Cost Of Effort Research Articles

Policy abstraction as a predictor of cognitive effort avoidance.

Consistent evidence has established that people avoid cognitively effortful tasks. However, the features that make a task cognitively effortful are still not well understood. Multiple hypotheses have been proposed regarding which task demands underlie cognitive effort costs, such as time-on-task, error likelihood, and the general engagement of cognitive control. In this study, we test the novel hypothesis that tasks requiring behavior according to higher degrees of policy abstraction are experienced as more effortful. Accordingly, policy abstraction, operationalized as the levels of contextual contingency required by task rules, drives task avoidance over and above the effects of task performance, such as time-on-task or error likelihood. To test this hypothesis, we combined two previously established cognitive control tasks that parametrically manipulated policy abstraction with the demand selection task procedure. The design of these tasks allowed us to test whether people avoided tasks with higher order policy abstraction while controlling for the contribution of factors such as time-on-task and expected error rate (ER). Consistent with our hypothesis, we observed that policy abstraction was the strongest predictor of cognitive effort choices, followed by ER. This was evident across both studies and in a within-subject cross-study analysis. These results establish at least one task feature independent of performance, which is predictive of task avoidance behavior. We interpret these results within an opportunity cost framework for understanding aversive experiences of cognitive effort while performing a task. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Sustainable strategies for green supply chain within the platform economy consider subsidies and marketing efforts.

As consumers' green awareness continues to grow, the level of a product's eco-friendliness and the quality of its marketing have become significant factors in shaping consumers' purchasing decisions. The power structures within the supply chain, as well as corresponding government subsidy policies, are also key elements influencing sustainable strategies for the green supply chain. In a green supply chain comprising one manufacturer and one e-commerce platform, two sales models exist within the online e-commerce platforms: reselling and agency selling. This paper establishes and analyzes three distinct Stackelberg game models, namely: manufacturer-led model without subsidy (bm), manufacturer-led model with subsidy (sm), and platform-led model with subsidy (sp). The results are shown as follows, with the rise of consumers' environmental awareness, more consumers opt for green products, inspiring the manufacturer to increase its optimal greenness and platform to enhance its optimal marketing efforts level. Notably, government subsidies provide a significant stimulus. An increase in the green technology cost coefficient leads to a decline in the manufacturer's profits across all three modes. Intriguingly, the manufacturer's profits are always highest in the sp mode. As the marketing efforts cost coefficient increases, the platform's profits decrease in the bm and sm modes. In contrast, in the sp mode, the platform's profits increase rather than a decrease. The choice of mode primarily depends on the platform's marketing efforts cost coefficient. When this coefficient exceeds a threshold, the platform chooses the sp mode. However, due to the relatively low marketing efficiency in this scenario, the manufacturer prefers the sm mode. For the government, the sp mode involves agency selling, serves as an effective mechanism to redistribute subsidies, thereby yielding the maximum social welfare benefits. Management insights are provided for the manufacturer and platform managers to make decisions about the degree of greenness and marketing efforts level, along with insights for governments to optimize subsidy policies.

Cost of effort in benchmarking: exploration and applications

The literature on incentives suggests that the cost of effort is a key determinant of production, as an agent’s utility is dependent on both consumption and the cost of effort. However, the benchmarking literature has neglected to consider the cost of effort in performance improvement, as it primarily focuses on selecting best-practice benchmarks for underperforming agents. This paper aims to bridge these two literatures by examining the cost of effort in benchmarking and its applications. Our approach is a direct extension of the rational inefficiency hypothesis. We use the information of slacks regarding technology to make inference about the cost of effort in benchmarking. Importantly, we show that inference about the cost of effort gives new sights into activity planning, incentive provision, and employee layoffs. Our analysis provides a new explanation for benchmarking failure in business practices. It also contributes to the rational inefficiency hypothesis by revealing that inefficiency can be beneficial in benchmarking since it can be regarded as a form of fringe reimbursement provided to stakeholders to offset the cost of effort.

