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In recent years, there has been significant interest in adopting XR (extended reality) technologies such as VR (virtual reality) and AR (augmented reality), particularly in retail. However, extending activities through reality-mediation is still mostly believed to offer an inferior experience due to their shortcomings in usability, wearability, graphical fidelity, etc. This study aims to address the research gap by experimentally examining the acceptance of metaverse shopping.

This study conducts a 2 (VR: with vs. without) × 2 (AR: with vs. without) between-subjects laboratory experiment involving 157 participants in simulated daily shopping environments. This study builds a physical brick-and-mortar store at the campus and stocked it with approximately 600 products with accompanying product information and pricing. The XR devices and a 3D laser scanner were used in constructing the three XR shopping conditions.

Results indicate that XR can offer an experience comparable to, or even surpassing, traditional shopping in terms of its instrumental and hedonic aspects, regardless of a slightly reduced perception of usability. AR negatively affected perceived ease of use, while VR significantly increased perceived enjoyment. It is surprising that the lower perceived ease of use appeared to be disconnected from the attitude toward metaverse shopping.

This study provides important experimental evidence on the acceptance of XR shopping, and the finding that low perceived ease of use may not always be detrimental adds to the theory of technology adoption as a whole. Additionally, it provides an important reference point for future randomized controlled studies exploring the effects of technology on adoption.

Ultracapacitors find extensive applications in various fields because of their high energy density and long cycling periods. However, due to the movement of ions and the arrangement patterns on rough/irregular electrode surfaces during the charge and discharge process of ultracapacitors, the parameters of ultracapacitors usually change with the variation of operating conditions. The purpose of this study is to accurately and quickly identify the parameters of ultracapacitors.

A variable forgetting factor recursive least square (VFFRLS) algorithm is proposed in this paper for online identifying the equivalent series resistance and capacitance C of ultracapacitors. In this work, a real-time error-based strategy is developed to adaptively regulate the value of the forgetting factor of traditional forgetting factor recursive least square (FFRLS) algorithm. The strategy uses the square of the average time autocorrelation estimation of the prior error and the posterior error between the predicted output and the actual output as the adjustment basis of forgetting factors.

Experiments were conducted using the proposed scheme, and the results were compared with the estimation results obtained by the recursive least squares (RLS) algorithm and the traditional FFRLS algorithm. The maximum root mean square error between the estimated values and actual values for VFFRLS is 3.63%, whereas for FFRLS it is 9.61%, and for RLS it is 19.33%.

By using the proposed VFFRLS algorithm, a relatively high precision can be achieved for the online parameter estimation of ultracapacitors. Besides, the dynamic balance between parameter stability and tracking performance can be validated by dynamically adjusting the forgetting factor.

The emergence of consumerism has led to regulatory measures being integrated into business practices, but the influence of consumers in developing countries remains limited, resulting in businesses being less responsive. The digital retail landscape is undergoing a transformative revolution, driven by Industrial Revolution (IR) 4.0 technological advancements such as artificial intelligence (AI), wearables, virtual reality (VR), augmented reality (AR), and blockchain technology. This development focuses on convenience, personalisation, and emotional connections. Companies are adapting to modern consumer behaviour through various strategies, including online shopping, mobile commerce, data analytics, technology integration, user reviews, and contactless payments. The COVID-19 pandemic has accelerated this seismic shift in the retail industry, and online retail is expected to continue to grow post-pandemic, driven by these technologies. AI enhances the customer experience, wearables provide interactive engagement, VR offers immersive shopping, AR merges online and physical shopping, and blockchain ensures secure transactions in the emerging metaverse. As retail converges with the metaverse, the potential for borderless and personalised shopping experiences is enormous. Advances in VR technology could lead to interconnected virtual spaces that seamlessly connect physical and digital retail, providing immersive and personalised shopping experiences. However, challenges such as cost, learning curves, digital security, legal ambiguity, data privacy, financial risk, and ethical considerations need to be addressed through vigilant and informed consumer engagement in this evolving digital landscape.

This paper aims to examine the impact of metaverse experiences on customers’ offline behavioral intentions: How do customers’ visits to a hospitality business’s virtual property in the metaverse affect their intentions to visit the physical property in the real world?

