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The first step toward developing an automated construction workers performance monitoring system is to initially establish a complete and competent activity recognition solution, which is still lacking. This study aims to propose a novel approach of using labor physiological data collected through wearable sensors as means of remote and automatic activity recognition.

A pilot study is conducted against three pre-fabrication stone construction workers throughout three full working shifts to test the ability of automatically recognizing the type of activities they perform in-site through their lively measured physiological signals (i.e. blood volume pulse, respiration rate, heart rate, galvanic skin response and skin temperature). The physiological data are broadcasted from wearable sensors to a tablet application developed for this particular purpose, and are therefore used to train and assess the performance of various machine-learning classifiers.

A promising result of up to 88% accuracy level for activity recognition was achieved by using an artificial neural network classifier. Nonetheless, special care needs to be taken for some activities that evoke similar physiological patterns. It is expected that blending this method with other currently developed camera-based or kinetic-based methods would yield higher activity recognition accuracy levels.

The proposed method complements previously proposed labor tracking methods that focused on monitoring labor trajectories and postures, by using additional rich source of information from labors physiology, for real-time and remote activity recognition. Ultimately, this paves for an automated and comprehensive solution with which construction managers could monitor, control and collect rich real-time data about workers performance remotely.

This study aims to illuminate the contribution of neurophysiological techniques in the field of marketing and consumer decision-making and to highlight avenues and research questions that marketing researchers can take advantage of from neuroscience and psychology to inform marketing phenomena.

The authors first reviewed the roots and definition of consumer neuroscience. Then, the authors outlined the main characteristics of the most commonly used neurophysiological tools (namely, skin conductance, facial electromyography, electrocardiogram, eye-tracking, electroencephalography, functional magnetic resonance imaging, functional near-infrared spectroscopy, magnetoencephalography and transcranial magnetic stimulation) with a special emphasis on their advantages and weaknesses. Finally, the authors propose the development of research lines that could be implemented by marketing researchers with an appropriate application and understanding of tools and theories of neuroscience and psychology.

The authors propose research questions to be addressed within four thematic areas: opportunities in product decisions (predicting product purchasing decisions, consumer responses to branding efforts and packaging), pricing, communication and retailing scenarios. The authors also incorporate insights into the complementarity of neurophysiological tools to traditional ones and situations in which these tools are useful for enhancing marketing theory. The authors finally shed light on the moral–ethical criticisms of this new branch of marketing.

To the best of the authors’ knowledge, this research constitutes the first study in identifying the research opportunities that marketing researchers could take advantage from neuroimaging and physiological tools to inform marketing theory and practice.

Esta investigación tiene como objetivo esclarecer la contribución de las técnicas neurofisiológicas en el campo del marketing y la toma de decisiones de los consumidores y destacar las vías y preguntas de investigación que los investigadores de marketing pueden aprovechar de la neurociencia y la psicología para informar sobre los fenómenos del marketing.

En primer lugar, revisamos el origen y la definición de la neurociencia del consumidor. A continuación, esbozamos las principales características de las herramientas neurofisiológicas más utilizadas (a saber, la conductancia, la electromiografía facial, el electrocardiograma, el seguimiento ocular, la electroencefalografía, la resonancia magnética funcional, la espectroscopia funcional en el infrarrojo cercano, la magnetoencefalografía y la estimulación magnética transcraneal), haciendo especial hincapié en sus ventajas y debilidades. Finalmente, se propone el desarrollo de líneas de investigación que podrían ser implementadas por los investigadores de marketing con una adecuada aplicación y comprensión de las herramientas y teorías de la neurociencia y la psicología.

Proponemos preguntas de investigación para ser abordadas dentro de cuatro áreas temáticas: oportunidades en las decisiones de producto (predicción de las decisiones de compra de productos, respuestas de los consumidores a los esfuerzos de marca y envasado), precios, comunicación y distribución. También incorporamos ideas sobre la complementariedad de las herramientas neurofisiológicas con las tradicionales y las situaciones en las que estas herramientas son útiles para mejorar la teoría del marketing. Por último, arrojamos luz sobre las críticas ético-morales a esta nueva rama del marketing.

