Maximization Strategy Research Articles

An Online Unsupervised Streaming Features Selection Through Dynamic Feature Clustering

Streaming feature selection (SFS) is emerging as a key research direction which addresses the non-stationary property of feature streams when the sample size is fixed. Most existing SFS techniques are supervised methods, and ignore the label scarcity. Real-world datasets are typically unlabeled and the labeling costs are expensive. Although some unsupervised SFS approaches are proposed, these approaches are either limited to the homogeneous feature types or require substantial computational complexity. To address these problems, we propose an online unsupervised feature selection framework using dynamic feature clustering in this paper. We derived a recursive density lower bound to estimate the density distribution of feature streams and developed a density-based dynamic clustering method to perform the online feature stream clustering for exploring feature redundancy. An unsupervised online feature relevance maximization and redundancy minimization strategy is introduced to extract a subset of important features with low redundancy from the feature stream. Experimental results on thirteen well-known benchmark datasets and comparison studies with seven state-of-the-art supervised SFS methods demonstrate that the proposed unsupervised method provides statistically comparable performance with the supervised SFS techniques while the label information is unknown.

A Study of Non-Economic Factors Impacting Consumer Demand

A study of non-economic factors impacting consumer demand tries to identify whether there is an impact of non-economic factors on consumer demand, or desire leading to demand. Respecting the subjective nature of demand. The researcher identifies variables leading to the creation of demand and tries to categorize the variables in the factors impacting the demand, identifying key factors and formulating them to find new connections and help understand demand. This study explores the non-economic factors, to understand the basics of demand by consumers. This study complements the law of demand, assuming an inverse relationship between price and demand; the researcher hypothecates certain non-economic variables that along with the price of the commodity influences the demand. This study shall prove to be an important tool to anyone who wants to understand demand dynamics and turn it in their own favor. The descriptive study with a survey and a detailed analysis of the data shall help to increase confidence in finding new connections between economic and non-economic factors that can be used as a strategy for sales and/or profit maximization. The study is built on a research scale that will help the reader to understand how the title is justified. The listed limitations of the study shall pave the path for further research in the given area.

Multi-source weighted source-free domain transfer method for rotating machinery fault diagnosis

The mainstream approach to addressing the issues of insufficient historical data and high annotation costs in the domain of rotating machinery is to build transfer learning models based on labeled multi-source data. However, the practical diagnosis of failure cases often relies on data privacy, thereby limiting the widespread application of current multi-source domain transfer approaches for the ‘data silos’ problem of. In view of the above problem, a multi-source weighted source-free domain transfer approach is designed for rotating machinery fault diagnosis, and the designed scheme can efficiently achieve data privacy and domain transfer. Specifically, the proposed approach achieves knowledge transfer from the source to the target during the training process of the unlabeled target data without accessing the source data. This is accomplished through the utilization of a designed reinforced information maximization strategy and improved self-training mechanism. Additionally, a weighted strategy is devised to automatically apply optimal values to all source domains based on their relevance to the target domain. The proposed framework demonstrates accuracy exceeding 96% across eight cross-domain diagnostic cases in two sets of rotating machinery data, with an average accuracy of 98.26%. These results underscore the exceptional ability of the proposed method to address cross-domain fault diagnosis in rotating machinery while ensuring privacy protection.

Domain-invariant feature fusion networks for semi-supervised generalization fault diagnosis

Machinery fault diagnosis based on deep learning methods is cost-effective to guarantee safety and reliability of mechanical systems. Due to the variability of machinery working condition and difficulty of data obtaining under different health states, it is desirable to enhance the generalization capability to unseen working conditions for the fault diagnosis models trained by available data sets under limited number of working conditions. Considering that labeling industrial data is also a laborious work, this paper proposes a novel semi-supervised domain generalization model, termed domain-invariant feature fusion networks (DIFFN) for intelligent fault diagnosis under unseen target working conditions. The main contributions are that, intra-domain-invariant features are considered to capture the intrinsic semantic information within the domain and are fused with inter-domain-invariant features to enhance the discrimination and generalization abilities in fault diagnosis. First, a domain-invariant representation learning method is established to learn the inter- and intra-domain-invariant features using two network branches and fuse them via a fusion module. Second, a mutual learning strategy is designed to enable the network branches and the fusion module to learn from each other, thereby improving the discrimination of the extracted features for accurate fault diagnosis. Lastly, a feature divergence maximization strategy is embedded between the two network branches to improve the generalization ability of the fault diagnosis model. Experiments on two bearing data sets demonstrate that the proposed model has better diagnostic accuracy and stability over state-of-the-art semi-supervised domain generalization methods, indicating its great potential for application in generalization fault diagnosis of machinery under unseen target working conditions.

