Background: In modern society, the use of computers accounts for a large proportion of our daily lives. Although substantial research is being actively conducted on musculoskeletal diseases resulting from computer use, there has been a recent surge in interest in improving the working environment for prevention. Objects: This study aimed to examine the effects of posture correction feedback (PCF) on changes in neck posture and muscle activation during computer typing. Methods: The participants performed a computer typing task in two sessions, each lasting 16 minutes. The participant’s dominant side was photographed and analyzed using ImageJ software to verify neck posture. Surface electromyography (EMG) was used to confirm the participant’s cervical erector spinae (CES) and upper trapezius muscle activities. The EMG signal was analyzed using the percentage of reference voluntary contraction and amplitude probability distribution function (APDF). In the second session, visual and auditory feedback for posture correction was provided if the neck was flexed by more than 15° in the initial position during computer typing. A 20-minute rest period was provided between the two sessions. Results: The neck angle (p = 0.014), CES muscle activity (p = 0.008), and APDF (p = 0.015) showed significant differences depending on the presence of the PCF. Furthermore, significant differences were observed regarding the CES muscle activity (p = 0.001) and APDF (p = 0.002) over time. Conclusion: Our study showed that the feedback system can correct poor posture and reduces unnecessary muscle activation during computer work. The improved neck posture and reduced CES muscle activity observed in this study suggest that neck pain can be prevented. Based on these results, we suggest that the PCF system can be used to prevent neck pain.

Background: Office workers experience neck or back pain due to poor posture, such as flexed head and forward head posture, during long-term sedentary work. Posture correction is used to reduce pain caused by poor posture and ensures proper alignment of the body. Several assistive devices have been developed to assist in maintaining an ideal posture; however, there are limitations in practical use due to vast size, unproven long-term effects or inconsistency of maintaining posture alignment. We developed a headphone and necklace posture correction system (HANPCS) for posture correction using an inertial measurement unit (IMU) sensor that provides visual or auditory feedback. Objects: To demonstrate the test-retest reliability and concurrent validity of neck and upper trunk flexion measurements using a HANPCS, compared with a three-dimensional motion analysis system (3DMAS). Methods: Twenty-nine participants were included in this study. The HANPCS was applied to each participant. The angle for each action was measured simultaneously using the HANPCS and 3DMAS. The data were analyzed using the intraclass correlation coefficient (ICC) = [3,3] with 95% confidence intervals (CIs). Results: The angular measurements of the HANPCS for neck and upper trunk flexions showed high intra- (ICC = 0.954–0.971) and inter-day (ICC = 0.865–0.937) values, standard error of measurement (SEM) values (1.05°–2.04°), and minimal detectable change (MDC) values (2.92°–5.65°). Also, the angular measurements between the HANPCS and 3DMAS had excellent ICC values (> 0.90) for all sessions, which indicates high concurrent validity. Conclusion: Our study demonstrates that the HANPCS is as accurate in measuring angle as the gold standard, 3DMAS. Therefore, the HANPCS is reliable and valid because of its angular measurement reliability and validity.

This paper explores the relationships of various cognitive, attitudinal, and behavioural responses of e-discount sales promotion technique preferences consumers have when making purchases through online food delivery services (OFDS). This paper highlights new findings towards expanding e-discount and sales promotion literature and thus provide relevant implications.

This research investigated the effects of para-social interaction (PSI) in online and traditional celebrity endorsements. The data was collected by distributing questionnaire sets. And covariance structural equation modeling was used to analyze the model. The findings showed that para-social interaction is the moderator variable in the online celebrity endorsement effect.

In this research, given the recent spurt of luxury brands featuring their products in the metaverse, we examine if the use of luxury brand items to enhance one’s virtual avatar augments the gamer’s sense of confidence, empowerment, and actual performance. We conduct two studies to test the same. In our first study, we simulated a gaming environment on an online survey portal and asked participants to choose their avatar features. We provided them with a branded item (luxury/regular). Consequently, we asked them to indicate their level of confidence, and empowerment. They were asked to play a game to record their actual performance. In our second study, we asked participants to choose their avatar on an online game on their mobiles and similarly provided them with a branded item (luxury/regular). We then measured their confidence, and empowerment and observed their actual performance.