Need for cognition moderates the relief of avoiding cognitive effort.

When making decisions, humans aim to maximize rewards while minimizing costs. The exertion of mental or physical effort has been proposed to be one those costs, translating into avoidance of behaviors carrying effort demands. This motivational framework also predicts that people should experience positive affect when anticipating demand that is subsequently avoided (i.e., a "relief effect"), but evidence for this prediction is scarce. Here, we follow up on a previous study [1] that provided some initial evidence that people more positively evaluated outcomes if it meant they could avoid performing an additional demanding task. However, the results from this study did not provide conclusive evidence that this effect was driven by effort avoidance. Here, we report two experiments that are able to do this. Participants performed a gambling task, and if they did not receive reward they would have to perform an orthogonal effort task. Prior to the gamble, a cue indicated whether this effort task would be easy or hard. We probed hedonic responses to the reward-related feedback, as well as after the subsequent effort task feedback. Participants reported lower hedonic responses for no-reward outcomes when high vs. low effort was anticipated (and later exerted). They also reported higher hedonic responses for reward outcomes when high vs. low effort was anticipated (and avoided). Importantly, this relief effect was smaller in participants with high need for cognition. These results suggest that avoidance of high effort tasks is rewarding, but that the size off this effect depends on the individual disposition to engage with and expend cognitive effort. They also raise the important question of whether this disposition alters the cost of effort per se, or rather offset this cost during cost-benefit analyses.

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 .

Extraction of stratigraphic exposures on visible images using a supervised machine learning technique

As volcanic stratigraphy provides important information about volcanic activities, such as the eruption style, duration, magnitude, and their time sequences, its observation and description are fundamental tasks for volcanologists. Since outcrops are often obscured in nature, the first task would be identifying stratigraphic exposures in many cases. This identification/selection process has depended on humans and has difficulties in terms of time and effort consumption and in biases resulting from expertise levels. To address this issue, we present an approach that utilizes supervised machine learning with fine-tuning and forms the backbone to automatically extract the areas of stratigraphic exposures in visible images of volcanic outcrops. This study aimed to develop an automated method for identifying exposed stratigraphy. This method will aid in planning subsequent field investigations and quickly outputting results. We used U-Net and LinkNet, convolutional neural network architectures developed for image segmentation. Our dataset comprised 75 terrestrial outcrop images and their corresponding images with manually masked stratigraphic exposure areas. Aiming to recognize stratigraphic exposures in various situations, the original images include unnecessary objects such as sky and vegetation. Then, we compared 27 models with varying network architectures, hyperparameters, and training techniques. The highest validation accuracy was obtained by the model trained using the U-Net, fine-tuning, and ResNet50 backbone. Some of our trained U-Net and LinkNet models successfully excluded the sky and had difficulties in excluding vegetation, artifacts, and talus. Further surveys of reasonable training settings and network structures for obtaining higher prediction fidelities in lower time and effort costs are necessary. In this study, we demonstrated the usability of image segmentation algorithms in the observation and description of geological outcrops, which are often challenging for non-experts. Such approaches can contribute to passing accumulated knowledge on to future generations. The autonomous detection of stratigraphic exposures could enhance the output from the vast collection of remote sensing images obtained not only on Earth but also on other planetary bodies, such as Mars.

Rent dissipation and streamlined costs: Laboratory experiments

The effect of group size and effort cost on rent dissipation in the standard Tullock rent seeking model is examined using a laboratory experiment. Data support qualitative comparative static Nash equilibrium predictions as reductions in both group size and effort cost increase effort levels. However, effort and dissipation rates are above Nash predictions for all treatments. In addition, while cutting per-unit effort costs in half increases individual effort levels, they do not fully double as predicted and therefore lower rent dissipation, creating a policy-relevant “streamlining effect”. A quantal response equilibrium, which adds stochastic elements into the game structure, predicts the observed higher effort levels and dissipation but is proven to be unable to explain the streamlining effect. A modification of quantal response that incorporates implementation error noise and loss aversion outperforms other estimated models and tracks the observed all data patterns more closely, including the streamlining effect.