本研究旨在阐明神经生理学技术在营销和消费者决策领域的贡献, 并强调营销研究人员可以从神经科学和心理学中利用的途径和研究问题, 以告知营销现象。

我们首先回顾了消费者神经科学的根基和定义。然后, 我们概述了最常用的神经生理学工具（即皮肤电导率、面部肌电图、心电图、眼球追踪、脑电图、功能性磁共振成像、功能性近红外光谱、脑磁图和经颅磁刺激）的主要特点, 特别强调了它们的优势和劣势。最后, 我们提出了研究路线的发展, 这些路线可以由营销研究人员通过适当的应用和理解神经科学和心理学的工具和理论来实施。

我们提出了四个主题领域的研究问题：产品决策中的机会（预测产品购买决策、消费者对品牌推广工作的反应和包装）、定价、沟通和零售场景。我们还纳入了对神经生理学工具与传统工具的互补性的见解, 以及这些工具对加强营销理论有用的情况。最后, 我们对这个新的营销分支的道德伦理批评进行了说明。

纸张类型 – 研究论文

The impact of stress on personal and work-related outcomes has been studied in the information systems (IS) literature across several professions. However, the cybersecurity profession has received little attention despite numerous reports suggesting stress is a leading cause of various adverse professional outcomes. Cybersecurity professionals work in a constantly changing adversarial threat landscape, are focused on enforcement rather than compliance, and are required to adhere to ever-changing industry mandates – a work environment that is stressful and has been likened to a war zone. Hence, this literature review aims to reveal gaps and trends in the current extant general workplace and IS-specific stress literature and illuminate potentially fruitful paths for future research focused on stress among cybersecurity professionals.

Using the systematic literature review process (Okoli and Schabram, 2010), the authors examined the current IS research that studies stress in organizations. A disciplinary corpus was generated from IS journals and conferences encompassing 30 years. The authors analyzed 293 articles from 21 journals and six conferences to retain 77 articles and four conference proceedings for literature review.

The findings reveal four key research opportunities. First, the demands experienced by cybersecurity professionals are distinct from the demands experienced by regular information technology (IT) professionals. Second, it is crucial to identify the appraisal process that cybersecurity professionals follow in assessing security demands. Third, there are many stress responses from cybersecurity professionals, not just negative responses. Fourth, future research should focus on stress-related outcomes such as employee productivity, job satisfaction, job turnover, etc., and not only security compliance among cybersecurity professionals.

This study is the first to provide a systematic synthesis of the IS stress literature to reveal gaps, trends and opportunities for future research focused on stress among cybersecurity professionals. The study presents several novel trends and research opportunities. It contends that the demands experienced by cybersecurity professionals are distinct from those experienced by regular IT professionals and scholars should seek to identify the key characteristics of these demands that influence their appraisal process. Also, there are many stress responses, not just negative responses, deserving increased attention and future research should focus on unexplored stress-related outcomes for cybersecurity professionals.

The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.

Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.

The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.

The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.

Technical solutions can be important when key communicators take on the task of making sense of social media flows during crises. However, to provide situation awareness during high-stress assignments, usability problems must be identified and corrected. In usability studies, where researchers investigate the user-friendliness of a product, several types of data gathering methods can be combined. Methods may include subjective (surveys and observations) and psychophysiological (e.g. skin conductance and eye tracking) data collection. This chapter mainly focuses on how the latter type can provide detailed clues about user-friendliness. Results from two studies are summarised. The tool tested is intended to help communicators and journalists with monitoring and handling social media content during times of crises.

The authors aim to develop a conceptual framework for longitudinal estimation of stress-related states in the wild (IW), based on the machine learning (ML) algorithms that use physiological and non-physiological bio-sensor data.

The authors propose a conceptual framework for longitudinal estimation of stress-related states consisting of four blocks: (1) identification; (2) validation; (3) measurement and (4) visualization. The authors implement each step of the proposed conceptual framework, using the example of Gaussian mixture model (GMM) and K-means algorithm. These ML algorithms are trained on the data of 18 workers from the public administration sector who wore biometric devices for about two months.

The authors confirm the convergent validity of a proposed conceptual framework IW. Empirical data analysis suggests that two-cluster models achieve five-fold cross-validation accuracy exceeding 70% in identifying stress. Coefficient of accuracy decreases for three-cluster models achieving around 45%. The authors conclude that identification models may serve to derive longitudinal stress-related measures.

Proposed conceptual framework may guide researchers in creating validated stress-related indicators. At the same time, physiological sensing of stress through identification models is limited because of subject-specific reactions to stressors.

Longitudinal indicators on stress allow estimation of long-term impact coming from external environment on stress-related states. Such stress-related indicators can become an integral part of mobile/web/computer applications supporting stress management programs.

Timely identification of excessive stress may improve individual well-being and prevent development stress-related diseases.

The study develops a novel conceptual framework for longitudinal estimation of stress-related states using physiological and non-physiological bio-sensor data, given that scientific knowledge on validated longitudinal indicators of stress is in emergent state.

