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Targeting the common functions of the Zhejiang Library website, elderly individuals were invited to complete six experimental tasks on the improved website interfaces, and subjective data (PAD emotion scale and usability evaluation) and objective data (eye movement data) were recorded to verify the effects of graphic layout and navigation position on the information-seeking experience of elderly individuals.

This study analyzes the effect of the graphic layout and navigation position of the Zhejiang Library’s website interface on the emotional state, perceived usability and information-seeking time of elderly individuals, with the aim of providing guidance and suggestions for the elderly-oriented reform of the public library website.

The experimental results show that the graphic layout has a significant effect on the emotional state and perceived usability of elderly individuals, and the navigation position has a significant effect on the information-seeking time; the interaction between graphic layout and navigation position exerts a significant effect on the information-seeking time of elderly individuals. The eye movement data show that elderly individuals have a better information-seeking experience when the top navigation bar and image-text matched arrangement are used for the interface layout.

This study adopts a new approach combining subjective data and eye movement data to evaluate the effect of the public library website’s interface layout on the information-seeking experience for older people. The findings can provide a theoretical basis and methodological support for the elderly-oriented reform of public library websites. They can also provide scientific design suggestions for age-friendly interface layouts of other Internet products and service applications.

This study aims to examine the impact of COVID-19 pandemics on subjective well-being (SWB) and mental health in Hungary, with a particular focus on the roles of socioeconomic and COVID-19-related factors in shaping these outcomes.

A cross-sectional online survey was conducted among 2,000 Hungarian adults from May 25 to June 8, 2021. The data was collected using standardized questionnaires, including the SWLS, GAD-7 and PHQ-9. Correlation analysis, one-way ANOVA and linear regression were used.

The study found significant anxiety (41.5%), depression (48.1%) and life dissatisfaction (41.9%) among 2,000 participants. Females reported higher anxiety and depression than males. Single individuals had the lowest life satisfaction. Higher income levels are correlated with greater life satisfaction. Quarantine and vaccination were linked to higher life satisfaction and lower psychological distress. The regression showed that older age, being married and higher income were linked to lower depression and anxiety. Severity of infection and quarantine increased both depression and anxiety. Infection reduced life satisfaction, but severe cases showed a slight improvement in satisfaction, highlighting complex health and well-being relationships.

The findings reveal complex relationships, including unexpected resilience in severe COVID-19 cases.

This study offers insights into the impact of the COVID-19 pandemic on SWB and mental health in Hungary. By analyzing socioeconomic, demographic and pandemic-related factors in a large sample, it highlights complex relationships between quarantine, infection severity, vaccination and well-being. The findings contribute to understanding how these factors influence mental health, informing targeted interventions during health crises.

Human beings are able to adjust their arm stiffness in daily life tasks. This paper aims to enable a robot to learn these human-like variable stiffness motor skills autonomously.

The paper presents a reinforcement learning method to enable a robot to learn variable stiffness motor skills autonomously. Firstly, the variable stiffness motor skills are encoded by the previously proposed dynamical movement primitives and stiffness primitives (DMP-SP) framework, which is able to generate both motion and stiffness curves for robots. The admittance controller is then used to make a robot follow the motion and stiffness curves. The authors then use the policy improvement with path integrals (PI2) algorithm to optimize the robot motion and stiffness curves iteratively.

The performance of the proposed method is evaluated on an UR10 robot by two different tasks: a) via-point task, b) sweeping the floor. The results show that after training, the robot is capable of accomplishing the tasks safely and compliantly.

The method can help the robots walk out of the isolated environment and accelerate their integration into human being’s daily life.

This paper uses reinforcement learning method to improve DMP-SP framework, thus allowing the robots to learn variable stiffness motor skills autonomously with no need for extra sensors.

Data transformation has prompted enterprises to rethink their strategic development. Scholars have frequently acknowledged the vast potential value of supply chain data and realised that simply owning data resources cannot guarantee excellent innovation performance (IP). Therefore, this study focussed on the mediating and moderating issues between data-driven supply chain orientation (DDSCO) and IP. More specifically, the purpose was to explore (1) whether DDSCO promotes enterprise innovation through dynamic and improvisational capabilities and (2) how information complexity (INC) plays a moderating role between capabilities and performance.

An empirical study was performed using the results of a questionnaire survey, and a literature review was used to build the premises of this study. A sample was conducted on 296 Chinese enterprises, and the data collected were used to test the hypothesis by successive regression.

This research has implications for the theoretical development of DDSCO, as well as the dynamic capabilities (DC) and improvisation capabilities (IC) in innovation strategic literature. The empirical results show that DDSCO has a direct, positive impact on both DC and IC, which thus positively impact IP. Meanwhile, IC has a negative moderating effect on the path joining DC and IP. Conversely, IC has a positive moderating effect on the path joining IC and IP.