AN APPROACH TO THE IMPLEMENTATION OF THE MULTIGRID METHOD WITH FULL APPROXIMATION FOR CFD PROBLEMS

This paper is devoted to the use of the multigrid method to accelerate calculations of compressible flows in a stationary formulation on unstructured grids. The multigrid method is used with the construction of a full approximation for each grid level (FAS MG—Full Approximation Scheme Multigrid). In the case of an unstructured grid, such a method can cause difficulties associated both with the construction of grid levels and transition operators between them, and with software implementation in the existing simulation code. The program needs to deal with several different discretizations at once. If the entire data structure, including arrays with grid data, topology, and time integration data, was designed to work on a single grid, then the implementation of the FAS MG can turn into a disaster involving rewriting the entire code. The purpose of this work is to achieve multiple acceleration of calculations at the cost of minimal effort. The problem of implementing the multigrid method on the basis of an existing software package that was not designed to work with several grid levels is solved. The implementation of the multigrid method in an MPI parallel code is carried out in such a way that there is no need to rewrite the program to work with multiple grids at all. Also, difficulties with constructing grid levels for an unstructured grid are avoided; agglomeration of cells is not used, and the number of faces per cell at coarse levels is not increased. In fact, this paper describes how to deploy a FAS MG accelerator in literally a week, even in code that is outdated from the point of view of software architecture.

Human-centred design on crowdsourcing annotation towards improving active learning model performance

Active learning in machine learning is an effective approach to reducing the cost of human efforts for generating labels. The iterative process of active learning involves a human annotation step, during which crowdsourcing could be leveraged. It is essential for organisations adopting the active learning method to obtain a high model performance. This study aims to identify effective crowdsourcing interaction designs to promote the quality of human annotations and therefore the natural language processing (NLP)-based machine learning model performance. Specifically, the study experimented with four human-centred design techniques: highlight, guidelines, validation and text amount. Based on different combinations of the four design elements, the study developed 15 different annotation interfaces and recruited crowd workers to annotate texts with these interfaces. Annotated data under different designs were used separately to iteratively train a machine learning model. The results show that the design techniques of highlight and guideline play an essential role in improving the quality of human labels and therefore the performance of active learning models, while the impact of validation and text amount on model performance can be either positive in some cases or negative in other cases. The ‘simple’ designs (i.e. D1, D2, D7 and D14) with a few design techniques contribute to the top performance of models. The results provide practical implications to inspire the design of a crowdsourcing labelling system used for active learning.

Effects of the green manufacturer’s distribution channel selection on green dilemma: Reselling, direct selling, or agency selling?

Due to higher effort cost required for green products, the e-retailer may choose the non-green manufacturer for purchasing products, thus creating a green dilemma. Therefore, this paper discusses how the green manufacturer’s distribution channel selection affects the e-retailer’s manufacturer selection and the green dilemma. Specifically, (1) in the reselling mode, this paper defines the input–output ratio of the green efforts to identify triggering conditions for the occurrence of the green dilemma. (2) When the input–output ratio or the marketing skills difference is relatively high, the green manufacturer is able to adopt direct selling and simultaneously avoid the green dilemma. Further, this paper identifies two types of effects derived from direct selling towards green dilemma. (3) If the green input–output ratio is a middle value, the agency mode rather than direct selling is more effective for the green manufacturer to gain the e-retailer’s cooperation.