The purpose of this paper is to examine FaceReader as a tool to compare the olfactory preferences of two selected countries. This paper examines the olfactory preferences of customers in the bakery department of a grocery store in the Slovak and the Spanish market.

The aim of this study is to examine subconscious/unconscious preferences in the selection of aromas suitable for the bakery department in the Slovak and the Spanish market. In this case, it is not a classical qualitative sensory testing of the perception of fragrances. The aim is to identify the associations of scents related to the selected sales department through images of the selected aromas. A special platform is used to obtain subconscious/unconscious feedback, which allows online collection of implicit feedback using the software FaceReader 7.

The authors noticed the different moods of the two groups of respondents when they answered the question about what they associate with the smell of bakery products. The Spanish respondents were slightly pleasantly disposed, while the Slovak respondents were slightly unpleasantly disposed. The smell of bakery products evoked more memories and emotions in the Spanish respondents than in the Slovak respondents, which can be explained by the higher pleasant mood. The main contribution of this work lies in the new opportunities to obtain feedback that can be used in marketing research and that rely not only on explicit but also implicit data. The extension of the methodological apparatus to implicit feedback presupposes some form of control of the data collected by the questionnaire. The use of biometric tools can represent an efficient alternative in terms of time and money to the use of neuroimaging tools in the selection/research of aromas for specific stores/departments.

It must be noted that the sample is small, and adequate conclusions cannot be made about entire population. Based on empirical findings and pandemic-related limitations, the authors plan to conduct similar research with real aroma samples and with even larger sample of tested respondents, considering weather, season, olfactory sensitivity (anosmia, hyposmia and normosmia) and participant fatigue (beginning and end of the week).

Today, marketers are facing the greatest challenge of how to attract consumers’ attention. Every individual has a different perception of the shopping environment based on his own experience, beliefs and attitudes. This is why new marketing techniques and approaches are becoming increasingly popular in the marketing environment.

El objetivo de esta investigación es examinar FaceReader como una herramienta para comparar las preferencias olfativas entre dos países. Concretamente, examinamos las preferencias olfativas de los clientes en el departamento de panadería de un supermercado en el mercado eslovaco y español.

El objetivo de este estudio es examinar las preferencias subconscientes/inconscientes en la selección de aromas adecuados para el departamento de panadería en el mercado eslovaco y español. En este caso, no se trata de una prueba sensorial cualitativa clásica de la percepción de fragancias. El objetivo es identificar las asociaciones de olores relacionados con el departamento de ventas seleccionado a través de imágenes de los aromas seleccionados. Se utiliza una plataforma especial para obtener comentarios subconscientes/inconscientes, que permite la recopilación en línea de comentarios implícitos utilizando el software FaceReader 7.

Observamos diferentes estados de ánimo de los dos grupos de encuestados cuando respondieron a la pregunta sobre qué asociaban con el olor de los productos de panadería. Los encuestados españoles estaban ligeramente más predispuestos hacia aromas más agradables, mientras que los encuestados eslovacos estaban ligeramente más predispuestos hacia aromas menos agradables. El olor de los productos de panadería evocó más recuerdos y emociones en los encuestados españoles que en los eslovacos, lo que puede explicarse por el estado de ánimo. La principal contribución de este trabajo radica en las nuevas oportunidades para obtener comentarios que pueden ser utilizados en investigaciones de marketing y que no solo se basan en datos explícitos, sino también implícitos. La ampliación del aparato metodológico para obtener comentarios implícitos presupone algún tipo de control de los datos recopilados mediante el cuestionario. El uso de herramientas biométricas puede representar una alternativa eficiente en términos de tiempo y dinero al uso de herramientas de neuroimagen en la selección/investigación de aromas para tiendas/departamentos específicos.

Debe tenerse en cuenta que la muestra utilizada es pequeña y no se pueden extrapolar conclusiones para toda la población. Basándonos en los resultados empíricos y con las limitaciones relacionadas con la pandemia, planeamos realizar una investigación similar con muestras de aroma reales y con una muestra aún más grande de encuestados, considerando el clima, la temporada, la sensibilidad olfativa (anosmia, hiposmia, normosmia) y la fatiga de los participantes (inicio y fin de semana).

Hoy en día, los profesionales del marketing se enfrentan al gran desafío de cómo atraer la atención de los consumidores. Cada individuo tiene una percepción diferente del entorno de compra basada en su propia experiencia, creencias y actitudes. Es por eso que las nuevas técnicas y enfoques de marketing se están volviendo cada vez más populares en el entorno del marketing.

