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Positioning through visual merchandising: Can multi-brand outlets do it? ASIA PACIFIC MANAGEMENT REVIEW 2022. [DOI: 10.1016/j.apmrv.2022.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Huo C, Xu G, Li W, Xie H, Zhang T, Liu Y, Li Z. A review on functional near-infrared spectroscopy and application in stroke rehabilitation. MEDICINE IN NOVEL TECHNOLOGY AND DEVICES 2021. [DOI: 10.1016/j.medntd.2021.100064] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
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Why he buys it and she doesn't – Exploring self-reported and neural gender differences in the perception of eCommerce websites. COMPUTERS IN HUMAN BEHAVIOR 2021. [DOI: 10.1016/j.chb.2021.106809] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Business and Energy Efficiency in the Age of Industry 4.0: The Hulten, Broweus and Van Dijk Sensory Marketing Model Applied to Spanish Textile Stores during the COVID-19 Crisis. ENERGIES 2021. [DOI: 10.3390/en14071966] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Strategic and tactical factors come into play in shop competitiveness where, in addition to the products sold, other marketing mix variables must also be considered. There are also subjective factors, such as perceptions through the senses. This became even more important when, as a result of the COVID-19 crisis and the forced closure of certain establishments with physical sales, it was necessary to increase profitability and efficiency. The aim of this study was to determine the exact role of sensory marketing in shop efficiency and profitability, based on the guiding principles of technology, innovation, and respect for the environment. We conducted an exploratory and experimental study consisting of the creation of a sensory strategy through the adaptation of the Hulten, Broweus and Van Dijk model on a specific establishment in the current era of Industry 4.0. The results indicate an increase in sales as well as customer satisfaction and happiness after implementing the relevant strategies. The conclusions show that this model is valid and reliable for physical retail establishments, and that these business strategies can significantly contribute to the optimisation of energy resources.
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Yang D, Hong KS. Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach. J Alzheimers Dis 2021; 80:647-663. [PMID: 33579839 DOI: 10.3233/jad-201163] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
BACKGROUND Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer's disease. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors. OBJECTIVE This study aims to investigate the feasibility of individual MCI detection from healthy control (HC) using a minimum duration of resting-state functional near-infrared spectroscopy (fNIRS) signals. METHODS In this study, nine different measurement durations (i.e., 30, 60, 90, 120, 150, 180, 210, 240, and 270 s) were evaluated for MCI detection via the graph theory analysis and traditional machine learning approach, such as linear discriminant analysis, support vector machine, and K-nearest neighbor algorithms. Moreover, feature representation- and classification-based transfer learning (TL) methods were applied to identify MCI from HC through the input of connectivity maps with 30 and 90 s duration. RESULTS There was no significant difference among the nine various time windows in the machine learning and graph theory analysis. The feature representation-based TL showed improved accuracy in both 30 and 90 s cases (i.e., 30 s: 81.27% and 90 s: 76.73%). Notably, the classification-based TL method achieved the highest accuracy of 95.81% using the pre-trained convolutional neural network (CNN) model with the 30 s interval functional connectivity map input. CONCLUSION The results indicate that a 30 s measurement of the resting-state with fNIRS could be used to detect MCI. Moreover, the combination of neuroimaging (e.g., functional connectivity maps) and deep learning methods (e.g., CNN and TL) can be considered as novel biomarkers for clinical computer-assisted MCI diagnosis.
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Affiliation(s)
- Dalin Yang
- School of Mechanical Engineering, Pusan National University, Guemjeong-gu, Busan, Republic of Korea
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Guemjeong-gu, Busan, Republic of Korea.,Department of Cogno-Mechatronics Engineering, Pusan National University, Guemjeong-gu, Busan, Republic of Korea
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Kim HS, Lee JH, Yoo SH. Is consumer neural response to visual merchandising types different depending on their fashion involvement? PLoS One 2020; 15:e0241578. [PMID: 33362255 PMCID: PMC7757867 DOI: 10.1371/journal.pone.0241578] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 10/19/2020] [Indexed: 11/18/2022] Open
Abstract
This study investigated consumers' responses to fashion visual merchandising (VM) from a neuroscientific perspective. The brain activations of 20 subjects differently involved in fashion were recorded using functional near-infrared spectroscopy in response to three different fashion VM types. According to the types of fashion VM, significant differences were observed, which were significantly higher for the creative VM. Moreover, highly fashion-involved subjects showed activation of the orbital frontal cortex region in response to the creative VM. Based on these results, it is suggested that marketing strategies should be devised explicitly for the brand's targeted audience and goals.