Understanding the rationale and advantages of a traditional Mediterranean intercropping system in the nineteenth century

Traditional agricultural systems in Mediterranean Europe were characterised by diversity and multifunctionality, and polycultures played a fundamental role in them. Some of these farm systems and the traditional agricultural practices linked to them have now largely disappeared, but they are increasingly recognised as a valuable source of agroecological knowledge. In this study, we seek to recover the long-lost experience from a traditional Mediterranean intercropping system that combined the cultivation of vines and cereals. Using local historical resources available for a Catalan village for the second half of the nineteenth century, we compare the characteristics and functioning of intercropping and monocultures of vines and cereals using socioeconomic and agrarian metabolism indicators, and discuss the advantages of the traditional intercropping system as an adaptation to the productive limitations of the agroecosystem (particularly in terms of soil quality and productivity, and availability of labour and draft force), but also as a peasant economy strategy that responded to a multifunctional balancing rationale. This way, this research contributes to recovering the knowledge and experience of a long-lasting traditional crop system that had been used until the second half of the twentieth century, and provides an understanding of the rationale and advantages of traditional Mediterranean crop systems beyond productivity and profit maximisation strategies.

Fuel Economy Energy Management of Electric Vehicles Using Harris Hawks Optimization

Fuel cell hybrid electric vehicles (FCEVs) have gained significant attention due to their environmentally friendly nature and competitive performance. These vehicles utilize a fuel cell system as the primary power source, with a secondary power source such as a battery pack or supercapacitor. An energy management strategy (EMS) for FCEVs is critical in optimizing power distribution among different energy sources, considering factors such as hydrogen consumption and efficiency. The proposed EMS presents an optimized external energy maximization strategy using the Harris Hawks Optimization to reduce hydrogen consumption and enhance the system’s efficiency. Through a comparative simulation using the Federal Test Procedure (FTP-75) for the city driving cycle, the performance of the proposed EMS was evaluated and compared to existing algorithms. The simulation results indicate that the proposed EMS outperforms other existing solutions in terms of fuel consumption reduction, with a potential reduction of 19.81%. Furthermore, the proposed energy management strategy also exhibited an increase in system efficiency of 0.09%. This improvement can contribute to reducing the reliance on fossil fuels and mitigating the negative environmental impacts associated with vehicle emissions.

Characterization of the genetic composition and establishment of a core collection for the INERA Robusta coffee (Coffea canephora) field genebank from the Democratic Republic of Congo

Cultivation of Robusta coffee is likely to gain importance because of its high disease resistance and climate envelope. Robusta coffee genetic resources conserved in field genebanks can play an important role to further improve its cupping quality and other agronomic traits, but such Coffea canephora collections are limited and still poorly characterized. In this study, we characterized the genetic composition of the historically important but until recently neglected INERA Coffee Collection in Yangambi (the Democratic Republic of Congo). We used GBS to discover genome-wide genetic diversity, created and validated a novel multiplex amplicon sequencing (HiPlex) screening assay to genetically screen 730 coffee shrubs of the Yangambi Coffee Collection, grouped clonal material and delineated 263 accessions with unique genetic fingerprints. Comparison to reference material of three genetic origins revealed that the majority of the Yangambi accessions were assigned a ‘Lula’ cultivar origin, four accessions were assigned to Congolese subgroup A and nine accessions were most closely related to local wild accessions. About one-quarter of the accessions was likely derived from hybridization between these groups, which could result from seed-based propagation of the collection, breeding efforts, or natural cross-pollination. Parental analyses discovered eight preferentially used accessions, which may correspond to historically selected founders, or direct descendants thereof, whose seed material was once widely used to establish coffee plantations. Finally, two core collections were proposed using the maximization strategy (CC-I; 100 accessions) and genetic distance method (CC-X; 10 accessions). Our study demonstrates a method for the genetic characterization of Robusta coffee collections in general and contributes to the re-evaluation and exploration of the Robusta coffee genetic resources in the Democratic Republic of the Congo in particular.

