Search results

This study aims to enhance our understanding of customer expectations and experiences in grocery shopping within the metaverse. It investigates factors influencing customer satisfaction and driving continued engagement with metaverse platforms, offering insights into the drivers of customer adoption and barriers to usage.

Adopting a qualitative netnographic approach, this study analysed customer reactions to Walmart’s virtual store demonstration. Data were collected from user comments on YouTube, TikTok, Twitter and Reddit. Thematic analysis was employed to identify key factors contributing to satisfaction and dissatisfaction with metaverse grocery shopping experiences.

The study reveals three major drivers shaping customer satisfaction and subsequent positive intentions toward grocery shopping in the metaverse: social, functional and hedonic stimuli. Eight critical barriers affecting the metaverse shopping experience are identified: functional, hedonic, social, financial, privacy, safety, ownership and store atmospherics concerns, including tactile, acoustic and visual elements.

The findings are derived from a qualitative analysis of customer comments on social media platforms, which may limit generalisability. Future studies could adopt a mixed-methods approach to validate these findings across broader datasets.

This work is the first research to examine customer satisfaction with grocery shopping in the metaverse. It offers valuable insights into customer expectations, adoption drivers and critical barriers, laying the groundwork for further exploration of metaverse applications in retail.

This paper aims to study the influence of different seal structure parameters and working conditions on the air-oil two-phase flow characteristics and leakage characteristics of the seal cavity in the bearing cavity of the aero-engine spindle bearing tester.

In this paper, the VOF method and RNG k-ε turbulence model are used to explore the flow characteristics and leakage characteristics of the labyrinth seal cavity of an aero-engine spindle bearing tester under the condition of air-oil two-phase flow.

The distribution of the lubricating oil is related to the sealing clearance and the air-oil ratio. The amount of oil leakage increases with increasing of sealing chamber clearance, air-oil ratio and inlet velocity and decreases with increasing curvature and speed. The amount of air leakage increases with sealing clearance and inlet velocity.

In comparison to the pure air-phase flow field, the air-oil two-phase flow field can more accurately simulate the lubricating oil flow in the sealing chamber.

The paper proposes a place-space duality, rather than a dualism, for accounting research.

The discussion is informed by the literature in human geography, which, while developing the concept of space, has made an important distinction between abstract space and place as a site of experiential learning and memory.

The lack of a concept of place is a serious omission in the accounting literature and perpetuates an abstract sense of space, which can restrict the scope of accounting research.

The paper calls for further research to study accounting in place and to explore both the collective and individual senses of place, as well as conscious and unconscious place associations. We recognise that there is limited prior accounting research on this topic and that there are challenges in conducting such interdisciplinary research, especially as there is a lack of common ground between research in human geography and accounting and little integration of the two literatures.

The paper proposes an accounting research agenda based on a place-space duality, which reflects the strength of people-place relationships, including place identities, place attachment and place dependence.

The paper provides a critique of the conceptualisation of space in accounting research, identifies place-space as a duality (rather than a dualism) and suggests a novel distinction between studying accounting in context and in place.

On account of the flexibility, large working space and system openness, manipulators are often adopted in automatic grinding and polishing operations. In the flexible roboticized polishing process for complex surfaces with narrow spatial structures, such as aero-engine blades, the contact mode between the tool and the workpiece changes with the transformation of the manipulator’s end posture and the alternation of the workpiece curvature, which often leads to processing contact faults. These faults result in the obsolescence of expensive aerospace components and reduced efficiency. The purpose of this study is to collect vibration signals during the machining process and extract fault characteristic parameters for monitoring and diagnosis for diagnosing faults in automated flexible polishing to protect the workpiece.

This paper proposes a whale optimization algorithm (WAO)-support vector machine model based on the support vector machine and WAO. From the original grinding and polishing vibration signal, 11 time-domain features that can reflect the fluctuation of the vibration signal are extracted as detection features.