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Affiliation(s)
| | - Jin-Hwa Lee
- Department of Clothing & Textiles, College of Human Ecology, Pusan National University, Busan, Korea
| | - So-Hyeon Yoo
- School of Mechanical Engineering, Pusan National University, Busan, Korea
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Gier NR, Strelow E, Krampe C. Measuring dlPFC Signals to Predict the Success of Merchandising Elements at the Point-of-Sale - A fNIRS Approach. Front Neurosci 2020; 14:575494. [PMID: 33328849 PMCID: PMC7714758 DOI: 10.3389/fnins.2020.575494] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 10/22/2020] [Indexed: 12/02/2022] Open
Abstract
The (re-)launch of products is frequently accompanied by point-of-sale (PoS) marketing campaigns in order to foster sales. Predicting the success of these merchandising elements at the PoS on sales is of interest to research and practice, as the misinvestments that are based on the fragmented PoS literature are tremendous. Likewise, the predictive power of neuropsychological methods has been demonstrated in various research work. Nevertheless, the practical application of these neuropsychological methods is still limited. In order to foster the application of neuropsychological methods in research and practice, the current research work aims to explore, whether mobile functional near-infrared spectroscopy (fNIRS) - as a portable neuroimaging method - has the potential to predict the success of PoS merchandising elements by rendering significant neural signatures of brain regions of the dorsolateral prefrontal cortex (dlPFC), highlighting its potential to forecast shoppers' behaviour aka sales at the PoS. Building on previous research findings, the results of the given research work indicate that the neural signal of brain regions of the dlPFC, measured with mobile fNIRS, is able to predict actual sales associated with PoS merchandising elements, relying on the cortical relief effect. More precisely, the research findings support the hypothesis that the reduced neural activity of brain regions associated with the dlPFC can predict sales at the PoS, emphasising another crucial neural signature to predict shoppers' purchase behaviour, next to the frequently cited reward association system. The research findings offer an innovative perspective on how to design and evaluate PoS merchandising elements, indicating fruitful theoretical and practical implications.
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Affiliation(s)
- Nadine R. Gier
- Faculty of Business Administration and Economics, Chair of Marketing, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Enrique Strelow
- Faculty of Business Administration and Economics, Chair of Marketing and Sales, Justus Liebig University Gießen, Gießen, Germany
- Shopper Science, Ferrero Deutschland, Frankfurt am Main, Germany
| | - Caspar Krampe
- Faculty of Business Administration and Economics, Chair of Marketing, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Consumer Research and Marketing Group, Department of Social Science, Wageningen University & Research, Wageningen, Netherlands
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Yang D, Huang R, Yoo SH, Shin MJ, Yoon JA, Shin YI, Hong KS. Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy. Front Aging Neurosci 2020; 12:141. [PMID: 32508627 PMCID: PMC7253632 DOI: 10.3389/fnagi.2020.00141] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 04/27/2020] [Indexed: 12/16/2022] Open
Abstract
Mild cognitive impairment (MCI) is the clinical precursor of Alzheimer's disease (AD), which is considered the most common neurodegenerative disease in the elderly. Some MCI patients tend to remain stable over time and do not evolve to AD. It is essential to diagnose MCI in its early stages and provide timely treatment to the patient. In this study, we propose a neuroimaging approach to identify MCI using a deep learning method and functional near-infrared spectroscopy (fNIRS). For this purpose, fifteen MCI subjects and nine healthy controls (HCs) were asked to perform three mental tasks: N-back, Stroop, and verbal fluency (VF) tasks. Besides examining the oxygenated hemoglobin changes (ΔHbO) in the region of interest, ΔHbO maps at 13 specific time points (i.e., 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, and 65 s) during the tasks and seven temporal feature maps (i.e., two types of mean, three types of slope, kurtosis, and skewness) in the prefrontal cortex were investigated. A four-layer convolutional neural network (CNN) was applied to identify the subjects into either MCI or HC, individually, after training the CNN model with ΔHbO maps and temporal feature maps above. Finally, we used the 5-fold cross-validation approach to evaluate the performance of the CNN. The results of temporal feature maps exhibited high classification accuracies: The average accuracies for the N-back task, Stroop task, and VFT, respectively, were 89.46, 87.80, and 90.37%. Notably, the highest accuracy of 98.61% was achieved from the ΔHbO slope map during 20-60 s interval of N-back tasks. Our results indicate that the fNIRS imaging approach based on temporal feature maps is a promising diagnostic method for early detection of MCI and can be used as a tool for clinical doctors to identify MCI from their patients.
