Privacy and security are among the salient factors that prevent older adults from adopting information communication technologies. Using the original APCO model (Antecedents, Privacy Concern, Outcomes), we conducted a PRISMA review on older adults’ perceptions of technology. This commentary draws upon findings from the PRISMA review and then proposes expansions to the APCO framework. More specifically, we advance that technology type is an antecedent to privacy concerns that contribute to older adults’ willingness to use or adopt technology. We also aggregate specific privacy and security concerns that older adults expressed are barriers to their technology use. Because these concerns vary according to technology type, we highlight methodological challenges for consideration in future research that investigates contextual relationships between technology and privacy-related factors.

Products made from recycled materials, as an environmentally friendly option, have attracted public attention. Understanding public attitudes and preferences towards such products is crucial for their market development. In order to explore the Chinese residents’ attitude and emotional tendency, we analyzed public discussions and topics on products made from recycled materials from Sina Weibo. Meanwhile, LDA and BERT deep learning models were employed to assess the level of public attention, hot topics, and sentiment tendencies in China. Results show a significant upward trend in public interest in products made from recycled materials. Although the fact that most people have a positive attitude towards the environmental concept of products made from recycled materials and support such products, there is not a significant demand for products made from recycled materials, i.e., there is a green attitude-behaviour gap. Results further reveal that factors such as the price of products made from recycled materials, branding, and public skepticism about the sustainability of the products are the main reasons for the gap between environmental awareness and consumer behaviour. By analyzing the public's cognitive structure and psychological characteristics regarding products made from recycled materials through social media data, this paper can help recycling manufacturers understand consumers' needs and expectations, as well as assist policymakers in formulating better circular economy policies.

Widespread public opinion of food safety (POFS) has exacerbated consumer concerns about food safety and health, then driving consumers to purchase green food. However, the mechanisms at play in such concerns to consumer behaviour towards green food consumption have not explored, especially in the environment of social media. To fill up this gap, this research adopted a sequential two-stage mixed-methods approach to identify and examine the effects and mechanisms of public opinion of food safety on consumers' green food purchase intentions. In the initial qualitative research phase, the public comments on the microblogging platform was analyzed and we identified four top hot topics: public opinion of food safety, insecurity, green label trust (GLT) and green food purchase intentions (GFPI) based on text mining. In the quantitative phase of the survey-based research, we introduced the protection motivation theory and the hyper-attention cognitive model to construct a conceptual model. Based on this conceptual model, we conducted an empirical test with 1087 online questionnaires. The result showed that POFS had a significant positive effect on consumers' GFPI; consumer insecurity mediated the relationship between POFS and GFPI, and GLT played a moderating role between them. This study illustrates a psychological mechanism by which public opinion of food safety motivates consumers to switch to green food and provides new insights into green food consumption behaviour. It is an essential reference for encouraging green food consumption and promoting sustainability in the food industry.

Researchers have investigated whether machine learning (ML) may be able to resolve one of the most fundamental concerns in personnel selection, which is by helping reduce the subgroup differences (and resulting adverse impact) by race and gender in selection procedure scores. This article presents three such investigations. The findings show that the growing practice of making statistical adjustments to (nonlinear) ML algorithms to reduce subgroup differences must create predictive bias (differential prediction) as a mathematical certainty. This may reduce validity and inadvertently penalize high-scoring racial minorities. Similarly, one approach that adjusts the ML input data only slightly reduces the subgroup differences but at the cost of slightly reduced model accuracy. Other emerging tactics involve weighting predictors to balance or find a compromise between the competing goals of reducing subgroup differences while maintaining validity, but they have been limited to two outcomes. The third investigation extends this to three outcomes (e.g., validity, subgroup differences, and cost) and presents an online tool. Collectively, the studies in this article illustrate that ML is unlikely to be able to resolve the issue of adverse impact, but it may assist in finding incremental improvements.

Analyzing the perception differences and influencing factors of cross-cultural groups in heritage tourism can help heritage sites to formulate differentiated service and improve tourist satisfaction. This research adopted the BERT model to undertake sentiment analysis of 17,555 Chinese online reviews for nine scenic spots in Melaka. Using vocabulary filtering, co-occurrence analysis, and semantic clustering technology, the emotional characteristics of Chinese outbound tourists when they visited heritage sites in Melaka were analyzed, which revealed the factors influencing their positive and negative emotions. Results showed that: 1. The BERT-based deep learning approach can obtain improved sentiment predictive performance. 2. Chinese tourists’ general emotional perceptions of Melaka were positive and they were very interested in heritage sites. 3. The most important reason for the negative emotions of Chinese tourists was a lack of cultural experience in Melaka. This research expands the application of deep learning in the field of tourism, and it helps heritage tourism destinations to improve their marketing plans for Chinese tourists and achieve long-term sustainable development of the destination.