Although this study has limitations, it also creates opportunities for future research. The survey comes from different industries, so the possibility of unique influences within industries cannot be ruled out. Second, the authors' survey is based on cross-sectional data, which allow for more comprehensive data verification in the future. Third, this study also provides opportunities for future research, because it proves that DC and IC, as partial mediators of DDSCO and IP, can mine other paths of the data-driven supply chain in IP. For example, the perspective of the relationship between supply chain members, knowledge perspective, etc.

The research findings offer a novel perspective for enterprise managers. First, enterprises can leverage supply chain data to gain competitive advantages in innovation. Second, it is imperative for enterprises to acknowledge the significance of developing dynamic and IC. This also requires enterprises to acknowledge innovations in DDSCO necessitate a focus on dynamic and IC. Third, it is recommended that managers take into account both sides of IC and encourage enterprises to prioritise the utilisation of IC.

Empirical research results revealed how DDSCO improves IP and is an extension of digital transformation in the supply chain field, providing new opportunities and challenges for enterprise innovation. It can also expand the enterprise's understanding of DDSCO. Second, based on resource-based theory, it is possible to develop and test theoretical arguments regarding the importance of dynamic and IC as intermediaries in the DDSCO-IP. Third, the authors conducted simulations of highly dynamic data environments to develop and test theoretical arguments about the importance of IC as a moderator of capabilities-performance relationships.

Although many countries are focusing on the management of construction and demolition waste (CDW) resource utilization, the effect of risk aversion of the green innovation-led enterprise on the performance of the CDW resource utilization supply chain is unclear when considering different green innovation contexts (green innovation led by the building materials remanufacturer or by the construction waste recycler). This study aims to investigate how the level of risk aversion of the green innovation-led enterprise affects CDW resource utilization under different green innovation contexts based on contingency theory.

Using Stackelberg game theory, this study establishes a decision model consisting of a building materials remanufacturer, construction waste recycler and CDW production unit and investigates how the level of risk aversion of the green innovation-led enterprise under different green innovation contexts influences the performance level of the supply chain.

The conclusions are as follows. (1) For the green innovation-led enterprise, the risk-averse behaviour is always detrimental to his own profits. (2) For the follower, the profits of the construction waste recycler are negatively correlated with the level of risk aversion of the green innovation-led enterprise in the case of a small green innovation investment coefficient. If the green innovation investment coefficient is high, the opposite result is obtained. (3) When the green innovation investment coefficient is low, the total supply chain profits decrease as the level of risk aversion of the green innovation-led enterprise increases. When the green innovation investment coefficient is high, total supply chain profit shows an inverted U-shaped trend with respect to the degree of risk aversion of the green innovation-led enterprise.

(1) This study is the first to construct a green innovation context led by different enterprises in the CDW resource utilization supply chain, which provides a new perspective on green management and operation. (2) This study is the first to explore the operation mechanism of the CDW resource utilization supply chain based on contingency theory, which provides new evidence from the CDW resource utilization supply chain to prove contingency theory. At the same time, this study examines the interactive effects of the green innovation cost coefficient and the degree of risk aversion of green innovation-led enterprises on the performance of supply chain members, expanding the contingency theory research on contingencies affecting enterprise performance. (3) This study will guide members of the CDW resource utilization supply chain to rationally face risks and achieve optimal supply chain performance.

This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.

The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.

The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.

The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.

In today’s global and competitive e-commerce market spaces, social media influencers (SMIs) exert substantial influence on consumer behavior. This study aims to examine how electronic word of mouth (e-WOM), Instagram usage and the credibility of SMIs shape the dynamics of consumer purchase behavior (PB).

Information was gathered from 498 users in Ghana through judgmental sampling using SmartPLS 4.

The findings revealed that influencers’ credibility has a substantial impact on their followers’ parasocial interactions. As a promotional tool, Instagram plays a significant role in how followers perceive the credibility of influencers by modifying the associations between parasocial connections, e-WOM and consumer PB.

The findings offer valuable information for marketing professionals looking to improve their advertising efforts by collaborating with influencers, along with unique perspectives on influencer dynamics in a diverse socioeconomic context, extending beyond conventional boundaries.

Through an examination of the complex mechanisms underlying social media influencer advertisements on an e-commerce platform, namely, Instagram, this research uncovered the essence of customer behavior in the digital era, including the human need for connection, authenticity and trust, thus contributing to the literature empirical data from Africa, a region often overlooked in academic studies.