Experimental results indicate that this method effectively reflects the relationship between contact faults and diagnostic results, demonstrating good real-time performance and diagnostic capability.

This method provides a crucial theoretical basis for real-time fault diagnosis and monitoring in automatic flexible machining, ensuring reliable automatic flexible polishing processes.

The replenishment of construction materials heavily relies on the functioning of heavy machinery, which often leads to confusion and negotiations among construction work groups regarding the allocation rights of these materials. When multiple groups require the same construction materials, they often struggle to determine whether the delivered materials are intended for their own use or if they have encroached upon supplies designated for others. Such uncertainties and negotiations frequently result in delays in construction progress and have the potential to escalate into conflicts. To minimize misunderstandings among work groups and mitigate the risk of severe safety consequences, it is crucial to understand the decision-making processes involved in the interaction between work groups.

This paper adopts a game theory approach to examine the interactions among work groups from a safety perspective. Quantum response equilibrium (QRE), as a specialized form of game with incomplete information, is assumed to govern the behavior of work groups in this study. By conducting a questionnaire survey, interactive scenarios were simulated. A resource overlap scenario for high-altitude construction is established, with the key factors being the importance of construction materials, the time required to supplement materials, whether managers are present and the climate within the groups. The model parameters were estimated using the expectation–maximization algorithm. Additionally, individual traits and safety awareness are surveyed in the questionnaire, further explaining the results of the game.

The findings indicate that the likelihood of conflicts between work groups under resource overlap can be quantified. The radical behavior of construction work groups exhibits a positive correlation with the importance of construction materials and the time required for material replenishment. Furthermore, the presence of a safety climate and the oversight of management personnel play a significant role in maintaining the composure of construction work groups. The expanded results of the questionnaire demonstrate that there is considerable room for improvement in workers' safety awareness, and management approaches can be further enhanced to prevent unsafe behaviors from occurring.

A novel game theory model was developed to evaluate the behavior of construction groups in situations of resource overlap. This model offers practical suggestions to improve safety performance and efficiency in construction projects.

This paper investigates the spillover effect of firms’ social media engagement with investors on consumption market performance and examines the impact of balanced/imbalanced social media stakeholder engagement strategies on firms’ consumption market performance.

The study employs multi-source secondary data covering 3,856 quarterly observations of 188 firms in the Chinese retail industry over six years (2015–2020). Polynomial regression analysis and response surface methodology are used to test the hypotheses.

The study reveals that firms’ social media engagement with investors has a positive spillover effect on consumption market performance. Additionally, the authors find that a balanced social media engagement strategy, which allocates resources evenly between consumers and investors, is more likely to optimize firm performance than an imbalanced strategy.

The research reveals cross-stakeholder spillover effects of social media engagement, introduces balanced/imbalanced engagement strategy concepts and extends the balanced marketing perspective to the social media context, providing guidance for firms to optimize their social media strategies.

This study aims to estimate the growth rate of total agricultural output as well as the output of major crops in Kerala. Additionally, we examine the factors that influence overall agricultural growth in the state.

Using data from 1970 to 2019, the study estimated CAGR of the total agricultural output and output of major crops in Kerala. Further, the ARDL model is estimated to investigate the factors influencing agricultural growth.

Agricultural growth in Kerala has been weak, with food crop production consistently declining. Although commercial crop production initially increased, it has now begun to drop. An econometric analysis identified rainfall, fertilizer consumption and gross cropped area as key long-term factors affecting agricultural output. While rainfall has a negative impact, both fertilizer consumption (0.5%) and gross cropped area (8.89%) positively influence production.

This study uses proxy variable for agricultural output due to the unavailability of continuous data. Lack of time series data on certain variables such as agricultural credit, public investment and subsidy expenditure limited the inclusion of additional explanatory variables.

Several studies have examined various aspects of Kerala’s agricultural sector. However, to the best of our knowledge, no research has focused on identifying the key factors driving agricultural growth in the state.