Incentives from Career Concerns in a Contract Package: An Empirical Investigation

This paper empirically studies the extent to which career concerns as part of a typical contract offer influence employees’ work performance in a Japanese auto dealership firm. Because career movements and base-wage adjustments rely on performance evaluation over time, we develop a dynamic structural model that allows concerns for future payoffs to impact an employee’s current work effort. A reform in personnel-management policies of the firm during the data period enables us not only to compare the performances across individuals, but also to compare within an individual the performance before and after the reform—this enhances the model identification. Our estimation results show that the added value from career movements on top of the monetary payoffs is more important than the monetary payoffs. Individuals respond to career movements and commissions differently, mainly due to varying cost of effort and different payoffs from career movements. Our counterfactual exercises suggest that, compared with the scenario when there is only monetary compensation, adding career movements in a contract package will greatly improve the firm’s gross profit. The firm can improve its net profit by making commission and promotion more performance-based. This paper was accepted by Matt Shum, marketing. Funding: Financial support from the Social Sciences and Humanities Research Council of Canada [Grant 20190410-01] is gratefully acknowledged. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4956 .

Deep learning-based prediction of the remaining time and future distribution of pebble flow from real-scene images

Pebble flow dynamics is a crucial issue for designing and operating pebble bed reactors. The existing experimental or simulation methods are often associated with high time, resource, and effort costs. Therefore, image-based deep learning methods are explored to make real-time predictions of pebble flow dynamics directly from images. Real-scene images, captured by the high-speed camera during pebble flow experiments are used as the dataset. This paper proposes an RT-Net model based on Convolutional Neural Network (CNN) to predict the remaining time of pebble flow from experimental images. The core of the RT-Net model is the mergeable multi-branch convolutional component called ConvBlock, which effectively improves accuracy and reduces computational costs compared to traditional convolutional operators. Results show that this model is superior in both accuracy and efficiency metrics compared with typical convolutional neural networks (like AlexNet and VGGs). The proportion of test sets with prediction error within 0.05 s reaches 96.7 %, and the parameters count and inference time are 5.02 M and 1.99 ms respectively. Furthermore, to anticipate the pebble distribution at a given future time, a dual-input PreNet that combines CNN and Generative Adversarial Network (GAN) is designed, where the input includes the current pebble flow image and an arbitrarily chosen temporal displacement Δt. Targeted evaluation metrics, such as Target Similarity (TS) and Equivalent Target Similarity (TSeq) are proposed to quantitatively evaluate the model’s performance. Results indicate that the Pre-Net model can make quite satisfactory predictions of the future distribution of most data, while more efforts such as a task-specific loss function are encouraged to achieve better performance.

Responsible audit and consumer awareness under collusion risk

While sourcing from emerging economies can significantly reduce a buying firm’s operating costs, it may cause great financial or reputational damages due to supplier violations (e.g., child labor and environmental damage). In practice, multinational firms often employ third-party audits as an important approach to ensure their suppliers’ compliance prior to procurement. Unfortunately, in countries with lax law enforcement, collusion between suppliers and auditors often occurs, which helps suppliers pass audits with a lower compliance level, thus lowering consumers’ expectations of supply chain compliance. In this paper, we develop a game theoretic model to study a firm’s contracting strategy under supplier–auditor collusion risk when consumers have rational expectations. First, we show that the firm’s equilibrium contracting strategy takes three different forms depending on the collusion penalty. Interestingly, in contrast to the well-established results in the literature, we show that the firm may reduce the equilibrium compliance requirement. Second, we show that decreasing the supplier’s compliance effort cost, increasing their ethical level, or increasing consumers’ awareness of responsibility may lead to more rampant collusion, although all of them increase the supplier’s compliance level and the firm’s profit. In contrast, increasing the penalty always leads to a higher profit for the firm, a higher supplier’s compliance level, and lower collusion risk. Finally, we extend our model to examine the impacts of uncertainty about the auditor’s ethical level, bargaining power, and mandatory external standards.