Numerous studies suggested that Generation Z (Gen Z) is an age group that seeks individuality more than other generations. Their aesthetics, identity, and belief must represent their self-value. The consumption of fashion was selected as a focus of this study. Our consumption of fashion represents our value, and we construct and present our identity with physically what we wear when we face society daily. Looking at the fashion styles of Generation Z, this study aims to examine whether Generation Z in Hong Kong practice individuality or conformity in the course of their fashion consumption behaviour by a preliminary study. Generation Z perceives their fashion consumption behaviour to reflect their individualism and values. However, the findings suggest that there is a high degree of conformity among Generation Z's fashion consumption behaviour. This conformity is observed in the creation of a distinctive fashion style, which is influenced by the cultural aesthetics, values, and preferences of Generation Z, resulting in a unifying effect that goes unnoticed by the individuals. This complex formation of Generation Z fashion consumption behaviour has a complex implication to fashion marketing, where both the sense of exclusivity and conformity must both be exhibited to satisfy the consumption needs of Generation Z.

Social media began to boom in the 2000s. According to statistics, there are 92.3% of internet users are social media users who spend nearly two and a half hours daily on social media. Instagram is considered the most popular image-sharing platform, with more than 1.2 billion monthly active users. Meanwhile, consumers nowadays refer to and follow online reviews to assist their decision-making process more efficiently.

This paper aims to examine the effect of AI framing (i.e., Scientific AI vs Magic AI) on consumers' product evaluation. This study shows that magically framed AI technology may be more beneficial to appeal to product innovativeness when subjective product properties (e.g., personal taste) becomes important. On the contrary, when objective product properties (e.g., functionality) becomes important, scientifically framed AI technology is more likely to generate higher perceived product attractiveness and purchase intention.

In this paper, we propose a new neural network architecture for item recommendation with structural information. Our model, structural neural recommender (SNR) is based on neural networks and operates on a hierarchy paradigm, aiming to explore the effectiveness of incorporating different structural information for recommendation. Many recent state-of-the-art neural network based recommendation models exploit the nonlinear transformations for modeling the complex user-item interaction patterns and user historical behaviors, ignoring the item-item structural relationship. This structural information, however, is uncomplicated to derive and useful for inferring item characteristics. To utilize this information, SNR simultaneously learns representation from user-item interactions and item-item relationships. Empirical studies on eight real-world datasets demonstrate the effectiveness of incorporating such structural information, by outperforming classic and recent baselines. We also conduct detail ablation studies and hyper-parameter analysis to provide further understanding towards the behaviors of our model. Following the model development, we conduct a field experiment to demonstrate that the effectiveness of algorithmic recommender systems can further increase by using different types of message framing when communicating recommendations to consumers. Our results suggest that recommendations framed with a relevance appeal (e.g. “Top 5 brands for you”) are more effective in general, yet recommendations that are framed with a popularity appeal (“Top 5 most popular brands”) are more effective for customers who were acquired via social media (versus non-social media) advertising or for those who have stronger (versus weaker) social orientation.

The present study choose to conduct consumer behavioral research in Metaverse situation, explores factors that influence consumer shopping enjoyment and purchase intention from product, service and technology perceptive. The research team gathered the primary data through questionnaire subjecting to Chinese consumers (n=300) who know about TaoBao future city which themes on virtual buying in Metaverse. In addition, structural equation modeling is employed to examine the hypothesized relationship among the variables. The result shows that all the driving factors positive effects consumer shopping enjoyment and then influence purchase intention positively. The finding is significantly fundamental to establish theoretical framework future about virtual shopping in Metaverse, and help marketers realize how to set virtual stores in Metaverse to enhance consumer shopping experience so that they could improve consumer purchase intention in the context.

