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Augmented reality (AR), an innovative interactive technology that can realize the synchronization and integration of virtual and reality, has been widely used in commodity displays and museum exhibitions. However, few studies have examined the effectiveness of AR-based product display in the context of historically cultural and creative product (HCCP) marketing. This study aims to focus on whether and how the application of AR technology to the HCCP display will influence consumers’ product evaluation.

This study uses three experiments to examine the impact of an AR-based product display on consumers’ evaluation of HCCPs. In experiments 1 and 2, the researchers compared the consumer’s evaluation of HCCPs under the AR-based product display condition and two other display conditions (i.e. 3D model display and photographic display) and examined the mediating role of perceived authenticity in the evaluation process. Experiment 3 tested the moderating effect of the availability of artistic detail information on weakening the negative impact of AR-based product display on the evaluation of HCCPs.

This study found that using AR-based displays harms consumers’ evaluation of HCCPs by impairing perceived authenticity. The spatial-temporal cues of real-time circumstances impede consumers’ processing of the historical attributes of the product. The dynamic AR-based display makes it hard for consumers to build the product’s connection with historical prototypes. Thus, consumers’ perception of the authenticity of HCCP is reduced. Providing artistic details during the presentation makes artistic attributes more prominent than historical attributes, allowing consumers to pay more attention to the sensory experience caused by the artistic design instead of the spatial-temporal cues of the product. At this point, the negative impact of AR-based product display on the evaluation of HCCPs will be attenuated.

First, this study shows the adverse effects of AR-based product displays in the field of HCCP marketing. AR-based product display degrades product evaluations when the displayed product has historical attributes. Second, this study extends the perceived authenticity theory to the technological experience context and establishes a theoretical connection with the AR literature. Third, this study explores the multiple characteristics of HCCPs. The historical attributes are the central attribute of HCCPs, leading consumers to perceive lower sense of authenticity due to the conflict with real-time spatiotemporal cues risen from the AR-based display. However, the artistic attributes, which are beyond the limitation of time and space, will attenuate this conflict when they become prominent.

Contextual cues have become a hot research topic in the field of mobile consumer behavior, owing to the continuous rise of digital marketing. However, the complex online shopping scene makes it challenging to directly identify the association between the characteristics of contextual cues and consumer behavior. Presently, few studies have only systematically extracted and refined the types and characteristics of contextual cues. The purpose of this study is to explore the types and mechanisms of contextual cues in online shopping scenarios.

This study uses the word2vec algorithm, grounded theory and co-occurrence cluster method, along with online shopping word-of-mouth (WOM) text and consumer behavior theory, in order to explore different types of contextual cues and its efficiency from 5,619 comment corpus.

This study puts forward the following conclusions. (1) From the perspective of online shopping, contextual cues comprise aesthetic perception cues, value perception cues, trust-dependent cues, time perception cues, memory attention cues, spatial perception cues, attribute cues and relationship cues. (2) Based on the online shopping scenarios, contextual cues and their interaction effects exert an effect on consumer satisfaction, recommendation, purchase and return behavior.

The study conclusions are helpful to further reveal the deep association between contextual cues and consumer behavior in the process of online shopping, thus providing practical and theoretical enlightenment for enterprises to not only effectively reshape the scene but also promote the consumers' active purchase behavior.

The purpose of this paper is to explore external, mediator and moderator factors that influence tourist intention to use GPS-based navigation apps from the perspective of spatiotemporal tourist behavior (STTB).

A total of 636 valid questionnaires were selected from tourists visiting Lijiang Ancient Town. The partial least squares-structural equation modeling with the SmartPLS approach was adopted to estimate and validate the model.

The results of this paper showed that wayfinding efficiency, sensation-seeking behavior and spatial ability (SA) influence tourists’ intention to use (IU) navigation apps. A mediator of perceived location accuracy between SA and IU has been found. A multigroup generation moderator is verified in the model.

This paper provides a better understanding of the relationship between tourist spatiotemporal behavior and navigation apps, presenting practical suggestions for app developers, destination managers and vacation planners.

While the effects of information technology on tourist behavior have become a topic of interest among tourism industry stakeholders, this paper examines the effects of STTB on the acceptance of navigation apps in reverse, which enriches the theoretical framework.