Dynamic lung aeration and strain with positive end-expiratory pressure individualized to maximal compliance versus ARDSNet low-stretch strategy: a study in a surfactant depletion model of lung injury

BackgroundPositive end-expiratory pressure (PEEP) individualized to a maximal respiratory system compliance directly implies minimal driving pressures with potential outcome benefits, yet, raises concerns on static and dynamic overinflation, strain and cyclic recruitment. Detailed accurate assessment and understanding of these has been hampered by methodological limitations. We aimed to investigate the effects of a maximal compliance-guided PEEP strategy on dynamic lung aeration, strain and tidal recruitment using current four-dimensional computed tomography (CT) techniques and analytical methods of tissue deformation in a surfactant depletion experimental model of acute respiratory distress syndrome (ARDS).MethodsARDS was induced by saline lung lavage in anesthetized and mechanically ventilated healthy sheep (n = 6). Animals were ventilated in a random sequence with: (1) ARDSNet low-stretch protocol; (2) maximal compliance PEEP strategy. Lung aeration, strain and tidal recruitment were acquired with whole-lung respiratory-gated high-resolution CT and quantified using registration-based techniques.ResultsRelative to the ARDSNet low-stretch protocol, the maximal compliance PEEP strategy resulted in: (1) improved dynamic whole-lung aeration at end-expiration (0.456 ± 0.064 vs. 0.377 ± 0.101, P = 0.019) and end-inspiration (0.514 ± 0.079 vs. 0.446 ± 0.083, P = 0.012) with reduced non-aerated and increased normally-aerated lung mass without associated hyperinflation; (2) decreased aeration heterogeneity at end-expiration (coefficient of variation: 0.498 ± 0.078 vs. 0.711 ± 0.207, P = 0.025) and end-inspiration (0.419 ± 0.135 vs. 0.580 ± 0.108, P = 0.014) with higher aeration in dorsal regions; (3) tidal aeration with larger inspiratory increases in normally-aerated and decreases in poorly-aerated areas, and negligible in hyperinflated lung (Aeration × Strategy: P = 0.026); (4) reduced tidal strains in lung regions with normal-aeration (Aeration × Strategy: P = 0.047) and improved regional distributions with lower tidal strains in middle and ventral lung (Region-of-interest [ROI] × Strategy: P < 0.001); and (5) less tidal recruitment in middle and dorsal lung (ROI × Strategy: P = 0.044) directly related to whole-lung tidal strain (r = 0.751, P = 0.007).ConclusionsIn well-recruitable ARDS models, a maximal compliance PEEP strategy improved end-expiratory/inspiratory whole-lung aeration and its homogeneity without overinflation. It further reduced dynamic strain in middle-ventral regions and tidal recruitment in middle-dorsal areas. These findings suggest the maximal compliance strategy minimizing whole-lung dynamically quantified mechanisms of ventilator-induced lung injury with less cyclic recruitment and no additional overinflation in large heterogeneously expanded and recruitable lungs.

Fe─N─C Electrocatalyst for Enhancing Fe(II)/Fe(III) Redox Kinetics in Thermo‐Electrochemical Cells

AbstractHarvesting low‐grade waste heat, which constitutes 60% of the overall waste heat, is key to halting climate change. Electrochemical waste‐heat harvesting has recently drawn attention to practical low‐grade waste‐heat harvesting. In this study, a power density maximization strategy is presented in scalable and cost‐effective aqueous redox couple‐based thermo‐electrochemical cells (TECs). The n‐type feature of the water‐soluble Fe2+/3+ redox couple is essential for constructing the TEC p–n leg device; however, it has not been investigated much so far. The modulation of the chaotropicity of counteranions enhances the absolute value of the Seebeck coefficient for the Fe2+/3+ redox couple with an inner‐sphere reaction mechanism because of the greater structural disorder in the solvation shell. Moreover, the use of a cost‐effective Fe─N─C electrocatalyst shows redox kinetics and a power density comparable to those of state‐of‐the‐art Pt electrodes, economically compensating for the sluggish charge‐transfer kinetics of the inner‐sphere redox mechanism. The Fe─N─C ‐based TEC device exhibits 1.73 W m−2 of power density at 0.1 $ W−1 of cost per power, which is 1.24% with respect to the Carnot efficiency, exceeding 0.23–0.53% compared to those reported for previous Pt‐based TEC devices with the same redox chemistry.