The extensive application of robots in hospitality and tourism service has transformed the original human-contact into contact-less, so it is necessary to understand the transformation of customers consumption behaviors under this new service mode. While studies have started investigating how service robots enhance the consumer autonomy, the impact of such technology on customers consumption behaviors remains largely unexplored and its underlying mechanism are still unclear. To address this issue, we explore how service robots shape customers autonomous behaviors in hospitality and tourism services. Drawing on the social impact theory, we presented an underlying process in terms of social discomfort, and reveal the boundary conditions.

Crowdfunding is an increasingly popular fundraising tool where project creators solicit capital from potential backers in return for monetary rewards or presales of products/services. Potential backers in crowdfunding are looking for potential cues to reduce uncertainty and predict new venture success when making their capital contributions. To increase their funding success, project creators strategically use project descriptions as a marketing tool to attract potential backers and funding.

The purpose of this paper is to explore underlying antecedents for inconspicuous luxury motivations by developing and testing a conceptual model under the self-concept mechanism with the influence of cultural capital. A questionnaire-based survey is used to collect data, and structural equation modeling is applied to analyze the data. Although growing interests in academia focus on the relationship between cultural capital and inconspicuous luxury, quantitative studies are scant. This research contributes to the literature by empirically investigating the impact of cultural capital in the relationship within the construct of consumers’ self-concept and inconspicuous luxury motivations.

Over the past decade, in-feed native advertising has become one of the most popular forms of online advertising, attracting attention from both academia and industry. Compared to traditional online ads, in-feed native ads are less intrusive and have more engaging content, effectively reducing users' tendency to avoid ads and improving advertising effectiveness. Despite existing review studies, most of them focus on specific aspects of in-feed native advertising, such as ad identification, disclosure, or regulation, and lack an overview of the relevant literature. Moreover, with the diversified development of digital media forms, the widespread application of AI technologies, and the improvement of consumers' ad literacy, in-feed native ad research faces new issues and practical challenges. Thus, a systematic review of existing research is necessary to identify valuable future research directions.

Metaverse blends the physical and virtual worlds, transforming the customer's shopping experience. This study aims to identify the psychological mechanism in the metaverse environment and the relationship between metaverse experience and consumer happiness. To identify metaverse experiences, both behavioral and functional magnetic resonance imaging (fMRI) studies were conducted. In a behavioral study, we found that consumers' happiness increased when participants were in an immersive metaverse space. In the fMRI study, we found greater activation in the medial prefrontal cortex (MPFC) and lateral occipital cortex (LOC) regions in the high level of the immersive metaverse and found a positive relationship with consumers' happiness. This paper is the first attempt in marketing to provide an integrative brain map for the metaverse experience. This brain map helps marketers better understand the consumer experience. This study suggests that only in the immersive metaverse space where virtual and physical experiences interact can consumers become one with the virtual space and maximize customer experience values.

The GMT-Consortium Large Earth Finder (G-CLEF) is the first instrument for the Giant Magellan Telescope (GMT). G-CLEF is a fiber feed, optical band echelle spectrograph that is capable of extremely precise radial velocity measurement. G-CLEF Flexure Control Camera (FCC) is included as a part in G-CLEF Front End Assembly (GCFEA), which monitors the field images focused on a fiber mirror to control the flexure and the focus errors within GCFEA. FCC consists of an optical bench on which five optical components are installed. The order of the optical train is: a collimator, neutral density filters, a focus analyzer, a reimager and a detector (Andor iKon-L 936 CCD camera). The collimator consists of a triplet lens and receives the beam reflected by a fiber mirror. The neutral density filters make it possible a broad range star brightness as a target or a guide. The focus analyzer is used to measure a focus offset. The reimager focuses the beam from the collimator onto the CCD detector focal plane. The detector module includes a linear translator and a field de-rotator. We performed thermoelastic stress analysis for lenses and their mounts to confirm the physical safety of the lens materials. We also conducted the global structure analysis for various gravitational orientations to verify the image stability requirement during the operation of the telescope and the instrument. In this article, we present the opto-mechanical detailed design of G-CLEF FCC and describe the consequence of the numerical finite element analyses for the design.