本文从时空旅游行为 (STTB) 的角度探讨了影响游客使用基于 GPS 的导航应用程序的外部、中介和调节因素。

本研究共从丽江古城旅游者中抽取了636份有效问卷。采用 SmartPLS 方法的偏最小二乘结构方程建模 (PLS-SEM) 来估计和验证模型。

研究结果表明, 寻路效率 (WE)、感觉寻求行为 (SSB) 和空间能力 (SA) 会影响游客的使用意向 (IU) 导航应用程序。研究还发现感知位置准确度 (PLA)是空间能力 (SA) 和使用意图 (IU) 之间的中介变量。世代在多组比较模型中作为调节变量。

本文提供了对旅游时空行为与导航应用程序之间关系的更好理解, 为应用程序开发人员、目的地管理者和度假规划者提供了实用建议。

虽然信息技术对游客行为的影响已成为旅游业利益相关者感兴趣的话题, 但本文研究了 STTB 对导航应用程序接受度的影响, 从而丰富了理论框架。

Accounting’s definition of accountability should include attributes of socioenvironmental degradation manufactured by unsustainable technologies. Beck argues that emergent accounts should reflect the following primary characteristics of technological degradation: complexity, uncertainty, and diffused responsibility. Financial stewardship accounts and probabilistic assessments of risk, which are traditionally employed to allay the public’s fear of uncontrollable technological hazards, cannot reflect these characteristics because they are constructed to perpetuate the status quo by fabricating certainty and security. The process through which safety thresholds are constructed and contested represents the ultimate form of socialized accountability because these thresholds shape how much risk people consent to be exposed to. Beck’s socialized total accountability is suggested as a way forward: It has two dimensions, extended spatiotemporal responsibility and the psychology of decision-making. These dimensions are teased out from the following constructs of Beck’s Risk Society thesis: manufactured risks and hazards, organized irresponsibility, politics of risk, radical individualization and social learning. These dimensions are then used to critically evaluate the capacity of full cost accounting (FCA), and two emergent socialized risk accounts, to integrate the multiple attributes of sustainability. This critique should inform the journey of constructing more representative accounts of technological degradation.

The purpose of this paper is to provide a fast and accurate network for spatiotemporal action localization in videos. It detects human actions both in time and space simultaneously in real-time, which is applicable in real-world scenarios such as safety monitoring and collaborative assembly.

This paper design an end-to-end deep learning network called collaborator only watch once (COWO). COWO recognizes the ongoing human activities in real-time with enhanced accuracy. COWO inherits from the architecture of you only watch once (YOWO), known to be the best performing network for online action localization to date, but with three major structural modifications: COWO enhances the intraclass compactness and enlarges the interclass separability in the feature level. A new correlation channel fusion and attention mechanism are designed based on the Pearson correlation coefficient. Accordingly, a correction loss function is designed. This function minimizes the same class distance and enhances the intraclass compactness. Use a probabilistic K-means clustering technique for selecting the initial seed points. The idea behind this is that the initial distance between cluster centers should be as considerable as possible. CIOU regression loss function is applied instead of the Smooth L1 loss function to help the model converge stably.

COWO outperforms the original YOWO with improvements of frame mAP 3% and 2.1% at a speed of 35.12 fps. Compared with the two-stream, T-CNN, C3D, the improvement is about 5% and 14.5% when applied to J-HMDB-21, UCF101-24 and AGOT data sets.

COWO extends more flexibility for assembly scenarios as it perceives spatiotemporal human actions in real-time. It contributes to many real-world scenarios such as safety monitoring and collaborative assembly.

The purpose of this study is to explore spatiotemporal factors that affect the empirical analysis of whether crime rates in buffer areas surrounding abandoned properties transferred to a Land Bank that differed among three regimes: before transfer, during Land Bank stewardship and after disposition and whether those differences were associated with differences in relative crime activity in the neighborhoods in which they were located.

This study analyzed crime incidents occurring between 2010 and 2018 in 0.1-mile buffer areas surrounding 31 abandoned properties sold by the Land Bank and their neighborhoods in which those properties were located. Using Copulas, researchers compared concordance/discordance in the buffer areas across the three regime states for each property and approximately matched time periods for associated neighborhoods.

In a substantial number of cases, the relative crime activity levels for buffer areas surrounding individual sold properties as measured by the Copulas shifted from concordant to discordant states and vice versa. Similarly, relative crime activity levels for neighborhoods shifted from concordant to discordant states across three matched regimes. In some cases, the property and neighborhood states matched, while in other cases they diverged. These cross-level interactions indicate that criminal behavioral patterns and target selection change over time and relative criminal activity. The introduction of Copulas can improve the reliability of such models over time and when and where they should be customized to add more granular insights needed by law enforcement agencies.

The introduction of Copulas can improve the spatiotemporal reliability of the analysis of criminal activity over space and time.

Spatiotemporal considerations should be incorporated in setting interventions to manage criminal activity.

This study provides support for policies supporting renovation of abandoned properties.

To the best of authors’ knowledge, this research is the first application of Copulas to crime impact studies. As noted, Copulas can help reduce the risk of applying intervention or enforcement programs that are no longer reliable or lack the precision provided by insights into convergent/divergent patterns of criminal activity.

Effective rail surface defects detection method is the basic guarantee to manufacture high-quality rail. However, the existed visual inspection methods have disadvantages such as poor ability to locate the rail surface region and high sensitivity to uneven reflection. This study aims to propose a bionic rail surface defect detection method to obtain the high detection accuracy of rail surface defects under uneven reflection environments.

