common ownership relaxes competition, which hurts consumers. On the other hand, more common ownership may yield more information exchange, which benefits consumers. Consequently, positive degrees of common ownership may maximize the expected consumer (and total) surplus.

Exploring consumer preferences for cannabis edible products to support public health policy: A discrete choice experiment.

In October 2019, cannabis edibles were legalized for sale in Canada for non-medical use. This move was intended to improve public safety by regulating contents (including a maximum 10 mg tetrahydrocannabinol (THC) per package) and packaging to prevent accidental ingestion or over consumption. This study aimed to explore consumer preferences for cannabis edibles to inform cannabis policy. We explored the relative importance and trade-offs consumers make for attributes of cannabis edibles using a discrete choice experiment. Attributes included type of edible, price, THC content, cannabis taste, package information, product consistency, product recommendations, and Health Canada regulation. Participants lived in Canada, were 19 years of age or older, and purchased a cannabis edible in the last 12 months. A multinomial logit (MNL) model was used for the base model, and latent class analysis to assess preference sub-groups. This study was approved by the institutional ethics committee. Among 684 participants, the MNL model showed that potency was the most relevant attribute, followed by edible type. A two-group latent class model revealed two very distinct preference patterns. Preferences for group 1 (~65% of sample) were driven primarily by edible type, while for group 2 (~35% of sample) were driven almost entirely by THC potency. This study found that consumer preferences for ~65% of consumers of cannabis edibles are being met through regulated channels. The remaining ~35% are driven by THC potency at levels that are not currently available on the licensed market. Attracting this market segment will require reviewing the risks and benefits of restricting THC package content.

Aligning security and energy-efficiency using change detection and partial encryption for wireless camera networks

Wireless camera networks (WCNs) have seen widespread deployment in recent years, leading to an upsurge in monitoring and surveillance systems aimed at detecting foreign objects and changes within remote environments. However, wireless communication remains susceptible to cybersecurity threats, enabling eavesdroppers to access and manipulate confidential multimedia data effortlessly. Moreover, wireless cameras are low-power devices, encounter challenges as many state-of-the-art schemes adopt intricate security mechanisms, resulting in elevated costs, latency, and power consumption. This significantly compromises the stability of the node power and network lifetime and limits the applicability of security schemes in real-time implementations. This study addresses these issues by presenting DyimEnc, a dynamic image encryption scheme, to establish secure image transmission within wireless camera networks. The proposed scheme dynamically implements a security, complexity, and power consumption trade-off based on the currently available resources in each camera node. In the DyimEnc scheme, an effective in-node change-detection method is used to detect changes in the captured images and reduce the false transmission of images across the network. Additionally, a dynamic lightweight partial-encryption method is proposed to encrypt the edge maps of sensitive images that include changes. Moreover, a flexible key-management method is proposed for image encryption and transmission. Simulation results underscore DyimEnc's substantial impact, revealing significant reductions in power consumption, encryption cost, and transmission latency. It effectively achieves an average throughput of 254.9 KB/s and a remarkable 48 % decrease in network power consumption, showcasing a substantial 59 % improvement compared to a related study. Security analysis proved the robustness of our scheme in resisting attacks and satisfying the security requirements of WCNs.

A Low-Noise Low-Power 0.001Hz-1kHz Neural Recording System-on-Chip With Sample-Level Duty-Cycling.

Advances in brain-machine interfaces and wearable biomedical sensors for healthcare and human-computer interactions call for precision electrophysiology to resolve a variety of biopotential signals across the body that cover a wide range of frequencies, from the mHz-range electrogastrogram (EGG) to the kHz-range electroneurogram (ENG). Existing integrated wearable solutions for minimally invasive biopotential recordings are limited in detection range and accuracy due to trade-offs in bandwidth, noise, input impedance, and power consumption. This article presents a 16-channel wide-band ultra-low-noise neural recording system-on-chip (SoC) fabricated in 65nm CMOS for chronic use in mobile healthcare settings that spans a bandwidth of 0.001 Hz to 1 kHz through a featured sample-level duty-cycling (SLDC) mode. Each recording channel is implemented by a delta-sigma analog-to-digital converter (ADC) achieving 1.0 μ V rms input-referred noise over 1Hz-1kHz bandwidth with a Noise Efficiency Factor (NEF) of 2.93 in continuous operation mode. In SLDC mode, the power supply is duty-cycled while maintaining consistently low input-referred noise levels at ultra-low frequencies (1.1 μV rms over 0.001Hz-1Hz) and 435 M Ω input impedance. The functionalities of the proposed SoC are validated with two human electrophysiology applications: recording low-amplitude electroencephalogram (EEG) through electrodes fixated on the forehead to monitor brain waves, and ultra-slow-wave electrogastrogram (EGG) through electrodes fixated on the abdomen to monitor digestion.