Real earnings management trends in the context of the COVID-19 pandemic: The case of non-financial listed companies in Vietnam

Real earnings management comprises the intervention by managers intending to change business strategies or policies to achieve specific goals. The paper aims to examine trends and levels of real earnings management in the context of the COVID-19 pandemic in Vietnam. The study uses time series data, and the sample includes 1,800 observations from 2016 to 2021. The methods of the study are regression analyses of the real earnings management model. The results indicate that the COVID-19 pandemic positively and significantly affected real earnings management of companies listed on the Vietnamese stock exchange. The trends and levels of real earnings management in the context of the COVID-19 pandemic increase depending on the severity of the pandemic. In terms of applications, the study provides evidence that the quality of financial reporting is lower during the pandemic. Listed enterprises in Vietnam are using high financial leverage, leading to a higher vulnerability to shocks such as the pandemic. Therefore, the real earnings management technique mainly used by managers is operating cash flow adjustment by using income maximization strategies to increase the ability to borrow capital to maintain business operations. The study suggests that the choice of income maximization or income minimization strategy depends mainly on commitments with the capital provider (credit institutions), specific contexts, and economic factors.

Coyote Optimization Algorithm-Based Energy Management Strategy for Fuel Cell Hybrid Power Systems

This research proposes an improved energy management strategy (EMS) for a fuel cell hybrid power system for an electric aircraft based on a recently developed coyote optimization algorithm (COA). The suggested hybrid system consists of fuel cells and an energy storage system (ESS) to supply the required load in stable conditions. The distribution and performance of the hybrid electrical power system are determined by various energy sources. Consequently, having the best energy management system is essential for completing this work. The suggested EMS’s main objectives are to reduce hydrogen energy utilization and increase power source longevity. The proposed coyote optimization algorithm with external energy maximization strategy (COA-EEMS) and coyote optimization algorithm with equivalent consumption minimisation strategy (COA-ECMS) are tested with the help of the Opal-RT 5700 real-time HIL simulator and MATLAB/Simulink. The proposed algorithms confirm their robustness and higher efficiency by minimizing hydrogen fuel consumption compared to existing algorithms. The merits of the proposed algorithms are presented in detailed and compared with existing algorithms.

ANALYSIS OF SPORTS PERFORMANCES USING MACHINE LEARNING AND STATISTICAL MODELS - A GENERAL ANALYSIS OF THE LITERATURE

The attendance of football fans at the matches played in the big city stadiums has a significant impact on the incomes of football clubs, an aspect studied more in recent years in the specialized literature, but with the summary presentation of related analysis techniques. Machine Learning remains certainly one of the preferred methodologies that has shown gratifying results in the fields of sports classification and prediction. One of the ever-growing fields that require good accuracy continues to be sports prediction, due to the huge amounts of money involved (player transactions, betting market, etc). These predictive models created for application within various clubs become a starting point for creating revenue maximization strategies. Taking into account these aspects, I will start by presenting the necessary steps in cleaning the data sets, continuing with the data preparation and their exploratory analysis by presenting the techniques offered by CRAN for the use of the R language and by non-programmers. Therefore, after the data set is prepared, we can start formulating the research questions, and this paper aims to present an objective analysis of the sports prediction models presented in specialized papers and the directions that can be followed in future research in the sports field, especially football.

Which Influencers Can Maximize PCR of E-Commerce?

The Web has provided an increasing proportion of use as a medium for e-commerce in addition to various recommender systems. It can be used for analyzing recommendation system-based feedback (e.g., a form in which a user inputs their preferences for various items as numerical values into a specific evaluation system) to estimate customer interest; in addition, analyzing multi-modal types of feedback (e.g., product purchase traces, inquiry lists, inquiry times, and comments) with deep learning can also be used to estimate user interest. As many companies around the world promote their products through micro-influencers on the Web, related research has continued to predict the purchase conversion rate of the influencer through a variety of technologies. In this work, we present a multi-modal micro-influencer analysis scheme for a marketing maximization strategy. Our scheme uses the multi-modal data stored in Mecha Solution’s own shopping mall of Korea, as well as famous Korean Internet platforms, and Coupang, Naver, and Oliveyoung’s data such as article posting comments and statistics information. By extracting the main characteristics of the real article postings from real users as opposed to those from factitious influencers posting articles and comments and identifying articles other than advertisements, influencer scores are obtained, assuming that articles other than advertisements can further increase the purchase conversion rate. Based on influencer score, we propose a multi-modal micro-influencer analysis scheme that recommends influencers use content-based collaborative filtering and user-based collaborative filtering for items that the influencer has not yet reviewed. The experiment was implemented to prove that the proposed scheme successfully achieves this goal.