Based on the general learning model and social cognitive theory, this research hypothesizes the positive impact of metaverse experiences on customers’ visit intentions and explores two boundary conditions for positive impact: user–avatar resemblance and servicescape similarity. Two experimental studies were conducted.

Metaverse experience has a significant impact on customers’ visit intentions, and this impact is moderated by user–avatar resemblance and servicescape similarity.

This research addresses the call for empirical studies regarding the effects of metaverse experience on people’s behavioral intentions.

As one of the earliest empirical studies on the marketing effects of the metaverse, this research provides a basis for future metaverse studies in the hospitality field.

The purpose of this paper is to study the importance of image data in hotel selection-recommendation using different types of cognitive features and to explore whether there are reinforcing effects among these cognitive features.

This study represents user-generated images “cognitive” in a knowledge graph through multidimensional (shallow, middle and deep) analysis. This approach highlights the clustering of hotel destination imagery.

This study develops a novel hotel selection-recommendation model based on image sentiment and attribute representation within the construction of a knowledge graph. Furthermore, the experimental results show an enhanced effect between different types of cognitive features and hotel selection-recommendation.

This study enhances hotel recommendation accuracy and user satisfaction by incorporating cognitive and emotional image attributes into knowledge graphs using advanced machine learning and computer vision techniques.

This study advances the understanding of user-generated images’ impact on hotel selection, helping users make better decisions and enabling marketers to understand users’ preferences and trends.

This research is one of the first to propose a new method for exploring the cognitive dimensions of hotel image data. Furthermore, multi-dimensional cognitive features can effectively enhance the selection-recommendation process, and the authors have proposed a novel hotel selection-recommendation model.

This paper aims to study the problem of starvation lubrication of high-speed ball bearings due to temperature rise during operation and to avoid thermal failure of bearing lubrication.

Under the quasi-statics model of grease lubrication, both the oil film dragging force and the rolling friction between the balls and raceways collectively counteract the gyroscopic torque. Initially, the static model for grease lubrication is solved, followed by calculating the generated heat using the local heat generation method and ultimately the multinodal thermal network model is solved, and the solved results of the quasi-statics are updated by the temperatures of the grease nodes based on the relationship of the grease temperature and viscosity, as well as the relationship of the viscosity and the film thickness.

By comparing the numerical calculation results of bearings under different working conditions, the influence of starvation lubrication on the oil film thickness, oil film drag force and rolling friction of bearings is discussed, and it is found that the numerical calculation results of the outer ring temperature of bearings under the starvation lubrication due to the consideration of temperature rise are closer to the experimental values.

This study reveals the dynamic characteristics of bearings under starvation lubrication, which is more practical and engineering guiding significance for the design of bearings, and introduces a new method and basis for the calculation of temperature rise of rolling bearings.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0208/

Blockchain technology has been labeled as the most disruptive technological innovation of the current decade due to its impact on almost every major industry. Based on privacy calculus theory and prior adoption literature on emerging technologies, this research investigates the impact of blockchain technology in the consumer technology segment. It elaborated on the mechanism through which blockchain technology influences users’ willingness to share information with technology products enabled by blockchain.

Taking a heterogeneous pool of users, this study conducted multiple experiments with the application of blockchain (vs. regular database) technology to high (vs. low) sensitive data to study the impact of blockchain perception on users’ information-sharing tendencies.

Through a mediated moderation analysis, the result shows that the use of blockchain technology enhances the sense of security among users. However, the impact of this heightened sense of security only develops a higher willingness to share information when the data is highly sensitive.

The research reflects on the perception of blockchain technology and the leading impact on willingness to share information with firms. This could be a critical criterion for determining investment in blockchain technologies for consumer products, particularly based on the sensitivity of the data the consumer is sharing.

This research focuses on the perception of blockchain technology among consumers and its impact on consumers’ decision-making related to their data sharing. People have a higher sense of safety when it comes to blockchain-enabled products. However, we find that it would not be the same for all contexts, and the sensitivity of the data collected would have an impact on this relationship and consumers’ data-sharing decisions.

This paper aims to design a novel test device and study the wear properties and the thermal mechanisms of roller pairs in dual-freedom sliding contacts.