Consumption of Sustainable Denim Products: The Contribution of Blockchain Certified Eco-Labels

Consumers’ growing interest in the environmental and social impacts of products has increased demand for sustainable fashion items, particularly denim. Emerging technologies such as blockchain technology and labeling certifications have been developed to address sustainability issues by improving supply chain transparency and efficiency. This research investigates the trade-offs consumers make when purchasing sustainable denim jeans and the impact of sociodemographic factors on their decision-making process. Employing a conjoint analysis approach, four attributes were examined: price, brand name, types of materials, and eco-labeling. The results indicated that price is still the most influential factor, followed by material, brand name, and eco-label. Although eco-labeling is of little importance to consumers, it offers valuable insights for effective communication of sustainable practices. Consumers prefer denim with a blockchain eco-label, followed by a fair-trade certificate. This research enhances the understanding of consumer behavior toward sustainable consumption and offers strategic insights for denim producers and marketers.

Examining attributes of retailers that influence where cannabis is purchased: a discrete choice experiment

BackgroundWith the legalization of cannabis in Canada, consumers are presented with numerous purchase options. Licensed retailers are limited by the Cannabis Act and provincial regulations with respect to offering sales, advertising, location, maximum quantities, and information sharing in an effort to protect public health and safety. The degree these policies influence consumer purchase behavior will help inform regulatory refinement.MethodsA discrete choice experiment within a cross-sectional online survey was used to explore trade-offs consumers make when deciding where to purchase cannabis. Attributes included availability of sales/discounts, proximity, product information, customer service, product variety, and provincial regulation. Participants ≥ 19 years old who lived in Canada and purchased cannabis in the previous 12 months were recruited through an online market research survey panel. A multinomial logit (MNL) model was used for the base model, and latent class analysis was used to assess preference sub-groups. Key limitations included ordering effect, hypothetical bias, and framing effect.ResultsThe survey was completed by 1626 people, and the base model showed that customer service carried the most weight in purchase decisions, followed by proximity and availability of sales and discounts. There was considerable heterogeneity in preference patterns, with a five-group latent class model demonstrating best fit. Only one group (15% of sample) placed a high value on the store being provincially regulated, while three groups were willing to make a trade-off with regulation to access better customer service, product information, or closer proximity. One group preferred non-regulated sources (24% of sample); this group was also primarily driven by the availability of sales and discounts. Three groups (60.5% of sample) preferred online stores.ConclusionThis study highlighted that there exists significant diversity with respect to the influence of consumer experiences on cannabis purchase behaviors. Modifications to cannabis retail regulations that focus on improving access to product information as well as reviewing limitations on sales and discounts could have the most impact for shifting customers to licensed retailers.

Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning

Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning

Battery Thermal Management System: A Review on Recent Progress, Challenges and Limitations

In electric vehicles (EVs), wearable electronics, and large-scale energy storage installations, Battery Thermal Management Systems (BTMS) are crucial to battery performance, efficiency, and lifespan. This comprehensive analysis covers the latest BTMS advances and provides an overview of current methods and technologies. Recent developments in passive and active thermal management are covered in the following sections. The simplicity and efficiency of passive solutions like phase change materials and thermal insulators are highlighted. Operational systems, such as liquid cooling, air cooling, and sophisticated refrigeration, are precise and adaptable. BTMS still faces several obstacles despite advances. Non-uniform battery pack temperature distribution, thermal runaway hazards, and BTMS integration in tight locations are discussed. The review also highlights material limits, energy consumption trade-offs, and scalability issues in present techniques. This review provides a comprehensive history of BTMS, identifying knowledge and technological gaps and suggesting battery technology research and development for academics, industry veterans, and newcomers.