A model and simulation study of developers’ multicontract incentives for contractors’ green technology innovation decisions considering marketing efforts and innovation capability

To promote low-carbon development of the construction industry and popularize green buildings (GBs), considering the influence of marketing efforts and innovation capability on GB market demand, this paper constructs multiple incentive decision models based on contract theory, analyzes developers’ decision-making behaviors for incentivizing contractors’ green technology innovation (GTI) under different incentive decision models; and explores the impacts of greenness preference and service quality preference on contract prices, sales prices, innovation levels and marketing effort levels. In addition, the impacts of marketing effort cost coefficient and innovation capability on innovation levels, marketing effort levels and developers’ profits are simulated through parameter assignment and computer software. The results show that greenness preference and service quality preference have positive impacts on developers’ and contractors’ decisions and profits; under the decision model based on innovation level, the developers maximize profits, but the developers’ marketing effort levels and contractors’ innovation levels are lower at this time; under the decision model based on cost-sharing, the contractors have the highest innovation levels and achieve optimal profits, while the innovation capability can promote innovative activities. The results of the study have guiding significance for enhancing the levels of GTI and promoting the development of GBs.

Information disclosure and pricing in the online expert service platform

The online expert service (OES) market is thriving by providing convenient professional services such as telemedicine consultation, legal advice, and financial planning. However, the practical phenomena, namely information asymmetry and the existence of naive consumers in the OES industry, motivate us to investigate how consumers’ two-dimensional heterogeneity and the expert’s agency pricing strategy with effort costs affect the OES platform’s information disclosure decision and corresponding market outcomes. By building a game-theoretic model, we consider the monopolistic OES platform in which the expert provides service to a mass of consumers, who can be either sophisticated or naive. The platform, as the designer of the disclosure framework, is thus an additional player in the seller-consumer game. Our results show that, when the marginal cost is intermediate, the platform strategically manipulates consumers’ valuation beliefs with partial disclosure to increase profitability without significant loss of market coverage. Furthermore, when naive consumers exist, the platform counter-intuitively discloses more information compared to that of all sophisticated consumers. Moreover, interestingly, when the marginal cost is low-to-intermediate, more naive consumers bring about more disclosure and thus obtain more demand for high-end sophisticated consumers, making both the expert and platform better off.

Predictors of short-term trajectories of students’ expectancy-value motivational beliefs in introductory calculus courses

In the present study, we examined the rate of change among college students’ (N = 429) competence beliefs, interest value, importance value, and four facets of cost measured weekly across a semester of two introductory calculus courses. We also examined how baseline levels of expectancy-value motivational beliefs predicted initial levels and rates of change in weekly expectancy-value beliefs. Results suggested that competence-related beliefs, interest value, and importance value decreased weekly throughout the semester, whereas outside effort and loss of valued alternatives cost increased. Additionally, baseline levels of each task-value-related construct predicted initial weekly levels of their respective constructs, but did not generally predict changes in task values over time. Conversely, baseline competence-related beliefs predicted slower declines in weekly competence-related beliefs, as well as slower declines in importance value and slower increases in outside effort cost, loss of valued alternatives cost, and emotional cost. Finally, baseline importance value predicted larger increases in weekly cost beliefs for all four dimensions of cost that were examined. Together, findings point to the importance of providing early course supports to buffer students against motivational challenges that threaten course participation and performance. Findings also suggest that students’ initial levels of competence-related beliefs are an important buffer against introductory course motivation declining across a semester, whereas initial importance value may have both positive and negative motivational consequences.

Optimizing the distribution of outcomes across multiple levels of subcontracting in construction projects

PurposeAlthough outcome sharing in construction is a well-established concept in the literature, there is still an ongoing debate on the most effective approach for distributing project outcomes between an owner and downstream contracting parties (DCPs). To address this issue, this paper aims to investigate an optimal framework for distributing project outcomes among various levels of subcontracting in construction projects. The framework includes contractors, subcontractors, sub-subcontractors and other related parties.Design/methodology/approachTo formulate the optimization problem, the principal–agent model is utilized. The theoretical development is validated through an experiment conducted with employees from road construction companies.FindingsWhen distributing outcomes among various levels of subcontracting, the sharing should be determined by their contribution to the outcome, effort costs, level of outcome uncertainty and risk preference.Originality/valueThis paper expands on the existing principal–agent theory by incorporating multiple levels of agents, transforming the conventional view of outcome sharing among downstream subcontracting levels into testable hypotheses and well-defined concepts. The paper has practical implications for industry practitioners seeking to effectively allocate benefits and costs throughout a project's subcontracting chain.