本文旨在探讨FaceReader在比较斯洛伐克和西班牙两个国家的顾客嗅觉偏好方面的效用。我们以斯洛伐克和西班牙市场一家食品杂货店的面点部门顾客为研究对象, 考察其嗅觉偏好。

本研究的目标是探讨在斯洛伐克和西班牙市场选择适合面点部门的香气时潜在的/无意识的偏好。与传统的定性感官测试不同, 我们旨在通过选定香气的图像识别与选定销售部门相关的气味的联想, 并通过FaceReader 7软件在线收集隐性反馈。

我们观察到两组受访者在回答关于面点产品气味联想时的心境差异。西班牙受访者略带愉悦, 而斯洛伐克受访者略带不悦。西班牙受访者对面点产品的气味引起的记忆和情感更为丰富, 这可能是由更高愉悦心境所解释的。该研究的主要贡献在于提供了在营销研究中利用反馈的新机会, 该反馈不仅依赖于明确的数据, 还依赖于隐性数据。将方法学工具扩展到隐性反馈的前提是以某种形式对问卷收集的数据进行控制。在为特定商店/部门选择/研究香气方面, 相对于使用神经影像工具在时间和金钱方面的花费, 生物测定工具的使用可以作为高效替代。

由于本研究的样本量较小, 因此不能对整个人口做出充分的结论。基于经验发现和受到大流行病限制, 我们计划进行类似研究, 使用真实的香气样本, 并考虑更大规模的受试者样本, 同时考虑到天气、季节、嗅觉敏感度（嗅觉缺失、嗅觉减退、正常嗅觉）和参与者疲劳程度（周初和周末）对受试者的影响。

当今, 营销人员面临着吸引消费者注意的最大挑战。每个个体根据其自身经验、信仰和态度对购物环境有着不同的感知。因此, 在营销环境中, 新的营销技术和方法变得越来越受欢迎。

The purpose of this study is to understand how neuroscientific tools are used and discussed in ongoing research on strategy in organizations.

The authors used a bibliometric study of bibliographic pairing to answer the research question. They collected data from the Web of Science and Scopus databases using the keywords “neuroscience*,” “neurostrategy*” and “neuroscientific*.”

This study presents a framework that relates fundamental aspects discussed in current research using neuroscientific tools: Neuroscience and its research tools in organizations; emotions and information processing; interdisciplinary application of neuroscientific tools; and moral and ethical influences in the leaders' decision-making process.

The inclusion of neuroscientific tools in Strategic Management research is still under development. There are criticisms and challenges related to the limitations and potential to support future research.

Despite recognizing the potential of neuroscientific tools in the mind and brain relationship, this study suggests that at this stage, because of criticisms and challenges, they should be used as support and in addition to other traditional research techniques to assess constructs and mechanisms related to strategic decisions and choices in organizations.

Neuroscientific methods in organizational studies can provide insights into individual reactions to ethical issues and raise challenging normative questions about the nature of moral responsibility, autonomy, intention and free will, offering multiple perspectives in the field of business ethics.

In addition to presenting the potential and challenges of using scientific tools in strategic management studies, this study helps create methodological paths for studies in strategic management.

Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly introduce a new classification approach called adaptive k-nearest neighbors (AKNN) for intelligent HAR using smartphone inertial sensors with a potential real-time implementation on smartphone platform.

The proposed method puts forward several modification on AKNN baseline by using kernel discriminant analysis for feature reduction and hybridizing weighted support vector machines and KNN to tackle imbalanced class data set.

Extensive experiments on a five large scale daily activity recognition data set have been performed to demonstrate the effectiveness of the method in terms of error rate, recall, precision, F1-score and computational/memory resources, with several comparison with state-of-the art methods and other hybridization modes. The results showed that the proposed method can achieve more than 50% improvement in error rate metric and up to 5.6% in F1-score. The training phase is also shown to be reduced by a factor of six compared to baseline, which provides solid assets for smartphone implementation.

This work builds a bridge to already growing work in machine learning related to learning with small data set. Besides, the availability of systems that are able to perform on flight activity recognition on smartphone will have a significant impact in the field of pervasive health care, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring.

The purpose of this study is to build and test an accurate offline model by using only a compact training data that can reduce the computational and memory complexity of the system. This provides grounds for developing new innovative hybridization modes in the context of daily activity recognition and smartphone-based implementation. This study demonstrates that the new AKNN is able to classify the data without any training step because it does not use any model for fitting and only uses memory resources to store the corresponding support vectors.