An examination of the impact of the selected error criterion on the ACO performance in BLDC drives

Brushless direct current (BLDC) motor drives are becoming increasingly popular in motion control applications. As a result, a low-cost but effective BLDC motor speed controller is required. They are used in industrial and home appliances with conventional motor drive technology, such as robots, refrigerators and air conditioning systems. Brushless direct current (BLDC) drives are well known for their high efficiency and low maintenance. This work aims to design a non-conventional PID controller via Ant Colony Optimisers (ACO) in order to obtain an efficient speed performance for a BLDC motor "namely, Maxon EC flat". Moreover, when ACO is used for tuning of PID control, the objective function is required. Therefore, in this work, four different objective functions which are MSE, ISE, IAE, and ITAE are applied and their performance was compared in terms of motor' speed stability. In a simulation environment, the PID controller is used to control the speed of BLDC motors under three testing conditions which are Small, medium, and high inertia loads. The simulation results demonstrate that MSE offers the best performance under low and high inertial loading cases. Whereas, IAE has been superior compared with other criterions when medium loads are applied. Moreover, this paper shows a stability speed- power consumption trade-off problem. In other words, achieving a fast stability via any type of error indices doesn’t guarantee a low amount of consuming energy.

Grow the pie, or have it? Using machine learning to impact heterogeneity in the Ultra-poor graduation model

ABSTRACT The ‘Ultra-poor Graduation’ model, though highly effective in poverty alleviation, costs substantially more than alternative poverty alleviation approaches. One possible way of improving the cost-effectiveness of the model is to analyse the treatment effect heterogeneity and identify the participants who do not gain much from the programme and better customise the interventions to their needs. Applying recently developed machine learning methods on a large-scale RCT dataset from Bangladesh, we identify and characterise the program participants who benefit and who do not. We find significant variation in impact on assets where the top quintile gainers experience asset growth of 337% while asset growth is only 189% for the bottom quintile. Heterogeneity in impact on household expenditures is found to be present but of lower magnitude than that of assets. Importantly, the machine learning techniques we apply reveal contrasts in characteristics of beneficiaries who made the most in assets vs. consumption. The most benefitted households in per-capita wealth outcome were relatively older, were more dependent on wage income, had less involvement in self-employment activities, and had lower participation in household decision-making at baseline. In contrast, the top quintile gainers of household expenditure are younger, earn less from wages, depend more on self-employment income, and have higher participation in household decision-making. The results identify beneficiary characteristics that can be used in targeting households either to maximise impact on the desired dimension and/or to customise interventions for balancing the asset and consumption trade-off.

Field Experimental Evidence on the Effect of Pricing on Residential Electricity Conservation

This study examines how electric utilities and regulators can encourage residential consumers to conserve electricity during the hottest summer days and shift electricity load from the day to off-peak, nighttime hours. We analyze a two-year field experiment involving 280 Texas households that explores approaches to conservation and load-shifting to enable emission reductions and reduce generation costs. Our critical peak pricing intervention reduces electricity consumption by 14% on the peak hours of the hottest days, leading to greenhouse gas emission reductions of about 16%. A key contribution of this study is the use of high-frequency appliance-level data. We show that 74% of the critical peak response is from reducing air conditioning. In a complementary nighttime pilot program, consumers respond strongly to lower prices by programming the timing of electric vehicle charging. Our work highlights how automation can influence the consumer tradeoffs relating to effort costs, discomfort, monetary incentives, and warm glow. This paper was accepted by Rajesh Chandy, Special Section of Management Science on Business and Climate Change. Supplemental Material: The data and online appendices are available at https://doi.org/10.1287/mnsc.2020.02074 .

A trade-off strategy and correlation analysis for secrecy rate and power consumption in IRS-assisted cognitive radio networks

This paper presents a novel approach to consider trade-off in secrecy rate and power consumption in intelligent reflecting surface (IRS)-assisted cognitive radio networks (CRNs). In CRNs, the objective of enhancing secrecy rate (SR) conflicts with that of reducing total power consumption (TPC), creating a trade-off that should be flexibly considered due to dynamic environmental requirements. Whereas IRS act as an essential role that helps enhance performance and facilitate trade-offs, but does not change the fundamental conflict between these two objectives. Investigating the relationship between environmental parameters and both SR and TPC is crucial for achieving optimal performance. To address this issue, we propose a weighting-based trade-off (WBTO) algorithm that leverages a multi-objective optimization framework to optimize SR and TPC simultaneously. Specifically, we formulate a nonconvex problem with coupled variables and use convex approximation methods such as the penalty function method and biconvex function difference method to solve two subproblems iteratively. Simulation results demonstrate that the proposed algorithm can effectively adjust the optimization trend by changing the weighting factor, achieving up to nearly a 1-fold performance improvement compared to the SR maximization algorithm and nearly half reduction in time consumption compared to another baseline algorithm. Our approach enables flexible adjustment of the weighting factor to efficiently adapt to new metrics and achieve optimal trade-offs between SR and TPC.