Influence of the physical effort of reminder-setting on strategic offloading of delayed intentions.

Intention offloading involves using external reminders such as diaries, to-do lists, and digital alerts to help us remember delayed intentions. Recent studies have provided evidence for various cognitive and metacognitive factors that guide intention offloading, but little research has investigated the physical cost of reminder-setting itself. Here, we present two pre-registered experiments investigating how the cost of physical effort associated with reminder-setting influences strategic intention offloading under different levels of memory load. At all memory loads, reminder-setting was reduced when it was more effortful. The ability to set reminders allowed participants to compensate for the influence of memory load on accuracy in the low-effort condition; this effect was attenuated in the high-effort condition. In addition, there was evidence that participants with less confidence in their memory abilities were more likely to set reminders. Contrary to prediction, physical effort had the greatest effect on reminder-setting at intermediate memory loads. We speculate that the physical costs of reminder-setting might have the greatest impact when participants are uncertain about their strategy choice. These results demonstrate the importance of physical effort as one of the factors relevant to cost-benefit decision-making about cognitive offloading strategies.

Research on Profit-Sharing Mechanism of IPD Projects Considering Multidimensional Fairness Preferences and BIM

The integration of building information modeling (BIM) and the integrated project delivery (IPD) mode effectively promotes collaboration among project members and enhances project profitability. However, the issue of profit sharing significantly impacts the successful implementation of IPD projects. To enhance the profit-sharing mechanism of IPD projects and ensure their smooth implementation, a game analysis model of profit sharing in IPD projects was established based on the Stackelberg game theory, taking into account the multidimensional fair preferences of the participants and the application of BIM technology. Through simulation, the impact of various parameters of participants on output utility, total revenue, and sharing coefficient in IPD projects was analyzed. The results show that: (1) participants achieve their highest output utility and total revenue under vertical–horizontal fairness preferences; (2) under vertical fairness preferences, the profit sharing coefficient is the highest, while the output utility and total revenue are the lowest; (3) although the output utility and total revenue of participants under horizontal fairness preferences exceed those under neutral fairness preferences, the profit-sharing coefficient is lower; (4) the output utility, the total revenue, and the profit-sharing coefficient of the participants all increase with the increase in effort utility value and decrease with the increase in the effort cost coefficient and the risk avoidance coefficient. The research findings provide valuable theoretical support for the profit sharing of IPD projects, thereby further promoting the advancement and implementation of the IPD model.

Situating cost perceptions: how general cost and motivational regulation predict specific momentary cost dimensions

Given the various motivational challenges that students experience when engaging in academic tasks, there is an emerging interest in investigating students’ cost perceptions and how to reduce them. However, most of the research has investigated general cost perceptions with a one-time assessment, often divorced from context in which cost is experienced. Centreing the situatedness of students’ motivational processes, we conducted an experience sampling study (57 undergraduates; 1,504 responses) to examine the link between general and in-situ momentary cost perceptions in students’ daily lives, as well as the potential moderating role of motivational regulation. Results showed that certain dimensions of momentary cost perceptions (outside effort cost and emotional cost) were positively associated with their corresponding general cost perceptions. Other dimensions of momentary cost (task effort cost and loss of valued alternatives cost) showed nonsignificant associations, suggesting higher sensitivity to context than others. Moreover, motivational regulation moderated the relationship between general and momentary cost for the majority of the dimensions, suggesting that interventions designed to improve students’ motivational regulation may reduce their momentary cost perceptions and increase the positivity of learning experiences.