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Affiliation(s)
- Dalin Yang
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
| | - Ruisen Huang
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
| | - So-Hyeon Yoo
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
| | - Myung-Jun Shin
- Department of Rehabilitation Medicine, Pusan National University School of Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, South Korea
| | - Jin A Yoon
- Department of Rehabilitation Medicine, Pusan National University School of Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, South Korea
| | - Yong-Il Shin
- Department of Rehabilitation Medicine, Pusan National University School of Medicine, Pusan National University Yangsan Hospital, Yangsan-si, South Korea
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
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Benitez-Andonegui A, Burden R, Benning R, Möckel R, Lührs M, Sorger B. An Augmented-Reality fNIRS-Based Brain-Computer Interface: A Proof-of-Concept Study. Front Neurosci 2020; 14:346. [PMID: 32410938 PMCID: PMC7199634 DOI: 10.3389/fnins.2020.00346] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 03/23/2020] [Indexed: 02/04/2023] Open
Abstract
Augmented reality (AR) enhances the user's environment by projecting virtual objects into the real world in real-time. Brain-computer interfaces (BCIs) are systems that enable users to control external devices with their brain signals. BCIs can exploit AR technology to interact with the physical and virtual world and to explore new ways of displaying feedback. This is important for users to perceive and regulate their brain activity or shape their communication intentions while operating in the physical world. In this study, twelve healthy participants were introduced to and asked to choose between two motor-imagery tasks: mental drawing and interacting with a virtual cube. Participants first performed a functional localizer run, which was used to select a single fNIRS channel for decoding their intentions in eight subsequent choice-encoding runs. In each run participants were asked to select one choice of a six-item list. A rotating AR cube was displayed on a computer screen as the main stimulus, where each face of the cube was presented for 6 s and represented one choice of the six-item list. For five consecutive trials, participants were instructed to perform the motor-imagery task when the face of the cube that represented their choice was facing them (therewith temporally encoding the selected choice). In the end of each run, participants were provided with the decoded choice based on a joint analysis of all five trials. If the decoded choice was incorrect, an active error-correction procedure was applied by the participant. The choice list provided in each run was based on the decoded choice of the previous run. The experimental design allowed participants to navigate twice through a virtual menu that consisted of four levels if all choices were correctly decoded. Here we demonstrate for the first time that by using AR feedback and flexible choice encoding in form of search trees, we can increase the degrees of freedom of a BCI system. We also show that participants can successfully navigate through a nested menu and achieve a mean accuracy of 74% using a single motor-imagery task and a single fNIRS channel.
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Affiliation(s)
- Amaia Benitez-Andonegui
- Department Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
- Laboratory for Cognitive Robotics and Complex Self-Organizing Systems, Department of Data Science and Knowledge Engineering, Faculty of Science and Engineering, Maastricht University, Maastricht, Netherlands
| | - Rodion Burden
- Department Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
| | - Richard Benning
- Instrumentation Engineering, Dean and Directors Office, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Rico Möckel
- Laboratory for Cognitive Robotics and Complex Self-Organizing Systems, Department of Data Science and Knowledge Engineering, Faculty of Science and Engineering, Maastricht University, Maastricht, Netherlands
| | - Michael Lührs
- Department Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
- Research Department, Brain Innovation B.V., Maastricht, Netherlands
| | - Bettina Sorger
- Department Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
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Zafar A, Hong KS. Reduction of Onset Delay in Functional Near-Infrared Spectroscopy: Prediction of HbO/HbR Signals. Front Neurorobot 2020; 14:10. [PMID: 32132918 PMCID: PMC7040361 DOI: 10.3389/fnbot.2020.00010] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 01/30/2020] [Indexed: 12/14/2022] Open
Abstract
An intrinsic problem when using hemodynamic responses for the brain-machine interface is the slow nature of the physiological process. In this paper, a novel method that estimates the oxyhemoglobin changes caused by neuronal activations is proposed and validated. In monitoring the time responses of blood-oxygen-level-dependent signals with functional near-infrared spectroscopy (fNIRS), the early trajectories of both oxy- and deoxy-hemoglobins in their phase space are scrutinized. Furthermore, to reduce the detection time, a prediction method based upon a kernel-based recursive least squares (KRLS) algorithm is implemented. In validating the proposed approach, the fNIRS signals of finger tapping tasks measured from the left motor cortex are examined. The results show that the KRLS algorithm using the Gaussian kernel yields the best fitting for both ΔHbO (i.e., 87.5%) and ΔHbR (i.e., 85.2%) at q = 15 steps ahead (i.e., 1.63 s ahead at a sampling frequency of 9.19 Hz). This concludes that a neuronal activation can be concluded in about 0.1 s with fNIRS using prediction, which enables an almost real-time practice if combined with EEG.
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Affiliation(s)
- Amad Zafar
- School of Mechanical Engineering, Pusan National University, Busan, South Korea.,Department of Electrical Engineering, University of Wah, Wah Cantonment, Pakistan
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, South Korea.,Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, South Korea
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Mirbagheri M, Hakimi N, Ebrahimzadeh E, Setarehdan SK. Quality analysis of heart rate derived from functional near-infrared spectroscopy in stress assessment. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2019.100286] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
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