This paper aims to provide a full introduction, new classification, comparison and investigation of the challenges as well as applications of layerless 3D printing, which is one of the industry 4.0 pioneers.

Given the significance and novelty of uniform 3D printing, more than 250 publications were collected and reviewed in an unbiased and clear manner.

As a result, the majority of uniform parts printed in polymer form are known up to this point. In a novel division for better researchers’ comprehension, uniform printing systems were classified into three categories: oxygen inhibition (OI), liquid lubrication (LL) and photon penetration (PP), and each was thoroughly investigated. Furthermore, these three approaches were evaluated in terms of printing speed, precision and accuracy, manufacturing scale and cost.

The parameters of each approach were compared independently, and then a practical comparison was conducted among these three approaches. Finally, a variety of technologies, opportunities, challenges and advantages of each significant method, as well as a future outlook for layerless rapid prototyping, are presented.

Financial fraud is a severe corporate fraud committed for achieving various objectives, like attaining financial targets, lowering debt and providing good signals to the market. Such financial fraud deceives stakeholders and results in substantial financial losses. This study aims to detect financial fraud using the modified Beneish M-Score, the most appropriate forensic tool for fraud detection. Furthermore, the current study also examines the influential role of the fraud triangle’s elements (pressure, opportunity and rationalization) on financial fraud in nonfinancial firms during 2018–2021, offering insight for understanding and mitigating fraudulent activities in the corporate world.

Financial fraud is treated as a dependent variable measured through a modified Beneish M-score, while the fraud triangle elements (pressure, opportunity and rationalization) are measured through six proxies, which are financial stability, leverage, financial target, nature of the industry, the effectiveness of supervision and auditor changes.

The study's finding proclaimed that fraud triangle elements result in financial fraud. Findings unveil that all elements (pressure, opportunity and rationalization) of the fraud triangle significantly influence financial fraud. The study confirms that these elements must be considered to protect investors and provide a safe environment for investment.

Rare literature found addressing the detection of financial fraud and its nexus with the fraud triangle specifically in Pakistan where deficient governance is notably prevalent. This study attempts to fill such a gap and contribute to knowledge.

Machine learning (ML) technologies are increasingly being applied in building cost estimation as an advanced method to overcome the challenge of insufficient data and subjective effects of experts. To address the gap of lacking a review of ML applications in building cost estimation, this research aimed to conduct a systematic literature review to provide a robust reference and suggest development pathways for creating novel ML-based building cost prediction models, ultimately enhancing construction project management capabilities.

A systematic literature review according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) was adopted using quantitative bibliographic analysis and qualitative narrative synthesis based on the 70 screened publications from Web of Science (WOS) and Scopus databases. The VOSviewer software was used to prepare the thematic focus from the bibliographic data garnered.

Based on the results of a bibliographic analysis, current research hotspots and future trends in the application of ML to building cost estimation have been identified. Additionally, the mechanisms behind existing ML models and other key points were analyzed using narrative synthesis. Importantly, the weaknesses of current applications were highlighted and recommendations for future development were made. These recommendations included defining the availability of building attributes, increasing the application of emerging ML algorithms and models to various aspects of building cost estimation and addressing the lack of public databases.

The findings are instrumental in aiding project management professionals in grasping current trends in ML for cost estimation and in promoting its adoption in real-world industries. The insights and recommendations can be utilized by researchers to refine ML-based cost estimation models, thereby enhancing construction project management. Additionally, policymakers can leverage the findings to advocate for industry standards, which will elevate technical proficiency and ensure consistency.

Compared to previous research, the findings revealed research hotspots and future trends in the application of ML cost estimation models in only building projects. Additionally, the analysis of the establishment mechanisms of existing ML models and other key points, along with the developed recommendations, were more beneficial for developing improved ML-based cost estimation models, thereby enhancing project management capabilities.