Maximizing the Sum-Rate of Secondary Cognitive Radio Networks by Jointly Optimizing Beamforming and Energy Harvesting Time

In this paper, we consider an energy harvesting (EH) cooperative cognitive radio network (CCRN) framework, in which the primary network (consisting of a transmitter-receiver pair) shares both energy and spectrum with the secondary network (consisting of two transceivers and multiple amplify-and-forward (AF)-based relays). Since the relays have no power source, they periodically switch between the EH and information transmission (IT) tasks. In particular, we apply a sum-rate maximization strategy to jointly obtain the optimal EH time allocation factor and distributed beamforming coefficients that achieve the best system performance for the secondary network under the individual EH power constraints at relays and an interference power constraint at the primary receiver. Although the formulated optimization problem is non-convex and complicated, we devise an efficient iterative scheme by applying the semi-definite relaxation (SDR) technique followed by the successive upper-bound minimization (SUM) strategy. Our simulation results confirm that the proposed method is spectrally and energy efficient.

Key Regulatory Initiatives in EU Sustainable Banking

Abstract With temperature exhibiting a growing trend and posing threats to future generations, the Paris Agreement set a cap level of no more than 2°C for the temperature increase, emphasizing the need for cross-national participation to combat climate change. At the European level, the European Commission pledged to make Europe achieve carbon neutrality by mid-century. However, to deliver on its commitment, extensive financial support and engagement from both private and public-sector players were acknowledged as requisites. Therefore, through their funding mechanism, banks assumed a leading role in financing the transition to a green economy. Yet, a new challenge of systemic nature – i.e. ESG risks – emerged, gaining regulatory attention and subsequently triggering numerous regulatory reforms. To date, the focus at the European level rests on the development of a comprehensive definition of ESG risks as well as of a system to identify activities in scope, i.e. the EU Taxonomy that can be nonetheless assessed as the core regulatory framework on which amendments that followed were based. To address the dynamics of the regulatory landscape and to ensure compliance, banks undertook several strategic changes. Not only that they adjusted the offered products and services, but they also amended their governance structures. Specifically, a clear shift could have been identified from employing profit maximization strategies to conducting activities that serve the interests of not only shareholders but a broader category of stakeholders including the general public as well as the surrounding environment. Following the implementation of sustainability-related strategic initiatives, changes in banks’ performance and their risk-taking capabilities could have been identified. Therefore, aiming to explore the extent to which ESG consideration can be considered as a strategic opportunity, or contrastingly a challenge, mixed opinions were acknowledged pointing towards the complexity of the topic at hand, complexities which are enhanced by the topic’s novelty as well as by the uncertain and convoluted nature of the EU regulatory environment addressing sustainability.

Reliability Assessment of Demand Response Strategies for Profit Maximization

The idea of renewable resource integration influences the demand-supply balance significantly. The effort taken to diminish the deviation in demand-supply balance is greatly influenced by demand response (DR) strategy. In this analysis, the concept of DR is incorporated via the approach of load management. The optimal scheduling of the load considering the available energy helps to avoid unessential load shedding. The DR strategy of load shifting is suggested at different levels in accordance with the nature of load and real-time price. The profit of the utility is enhanced by providing the customer the flexibility to schedule their load. The curtailment of load can be avoided by increased customer participation in the suggested DR programs. The operating system is designed to be both efficient and effective by utilizing the real-time tariff profile of electricity to plan the generation of renewable energy, energy storage systems that include electric vehicles, and the utilization of grid power. The scalability and generalization of the proposed DR approach make it suitable for large-scale power systems that integrate renewable energy sources. The validation of the proposed method using reliability analysis ensures the effectiveness of the formulated DR. The proposed method is tested under two different scenarios and topologies on the IEEE-33 bus system and the results accomplished are promising.

Diversity maximization-based transfer diagnosis approach of rotating machinery

The existing transfer diagnosis methods based on entropy minimization are easy to lead to trivial solution. To solve this problem, a deep diversity maximization-based adversarial transfer diagnosis approach for rotating machinery is presented in this paper. Firstly, the deep convolution neural network is utilized as the feature encoder to learn the characteristics of vibration signals in different working conditions. The diversity maximization strategy is taken to balance the entropy minimization, so as to avoid trivial local minimum. The categories predicted by nontrivial domain adaptation method are more diverse. Moreover, the entropy is conducted to evaluate the uncertainty of the predicted result of the classifier. Using this deterministic strategy based on entropy to adjust the domain discriminator. The experimental study demonstrates the effectiveness of the developed method.