On the transition process of lubrication regimes, experiments were conducted with various values of running speed and slip ratio obtained by two motorized spindles. Temperature and surviving time would be obtained of GCr15/GCr15 and DLC/GCr15 friction pairs. Micro photography was obtained with a PGI 3D stylus profiler and a confocal microscopy OLS4000-3D. An empirical mode decomposition method was used to eliminate measure errors.

Results showed that, even with little initial lubricant, rolling/sliding pairs still rotated for a certain time. With the synthetic actions of the dual-freedom sliding, the loss of lubrication and the tilt, interesting helical grooves appeared. Sliding speeds had remarkable effects on survive time, temperatures and surface topographies. In addition, the equilibrium values of the temperature and the surface roughness were obtained in sufficient oil supply. Extreme wear-out conditions were obtained with starved lubrication. Diamond-like carbon coatings showed better heat resistance and better wear resistance.

This work would be critical for the life design and the heat treatment of rolling bearings in the full flood lubrication and the starved lubrication.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0164/

The volume of passenger traffic at metro transfer stations serves as a pivotal metric for the orchestration of crowd flow management. Given the intricacies of crowd dynamics within these stations and the recurrent instances of substantial passenger influxes, a methodology predicated on stochastic processes and the principle of user equilibrium is introduced to facilitate real-time traffic flow estimation within transfer station streamlines.

The synthesis of stochastic process theory with streamline analysis engenders a probabilistic model of intra-station pedestrian traffic dynamics. Leveraging real-time passenger flow data procured from monitoring systems within the transfer station, a gradient descent optimization technique is employed to minimize the cost function, thereby deducing the dynamic distribution of categorized passenger flows. Subsequently, adhering to the tenets of user equilibrium, the Frank–Wolfe algorithm is implemented to allocate the intra-station categorized passenger flows across various streamlines, ascertaining the traffic volume for each.

Utilizing the Xiaozhai Station of the Xi’an Metro as a case study, the Anylogic simulation software is engaged to emulate the intra-station crowd dynamics, thereby substantiating the efficacy of the proposed passenger flow estimation model. The derived solutions are instrumental in formulating a crowd control strategy for Xiaozhai Station during the peak interval from 17:30 to 18:00 on a designated day, yielding crowd management interventions that offer insights for the orchestration of passenger flow and operational governance within metro stations.

The construction of an estimation methodology for the real-time streamline traffic flow augments the model’s dataset, supplanting estimated values derived from surveys or historical datasets with real-time computed traffic data, thereby enhancing the precision and immediacy of crowd flow management within metro stations.

This study aims to explore how to overcome and address the loss of exploratory innovation, thereby achieving greater success in exploratory innovation. This phenomenon of loss occurs when enterprises decrease their investment in and engagement with exploratory innovation, ultimately leading to an insufficient amount of such innovation efforts. Drawing on dynamic capabilities, this study investigates the relationship between organizational foresight and exploratory innovation and examines the moderating role of breakthrough orientation/financial orientation.

This study used survey data collected from 296 Chinese high-tech companies in multiple industries and sectors.

The evidence produced by this study reveals that three elements of organizational foresight (i.e. environmental scanning capabilities, strategic selection capabilities and integrating capabilities) positively influence exploratory innovation. Furthermore, this positive effect is strengthened in the context of a high-breakthrough orientation. Moreover, the relationships among environmental scanning capabilities, strategic selection capabilities and exploratory innovation become weaker as an enterprise’s financial orientation increases, whereas a strong financial orientation does not affect the relationship between integrating capabilities and exploratory innovation.

Ambidexterity is key to successful enterprise innovation. Compared with exploitative innovation, it is by no means easy to engage in exploratory innovation, which is especially important in high-tech companies. While the loss of exploratory innovation has been observed, few empirical studies have explored ways to promote exploratory innovation more effectively. A key research implication of this study pertains to the role of organizational foresight in the improvement of exploratory innovation in the context of high-tech companies.

This paper contributes to the broader literature on exploratory innovation and organizational foresight and provides practical guidance for high-tech companies regarding ways of avoiding the loss of exploratory innovation and becoming more successful at exploratory innovation.