AI on the street: Context-dependent responses to artificial intelligence

As artificial intelligence (AI) applications proliferate, their creators seemingly anticipate that users will make similar trade-offs between costs and benefits across various commercial and public applications, due to the technological similarity of the provided solutions. With a multimethod investigation, this study reveals instead that users develop idiosyncratic evaluations of benefits and costs depending on the context of AI implementation. In particular, the tensions that drive AI adoption depend on perceived personal costs and choice autonomy relative to the perceived (personal vs. societal) benefits. The tension between being served rather than exploited is lowest for public AI directed at infrastructure (cf. commercial AI), due to lower perceived costs. Surveillance AI evaluations are driven by fears beyond mere privacy breaches, which overcome the societal and safety benefits. Privacy-breaching applications are more acceptable when public entities implement them (cf. commercial). The authors provide guidelines for public policy and AI practitioners, based on how consumers trade off solutions that differ in their benefits, costs, data transparency, and privacy enhancements.

Identifying key factors that encourage vegetable intake by young adults: using the health belief model

PurposeAdequate vegetable consumption is fundamental to a healthy balanced diet; however, global compliance with recommendations is poor which is particularly important for young adults as they form food consumption habits. There is a growing interest in the circular economy of hospitality and sustainability of current dietary patterns in light of climate change and an expanding global population. The food value chain needs to be considered both vertically and horizontally where the research and development (R&D) investment is optimised by being “joined up” and not fragmentary; in addition, consumer trade-offs of health vs for example sensory appeal are taken into consideration. The purpose of this study was to identify factors predicting acceptance of vegetable dishes by young adults and present a roadmap that can be used for dish development and healthful marketing.Design/methodology/approachThis study used the health belief model (HBM) as framework to investigate key factors that encourage vegetable intake by young adults using an online questionnaire sample of 444 enrolled in undergraduate programs at universities in Brazil.FindingsStructural modelling showed that vegetable consumption frequency was positively influenced by Health concerns, Naturalness and Self-efficacy (including cooking skills), whereas Sensory factors and Familiarity demonstrated a negative loading that might be related to unpleasantness.Originality/valueGlobally, there is a strong need to promote the consumption of vegetables as a public health policy priority but also to ameliorate barriers to action that could be facilitated by availability, dish development and healthful marketing in hospitality operations.

Environmentalism in photovoltaics substitution, nationalism in photovoltaics rivalry and youth’s choice of solar power banks in Lanzhou

PurposeThis study aims to investigate how environmentalism in photovoltaic (PV) substitution and nationalism in PV rivalry with the USA are associated with the trade-offs made by young consumers in Lanzhou when selecting Chinese brand portable solar power banks.Design/methodology/approachIn this study, the choice-based conjoint survey was conducted to investigate mobile power bank consumers aged 18–28 in Lanzhou urban districts. A total of 2,004 valid questionnaires were collected and 1,813 sample was used in analyses. Logit and ordinary least squares regression models were run for empirical analyses.FindingsThe research results show that consumers tend to sacrifice certain levels of affordability for moderate technological capability, a reputable brand, better portability and advanced charging functions or sacrifice certain levels of technological capabilities for a moderate price. Consumers with stronger environmentalism in PV substitution tend to prioritize median price levels, larger battery capacity and better portability, while being less sensitive to brand and showing less preference for advanced charging functions. Consumers with stronger nationalism in PV rivalry tend to prioritize reasonably higher prices, bigger brands, enhanced portability, more solar panels and advanced charging functions.Practical implicationsThis research sheds light on consumer trade-offs between price, brand, portability, technological capability and charging function. It also explores how environmentalism and nationalism sentiments are associated with consumer decision-making. These insights carry valuable policy implications for fostering product innovation, supporting brand-building initiatives for small and medium-size enterprises, promoting market competition and preventing the weaponization of consumer nationalism.Originality/valueAs an emerging solar power product, the portable solar power bank holds significant potential for widespread adoption as a means to drive energy transition. Within the current context, two notable sentiments have surfaced: environmentalism, which pertains to the adoption of PV technology as a substitute for conventional energy sources and nationalism, which manifests in the PV rivalry between China and the USA. This research aims to investigate consumer preference related to this emerging product, specifically focusing on its relationship with these two sentiments.