The COVID-19 pandemic increased public health awareness, changing consumers’ sensitivity and beliefs about food health. Food anxiety and health scares turn consumers toward safe and healthy foods to strengthen their immunity, which makes green food more popular. However, it remains unclear how to understand the gap between consumer intention to purchase green food and their actual purchasing behaviour. Taking rice as an object of study, comparing differences in consumer perceptions and emotions towards green-labelled rice and conventional rice is beneficial for understanding the components and psychological characteristics of consumer perceptions of green food. Therefore, we used topic modelling and sentiment analysis to explore consumers’ focus of attention, attitudinal preferences, and sentiment tendencies based on the review (n = 77,429) from JD.com. The findings revealed that (1) consumers’ concerns about green-labelled rice are increasing rapidly, and most have a positive attitude; (2) consumers of green-labelled rice are more concerned about origin, aroma, and taste than conventional rice; (3) consumers of conventional rice are more concerned about the cost-performance ratio, while consumers of green-labelled rice are also price-sensitive; (4) green label mistrust and packaging breakage during logistics are the leading causes of negative emotions among consumers of green-labelled rice. This study provides a comparative analysis of consumer perceptions and emotions between the two types of rice, thus revealing the main influencing factors of the intention-behaviour gap and providing valuable consumer insights for the promotion of green consumption and the sustainable development of the green food industry.

Travel digital footprints can be used to analyze tourists’ spatio-temporal behavior to customize travel plans and recommendations. This study focuses on Chinese tourists in Malaysia as the research subject and uses the tourism digital footprint of Chinese tourists’ travel texts on Qunar.com as the primary data source. It connects traditional quantitative analysis with complex network analysis to study tourists’ behavior, time pattern, and complex network effects. The research results were as follows: (1) there was an uneven distribution of core tourism nodes with structural holes in Malaysia, which form a network pattern of imbalanced power and fierce internal competition; (2) the digital footprints of Chinese tourists were mainly concentrated in traditional tourist hot spots in Malaysia; (3) besides the competition between domestic islands, Thailand and Singapore are Malaysia’s main competitors for Chinese tourists. These results provide helpful information for the tourism management departments of Malaysia to improve their marketing and development efforts directed for Chinese tourists.

During the COVID-19 pandemic, lockdowns and isolation have limited the availability of face-to-face support services for victims of intimate partner violence (IPV). Despite the growing need for online help in supporting IPV victims, far less is known about the underlying mechanisms between IPV and online help-seeking. We studied the mediating role of emotion dysregulation (ED) and the moderating role of perceived anonymity (PA) on the internet to explain IPV victims' willingness of online help-seeking (WOHS). Through a PROCESS analysis of the questionnaire data (n = 510, 318 female, 192 male, Mage = 22.41 years), the results demonstrate that: (1) ED has been linked with the experience of IPV, and IPV significantly induces ED. (2) When IPV victims realize the symptoms of ED, they have a strong willingness to seek external intervention to support themselves. ED mediates the relationship between IPV and online help-seeking. (3) For youth growing up in the era of social networking sites (SNS), personal privacy protection is an important factor when seeking online help. The anonymity of the internet has a positive effect on victims who experience IPV and ED, and it increases WOHS. This study introduces a new perspective on the psychological mechanism behind IPV victims' help-seeking behaviors, and it suggests that the improvement of anonymity in online support can be an effective strategy for assisting IPV victims.

The mobile internet has resulted in intimate partner violence (IPV) events not being viewed as interpersonal and private issues. Such events become public events in the social network environment. IPV has become a public health issue of widespread concern. It is a challenge to obtain systematic and detailed data using questionnaires and interviews in traditional Chinese culture, because of face-saving and the victim's shame factors. However, online comments about specific IPV events on social media provide rich data in understanding the public's attitudes and emotions towards IPV. By applying text mining and sentiment analysis to the field of IPV, this study involved construction of a Chinese IPV sentiment dictionary and a complete research framework. We analyzed the trends of the Chinese public's emotional evolution concerning IPV events from the perspectives of a time series as well as geographic space and social media. The results show that the anonymity of social networks and the guiding role of opinion leaders result in traditional cultural factors such as face-saving and family shame for IPV events being no longer applicable, leading to the spiral of an anti-silence effect. Meanwhile, in the process of public emotional communication, anger often overwhelms reason, and the spiral of silence remains in effect in social media. In addition, there are offensive words used in the IPV event texts that indicate misogyny in emotional, sexual, economic and psychological abuse. Fortunately, mainstream media, as crucial opinion leaders in the social network, can have a positive role in guiding public opinion, improving people's ability to judge the validity of network information, and formulating people's rational behaviour.