Through this bionic rail surface defect detection algorithm, the positioning and correction of the rail surface region can be computed from maximum run-length smearing (MRLS) and background difference. A saliency image can be generated to simulate the human visual system through some features including local grayscale, local contrast and edge corner effect. Finally, the meanshift algorithm and adaptive threshold are developed to cluster and segment the saliency image.

On the constructed rail defect data set, the bionic rail surface defect detection algorithm shows good recognition ability on the surface defects of the rail. Pixel- and defect-level index in the experimental results demonstrate that the detection algorithm is better than three advanced rail defect detection algorithms and five saliency models.

The bionic rail surface defect detection algorithm in the production process is proposed. Particularly, a method based on MRLS is introduced to extract the rail surface region and a multifeature saliency fusion model is presented to identify rail surface defects.

This study aims to examine the relationship among nonverbal customer-to-customer interactions (CCIs), positive and negative emotions, customer satisfaction and loyalty intentions.

The conceptual model that was developed using the stimulus-organism-response theoretical framework was tested using a sample of 583 consumers.

The results show that kinesics and paralanguage positively affect customers’ positive emotions while proxemics, paralanguage and physical appearance negatively influence their negative emotions. Further, both positive and negative emotions are found to have significant impacts on customer satisfaction and loyalty intentions.

Theoretically, this study not only contributes to the existing servicescape and customer experience literature but also expands nonverbal interaction research in the hospitality management field. However, results may have limited generalizability to other service settings and other cultural contexts.

This study is one of the first to investigate the impact of nonverbal CCIs on service experiences.

The purpose of this study is to present a large-scale real-world comparative study using pre-COVID lockdown data versus post-COVID lockdown data on predicting shipment times of therapeutic supplies in e-pharmacy supply chains and show that our proposed methodology is robust to lockdown effects.

The researchers used organic data of over 5.9 million records of therapeutic shipments, with 2.87 million records collected pre-COVID lockdown and 3.03 million records collected post-COVID lockdown. The researchers built various Machine Learning (ML) classifier models on the two datasets, namely, Random Forest (RF), Extra Trees (XRT), Decision Tree (DT), Multi-Layer Perceptron (MLP), XGBoost (XGB), CatBoost (CB), Linear Stochastic Gradient Descent (SGD) and the Linear Naïve Bayes (NB). Then, the researchers stacked these base models and built meta models on top of them. Further, the researchers performed a detailed comparison of the performances of ML models on pre-COVID lockdown and post-COVID lockdown datasets.

The proposed approach attains performance of 93.5% on real-world post-COVID lockdown data and 91.35% on real-world pre-COVID lockdown data. In contrast, the turn-around times (TAT) provided by therapeutic supply logistics providers are 62.91% accurate compared to reality in post-COVID lockdown times and 73.68% accurate compared to reality pre-COVID lockdown times. Hence, it is clear that while the TAT provided by logistics providers has deteriorated in the post-pandemic business climate, the proposed method is robust to handle pandemic lockdown effects on e-pharmacy supply chains.

The implication of the study provides a novel ML-based framework for predicting the shipment times of therapeutics, diagnostics and vaccines, and it is robust to COVID-19 lockdown effects.

E-pharmacy companies can readily adopt the proposed approach to enhance their supply chain management (SCM) capabilities and build resilience during COVID lockdown times.

The present study is one of the first to perform a large-scale real-world comparative analysis on predicting therapeutic supply shipment times in the e-pharmacy supply chain with novel ML ensemble stacking, obtaining robust results in these COVID lockdown times.

Despite being an authentic carrier of various cultural practices, the human body is often underutilised to access the knowledge of human body. Digital inventions today have created new avenues to open up cultural data resources, yet mainly as apparatuses for well-annotated and object-based collections. Hence, there is a pressing need for empowering the representation of intangible expressions, particularly embodied knowledge within its cultural context. To address this issue, the authors propose to inspect the potential of machine learning methods to enhance archival knowledge interaction with intangible cultural heritage (ICH) materials.

This research adopts a novel approach by combining movement computing with knowledge-specific modelling to support retrieving through embodied cues, which is applied to a multimodal archive documenting the cultural heritage (CH) of Southern Chinese martial arts.

Through experimenting with a retrieval engine implemented using the Hong Kong Martial Arts Living Archive (HKMALA) datasets, this work validated the effectiveness of the developed approach in multimodal content retrieval and highlighted the potential for the multimodal's application in facilitating archival exploration and knowledge discoverability.

This work takes a knowledge-specific approach to invent an intelligent encoding approach through a deep-learning workflow. This article underlines that the convergence of algorithmic reckoning and content-centred design holds promise for transforming the paradigm of archival interaction, thereby augmenting knowledge transmission via more accessible CH materials.