Secrecy Energy Efficiency Maximization for Multi-User Multi-Eavesdropper Cell-Free Massive MIMO Networks

In this paper, the secrecy energy efficiency (SEE) optimization problem is studied in multi-user multi-eavesdropper cell-free massive multiple-input multiple-output (CF-mMIMO) networks, where a large number of distributed access points (APs) equipped with multiple antennas connect to a central processing unit via backhaul links to communicate with multiple users in the presence of multiple active non-colluding eavesdroppers. In order to theoretically analyze the SEE performance of CF-mMIMO systems, we first provide a lower bound on the ergodic secrecy rate. Meanwhile, a realistic model is used to measure the power consumption of CF-mMIMO networks, which takes into account the radio transmit power of all APs, circuit power consumption, and the power consumed by the backhaul link transmissions. Then, we develop a confidential and energy-efficient design for transmit power allocation subject to individual quality-of-service requirements for the users and total transmit power constraint of all APs. For the reason that the proposed SEE maximization (SEEM) problem is non-convex, we give a two-tier iterative power allocation algorithm, capitalizing on Dinkelbach's method and successive convex approximation approach. Simulation results show that the proposed SEEM algorithm converges fast and outperforms the conventional secrecy rate maximization and energy efficiency maximization strategies in terms of SEE performance.

Evaluation of Hospital Information Management System (HIMS) by using the method Coping Model of User Adaption (CMUA) at the Siti Khodijah Sepanjang Hospital

The Siti Khodijah Muhammdiyah Sepanjang Hospital uses EHOS as a means of providing HIMS. Preliminary study found that in outpatient units consisting of 30 clinics, 1 clinic did not record on the EHOS application, namely home care in filling out data, it was known that filling was still incomplete on patient identity sheets, general consent, initial assessment, and special examinations. The porposed this study to improve the utilization of HISM technology at siti khodijah sepanjang hospital based on the CMUA method using a sample of 135 respondents determination of the number of samples used the formula estimate a proportional in finite population with minimal 117 samples. Simple random sampling was used. This study was conducted from September to December 2022 at Siti Khodijah Sepanjang Hospital. The results of this study are 8 hypotheses, most of which have significant value, including H1: the perception of using HIMS as an opportunity (PO) will positively influence the adoption of a benefit-maximizing strategy (BM) of significant value (sig.0.007). H2: the perception of using HIMS as an opportunity (PO) will positively influence the adoption of a benefit satisfaction strategy (BS) with significant value (sig. 0.00). H3: The manager's perception of the use of HIMS as a threat (PT) will positively influence the disturbance management strategy (DH), with a significant value (sig. 0.000). H5: when managers perceive a situation as an opportunity, the more control they feel over the benefits of HIMS, the more likely they are to adopt a benefit maximizing (BM) strategy of significant value (sig. 0.000). H6: When managers perceive a situation as an opportunity, the more control they feel over the benefits of HIMS, the more likely they are to adopt a significant value benefit satisfaction (BS) strategy (sig. 0.016). H7: when a manager perceives the use of HIMS as a threat, the greater the perceived control over the implementation of security measures, the more he or she will adopt an intrusion management strategy (SP) of significant value (sig. 0.004). It was concluded that in using HIMS to optimize its use from the perspective of threats and opportunities, managers can adopt benefit maximizing strategies, benefit satisficing strategies, and disturbance handling strategies.

Maximization, internal reference price and consumers' response to promotions

PurposeThis study shows the influence of consumers' goal and strategy of maximization on the process behind their behavioral response to price.Design/methodology/approachData was collected through a 2×2 experimental design involving 314 respondents and analyzed using PLS-SEM.FindingsThe results show that when buyers maximize, their transaction value and acquisition value perceptions predict their behavioral response to deals and discounts. Further, these buyers do not consider sales price information to form their internal reference price. On the other hand, when buyers satisfice, their transaction value perceptions predict the behavioral responses to price deals, and the relationship between transaction value and the behavioral response is not mediated by acquisition value. Further, such buyers consider sales price to form their internal reference price.Originality/valueThe theory of purchase value assumes that consumers seek to maximize their value. Our work relaxes this assumption to show how value maximizers (and satisficers) differ in the process of value formation and their responses to price promotions.