Knowledge payment is a new type of E-learning that has developed in the era of social media. With the influence of the COVID-19 epidemic, the knowledge payment market is developing rapidly. Exploring the influencing factors of users' continuance intention is beneficial for the sustainable development of knowledge payment platforms. Our study took "Himalayan FM" as an example and included two studies: Study 1 used latent dirichlet allocation (LDA) to explore the main factors affecting the users' willingness to continue use, through mining user comment data on the knowledge payment platform; Study 2 constructed the conceptual model by integrating the technology acceptance model (TAM) and IS success model (IS) and carried out empirical analysis by SPSS and AMOS using the data that were collected through the questionnaire. The results show that: (1) perceived usefulness, user satisfaction, and spokesperson identity have a direct positive impact on users' willingness to continuous use, while perceived cost has a direct negative impact on users' willingness to continue use; (2) perceived ease of use, content quality, and system quality of knowledge payment platforms impacted user satisfaction directly, then affected users' willingness to continue use indirectly; (3) users' perceived enjoyment, membership experience, auditory experience, and other factors also directly impacted user satisfaction, affecting users' willingness to continue use indirectly. This study effectively expands the factors influencing knowledge payment users' willingness to continue use and provides a useful reference for the sustainable development of knowledge payment platforms.

Online learning is gaining popularity, but users can easily find alternatives and switch between learning platforms. Reducing users switching behavior is a critical condition for the sustainable development of an online learning platform; therefore, it is necessary to investigate the influence factors of users switching behavior between different platforms to retain users and enhance the competitiveness of enterprises. Push-Pull-Mooring (PPM) theory is adopted to construct a structural equation model of customer switching behavior on online learning platforms and to explore the mechanism of user switching behavior between learning platforms. The model is tested with data collected from 313 online learning users. The results show that information overload and dissatisfaction, as push factors, significantly affect user switching behavior. Functional value and network externality as pull factors positively affect user switching behavior, switching cost, and affective commitment as mooring factors negatively correlate with switching behavior. Further, this study also revealed that there are obvious different influencing factors for different online learning platforms. Overall, this study provides some practical strategies for the online learning platform and can help them to gain a competitive advantage.

We present a new device, the SwitchPaD, to generate an active lateral force on a bare fingertip over a large touch area. Like our previous device, the UltraShiver, the SwitchPaD uses synchronization of in-plane ultrasonic oscillation and out-of-plane electroadhesion to generate force. The UltraShiver, however, relied on a single longitudinal resonance to produce oscillations, resulting in an inconsistent force profile. The SwitchPaD switches between the first and the second longitudinal mode based on the finger position, resulting in a much more consistent force profile across the touch surface. Experiments are used to compare the performance of two different modal switching strategies. Results indicate that the SwitchPaD can generate 250 mN peak active lateral force over a large area, and that, with the proper switching strategy, the switch itself is imperceptible.

In this article, we have developed a novel button click rendering mechanism based on active lateral force feedback. The effect can be localized because electroadhesion between a finger and a surface can be localized. Psychophysical experiments were conducted to evaluate the quality of a rendered button click, which subjects judged to be acceptable. Both the experiment results and the subjects' comments confirm that this button click rendering mechanism has the ability to generate a range of realistic button click sensations that could match subjects' different preferences. We can, thus, generate a button click on a flat surface without macroscopic motion of the surface in the lateral or normal direction, and can localize this haptic effect to an individual finger.

One well-known class of surface haptic devices that we have called Tactile Pattern Displays (TPaDs) uses ultrasonic transverse vibrations of a touch surface to modulate fingertip friction. This article addresses the power consumption of glass TPaDs, which is an important consideration in the context of mobile touchscreens. In particular, based on existing ultrasonic friction reduction models, we consider how the mechanical properties (density and Young's modulus) and thickness of commonly-used glass formulations affect TPaD performance, namely the relation between its friction reduction ability and its real power consumption. Experiments performed with eight types of TPaDs and an electromechanical model for the fingertip-TPaD system indicate: 1) TPaD performance decreases as glass thickness increases; 2) TPaD performance increases as the Young's modulus and density of glass decrease; and 3) real power consumption of a TPaD decreases as the contact force increases. Proper applications of these results can lead to significant increases in TPaD performance.