The development of functional carbon materials using waste biomass as raw materials is one of the research hotspots of lithium-sulfur batteries in recent years. In this work, used a natural high-quality carbon source—coffee grounds, which contain more than 58% carbon and less than 1% ash. Honeycomb-like S and N dual-doped graded porous carbon (SNHPC) was successfully prepared by hydrothermal carbonization and chemical activation, and the amount of thiourea used in the activation process was investigated. The prepared SNHPC showed excellent electrochemical energy storage characteristics. For example, SNHPC-2 has a large pore volume (1.85 cm3·g− 1), a high mesoporous ratio (36.76%), and a synergistic effect (S, N interaction). As the cathode material of lithium-sulfur batteries, SNHPC-2/S (sulfur content is 71.61%) has the highest specific capacity. Its initial discharge-specific capacity at 0.2 C is 1106.7 mAh·g−1, and its discharge-specific capacity after 200 cycles is still as high as 636.5 mAh·g−1.

In this work, subabul wood biomass was used to prepare carbon adsorbents by physical and chemical activation methods at various carbonization temperatures. The properties of the carbon adsorbents were estimated through characterization techniques such as X-ray diffraction, Fourier transform infrared spectroscopy, X–ray photo electron spectroscopy, laser Raman spectroscopy, scanning electron microscopy, CHNS-elemental analysis and N2 adsorption studies. Subabul-derived carbon adsorbents were used for CO2 capture in the temperature range of 25–70 °C. A detailed adsorption kinetic study was also carried out. The characterization results indicated that these carbons contain high surface area with microporosity. Surface properties were depended on treatment method and carbonization temperature. Among the carbons, the carbon prepared after treatment of H3PO4 and carbonization at 800 °C exhibited high adsorption capacity of 4.52 m.mol/g at 25 °C. The reason for high adsorption capacity of the adsorbents was explained based on their physicochemical characteristics. The adsorbents showed easy desorption and recyclability up to ten cycle with consistent activity.

The present studies explored the possibility of immobilizing phosphocholine (PC) liposomes on the surface of graphene oxide (GO) which was pre-adsorbed with two kinds of enzymes, horseradish peroxidase and glucose oxidase. The transmission electron microscope images showed that the PC liposomes adsorbed onto the GO surface kept integrity. By using 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS)-encapsulated liposomes, a one-step colorimetric assay for glucose was developed. In the presence of glucose, the GO nanocomposites catalyzed the cascade enzymatic reaction producing colorimetric signals directly. Under the optimal conditions, the GO nanocomposites produced linearly increased colorimetric signal with increased concentrations of glucose ranging from 50 to 500 μM. The detection limit was 33 μM. The GO nanocomposites also exhibited good selectivity for the detection of glucose and were able to detect glucose in human serum.

Surface free energy is an important parameter in surface and interface properties of fiber reinforced polymer composite. The BET (Brunauer, Emmett, and Teller) surface area and surface energy of the sample can be obtained by Inverse Gas Chromatography (IGC) based on the adsorption principle. In this paper, surface energy of carbon fiber bundle was tested by means of IGC under different conditions to find reliable test parameters. The main parameters involved include length, mass, and packing density of sample, target fractional surface coverage, flow rate, and maximum elution time. It is demonstrated that IGC has the advantages of simple sample preparation, stable test data, high automation, and high sensitivity for carbon fiber. Among all test conditions, packing density and flow rate have the greatest influences on the experimental results. The optimized test parameters are suitable for various kinds of carbon fiber bundles, including polyacrylonitrilebased and pitch-based carbon fibers with different tensile properties and tow sizes. Moreover, IGC can acutely characterize the surface properties of carbon fibers after carbon nanotube modification and heat treatment, which are hard to carry out using contact angle method.