在当今全球化且竞争激烈的电子商务市场中, 社交媒体影响者对消费者行为有着重要的影响。本研究旨在探讨网络口碑、Instagram使用以及社交媒体影响者的可信度如何塑造消费者购买行为的动态变化。

使用SmartPLS 4, 通过判断抽样收集了来自加纳的 498 位用户信息。

研究表明, 影响者的可信度对其粉丝的准社会互动有着显著影响。作为一种推广工具, Instagram通过影响准社会联系、网络口碑和消费者购买行为之间的关系, 在粉丝感知影响者可信度方面发挥着重要作用。

本研究通过深入剖析社交媒体影响者在Instagram等电子商务平台上进行广告活动的复杂机制, 揭示了数字时代消费者行为的本质, 其中包括人们对联系、真实性和信任的需求。此外, 研究还提供了经常被学术界忽视的非洲地区的实证数据。

研究结果为希望通过与影响者合作来提升广告效果的营销专业人士提供了宝贵的信息。本文超越传统界限, 从不同的社会经济背景下提供了影响者动态的独特视角。

En un mercado tan global y competitivo como el del comercio electrónico actual, los influencers en redes sociales ejercen una influencia sustancial en el comportamiento del consumidor. Este estudio examina cómo el boca a boca electrónico, el uso de Instagram y la credibilidad de los influencers en redes sociales dan forma a la dinámica del comportamiento de compra del consumidor.

Se recogió información de 498 usuarios en Ghana mediante muestreo de conveniencia y se testó el modelo utilizando SmartPLS 4.

Los resultados revelaron que la credibilidad de los influencers tiene un impacto sustancial en las interacciones parasociales con sus seguidores. Como herramienta promocional, Instagram juega un papel significativo en la percepción de los seguidores sobre la credibilidad de los influencers, modificando las asociaciones entre las conexiones parasociales, el boca a boca electrónico y, finalmente, el comportamiento de compra del consumidor.

A través del estudio de los complejos mecanismos que subjacen en los anuncios y publicaciones de los influencers en Instagram, esta investigación desveló la esencia del comportamiento del cliente en la era digital, incluyendo la necesidad de conexión humana la conexión, la autenticidad y la confianza, contribuyendo así a la literatura con datos empíricos de África, una región a menudo pasada por alto en estudios académicos.

Los resultados ofrecen información valiosa para los profesionales del marketing que buscan mejorar sus esfuerzos publicitarios colaborando con influencers, junto con perspectivas únicas sobre la dinámica de éstos en un contexto socioeconómico diverso, más allá de los límites convencionales.

The purpose of this study is to propose and test an extended theory of planned behavior model to explain the significance of social media influencers’ credibility, social media usage (SMU) and social media marketing while simultaneously examining females’ intentions to purchase cosmetics online.

An online questionnaire was used to collect data from 386 female consumers of cosmetics, and the data were analyzed using partial least squares structural equation modeling (PLS-SEM) with the help of SmartPLS version 4.

First, the results indicated that SMU, social media influencers’ credibility and social media marketing significantly and positively impact the theory of planned behavior components, namely, attitude, subjective norms (SNs) and perceived behavioral control (PBC). Second, results confirmed that attitude, SNs and PBC significantly and positively influenced female consumers’ online purchase intentions (OPIs).

To the best of the authors’ knowledge, this study is one of the first to examine an integrated model that consisted of SMU, social media marketing and social media influencers along with the theory of planned behavior constructs in their proposed research model in the context of cosmetics in India. The study also enriched the body of knowledge about using the PLS-SEM approach to predict OPI for cosmetics.

The aim of this paper is to explore how information shared by SMIs affects consumers' food tasting intentions. To achieve this, it integrates the IAM and TAM, in conjunction with trust and EWOM.

This paper utilized a convenience sampling technique, employing a survey instrument to gather data online. The questionnaire was distributed across the social media pages of food bloggers from September 11 to November 30, 2023. The collected data was analyzed using SPSS and AMOS.

We developed a research framework that integrates IAM, TAM, Trust, and EWOM variables to assess how information shared by SMIs influence consumers' intentions to explore new food tastes. The model demonstrated enhanced predictive and explanatory capabilities.

This study enriches the existing literature on information adoption and technology acceptance by advancing our understanding of how SMIs influence consumers’ food tasting intentions. Additionally, it aids SMIs in comprehending their role in endorsing new food products and restaurants, fostering trust and reliability among their followers. This study enables consumers to make more informed decisions about trying new food products or dining establishments, empowering them to evaluate influencer recommendations critically.

This study uniquely focuses on the influence of information shared by SMIs on consumers' intentions to taste new foods. While SMIs have been extensively studied in various contexts, such as fashion, beauty, and travel, this research offers a fresh perspective on understanding their impact on consumer behavior within the food industry.