With the popularity of Wi-Fi smart devices, location estimation has been used to improve localization accuracy in indoor environments, and the Cramér-Rao lower bound (CRLB) is used to evaluate the performance of the localization algorithm. In this paper, first, the fingerprint-based received signal strength (RSS) and the time difference of arrival (TDoA) were used to estimate the position of target nodes. Then, Wi-Fi smart devices in the indoor localization environment were chosen to be the assistant nodes (ANs) to establish the searching scope based on rigid graph theory. Finally, the CRLB of joint localization algorithm was proposed, and the performance of the algorithm was evaluated by experiments in an indoor environment of our laboratory. We analyzed the influence of several parameters on the localization errors and provided a consistent evaluation method for this type of indoor localization algorithm.

We have developed a novel button click rendering mechanism based on active lateral force feedback. The effect can be localized because electroadhesion between a finger and a surface can be localized. We did psychophysical experiments to evaluate the quality of a rendered button click, which subjects judged to be acceptable. We can thus generate a button click on a flat surface without macroscopic motion of the surface in the lateral or normal direction, and we can localize this haptic effect to an individual finger. This mechanism is promising for touch-typing keyboard rendering ("multi-click").

Fingerprint based Wi-Fi localisation system often takes a lot of human efforts to measure the received signal strength (RSS) of dense grid in an indoor environment. In this paper, we propose a novel fingerprint database construction method with reduced human effort to obtain optimal length of RSS time series and grid division. We verify the chaotic characteristics of RSS time series, and use phase space reconstruction algorithm to calculate the optimal length of RSS data to be collected at each reference point. Then Gaussian process regression (GPR) for fingerprinting based indoor localisation is used to construct the database with information of limited reference points. The hyper-parameters of GPR is calculated by conjugate gradient descent algorithm. The performance of the proposed radio map construction framework is validated in real indoor environment, and with using Bayesian positioning method, the localisation error mean can be 1.5 m while the construction time of radio map is greatly reduced with ensuring accuracy.

Wireless sensor networks have been successfully applied in a wide range of application domains. However, because of the properties of wireless signals, Wireless sensor networks applications in underground environments have been limited. In this paper, we present a Kalman-filter-based localisation algorithm for use in a Wireless sensor networks deployed in a sub-surface mine for environmental monitoring to identify the positions of a large number of miners, each carrying a wireless mobile node. To improve the positioning accuracy even when current measurements are not available, we enhance the estimates of the received signal strength indication (RSSI) signal intensity and range obtained from the Kalman filter by adjusting them using the elastic particle model. Then, we obtain the distance matrix of the WSN based on arrival of angle and the cosine theorem. Finally, we determine the final positions of all mobile nodes using a multidimensional scaling algorithm.

We propose a new lateral force feedback device, the UltraShiver, which employs a combination of in-plane ultrasonic oscillation (around 30 kHz) and out-of-plane electroadhesion. It can achieve a strong active lateral force (400 mN) on the bare fingertip while operating silently. The lateral force is a function of pressing force, lateral vibration velocity, and electroadhesive voltage, as well as the relative phase between the velocity and voltage. In this paper, we perform experiments to investigate characteristics of the UltraShiver and their influence on lateral force.

The highly interactive nature of interpersonal communication on online social networks (OSNs) impels us to think about privacy as a communal matter, with users' private information being revealed by not only their own voluntary disclosures, but also the activities of their social ties. The current privacy literature has identified two types of information disclosures in OSNs: self-disclosure, i.e., the disclosure of an OSN user's private information by him/herself; and co-disclosure, i.e., the disclosure of the user's private information by other users. Although co-disclosure has been increasingly identified as a new source of privacy threat inherent to the OSN context, few systematic attempts have been made to provide a framework for understanding the commonalities and distinctions between self- vs. co-disclosure, especially pertaining to different types of private information. To address this gap, this paper presents a data-driven study that builds upon an innovative measurement for quantifying the extent to which others' co-disclosure could lead to actual privacy harm. The results demonstrate the significant harm caused by co-disclosure and illustrate the differences between the identity elements revealed through self- and co-disclosure.