Alice Sarantopoulos, Gabriela Spagnol, Maria Rosa Colombrini, Leticia Minatogawa, Vinicius Minatogawa, Renata Cristina Gasparino and Li Li Min
This paper aims to evaluate the measurement properties of the Employee Perception to Assess the Lean Implementation Tool (EPLIT) in the Brazilian hospital context.
Abstract
Purpose
This paper aims to evaluate the measurement properties of the Employee Perception to Assess the Lean Implementation Tool (EPLIT) in the Brazilian hospital context.
Design/methodology/approach
A cross-sectional study was conducted in two Brazilian hospitals, adhering to COnsensus-based Standards for the selection of health Measurement Instruments (COSMIN) guidelines. Exploratory factor analysis (EFA) and Cronbach's alpha were used for construct validity and reliability.
Findings
The adapted tool comprises 27 items across five domains, explaining 63.3% of the variance. Cronbach's alpha ranged from 0.78 to 0.86, indicating satisfactory reliability.
Research limitations/implications
Limitations include convenience sampling and exclusive use of EFA for validation. Future studies may employ Confirmatory Factor Analysis for further validation.
Practical implications
The tool aids healthcare managers in Brazil to systematically evaluate Lean implementation, contributing to process optimization and quality improvement.
Social implications
Effective Lean implementation using the validated tool could lead to improved healthcare delivery and patient outcomes.
Originality/value
This is the first study to adapt and validate EPLIT for the Brazilian healthcare sector, offering a robust tool for managers and researchers.
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Keywords
Peng Wu, Heng Su, Hao Dong, Tengfei Liu, Min Li and Zhihao Chen
Robotic arms play a crucial role in various industrial operations, such as sorting, assembly, handling and spraying. However, traditional robotic arm control algorithms often…
Abstract
Purpose
Robotic arms play a crucial role in various industrial operations, such as sorting, assembly, handling and spraying. However, traditional robotic arm control algorithms often struggle to adapt when faced with the challenge of dynamic obstacles. This paper aims to propose a dynamic obstacle avoidance method based on reinforcement learning to address real-time processing of dynamic obstacles.
Design/methodology/approach
This paper introduces an innovative method that introduces a feature extraction network that integrates gating mechanisms on the basis of traditional reinforcement learning algorithms. Additionally, an adaptive dynamic reward mechanism is designed to optimize the obstacle avoidance strategy.
Findings
Validation through the CoppeliaSim simulation environment and on-site testing has demonstrated the method's capability to effectively evade randomly moving obstacles, with a significant improvement in the convergence speed compared to traditional algorithms.
Originality/value
The proposed dynamic obstacle avoidance method based on Reinforcement Learning not only accomplishes the task of dynamic obstacle avoidance efficiently but also offers a distinct advantage in terms of convergence speed. This approach provides a novel solution to the obstacle avoidance methods for robotic arms.
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Wan-Chen Lee, Li-Min Cassandra Huang and Juliana Hirt
This study aims to understand fiction readers’ perspectives on the strengths and concerns of incorporating emojis into information systems for fiction. To solicit readers’…
Abstract
Purpose
This study aims to understand fiction readers’ perspectives on the strengths and concerns of incorporating emojis into information systems for fiction. To solicit readers’ feedback, the authors adopted Cho et al.’s (2023) model of three families of fiction mood categories as the theoretical framework. Based on this framework, prototypes of interface designs that implemented textual mood descriptors and/or emojis were developed.
Design/methodology/approach
Eighteen adult fiction readers at a US public university were recruited for online interviews. The participants shared their insights into the prototypes and their fiction search and review experiences.
Findings
Most participants preferred designs that support both mood terms and emojis. The findings highlighted the potential of emojis to improve metadata inclusivity and serve diverse users’ needs. Technical challenges and accessibility issues for blind or visually impaired users were noted as limitations of emoji implementation.
Originality/value
Based on established theoretical frameworks and emoji mappings for mood categories, this study advances the progress of implementing emojis into information systems for fiction. The findings will inform user-centered interface designs that support the description, search and review of fiction.
Dania Bilal and Li-Min Cassandra Huang
This paper aims to investigate user voice-switching behavior in voice assistants (VAs), embodiments and perceived trust in information accuracy, usefulness and intelligence. The…
Abstract
Purpose
This paper aims to investigate user voice-switching behavior in voice assistants (VAs), embodiments and perceived trust in information accuracy, usefulness and intelligence. The authors addressed four research questions: RQ1. What is the nature of users’ voice-switching behavior in VAs? RQ2: What are user preferences for embodied voice interfaces (EVIs), and do their preferred EVIs influence their decision to switch the voice on their VAs? RQ3: What are the users’ perceptions of their VAs concerning: a. information accuracy, b. usefulness, c. intelligence and d. the most important characteristics they must possess? RQ4: Do users prefer their voice interface to match their characteristics (age, gender, accent and race/ethnicity)?
Design/methodology/approach
The authors used a 52-question survey questionnaire to collect quantitative and qualitative data. The population was undergraduate students (freshmen and sophomores) at a research university in the USA. The students were enrolled in two required courses with a research participation assignment offered for credits. Students must register for research participation credits in the SONA Research Participation System www.sona-systems.com/platform/research-management/ Registered students cannot be invited or sampled to participate in a research study. There were 1,700 students enrolled in both courses. After the survey’s URL was posted in SONA, the authors received (n = 632) responses. Of these, (n = 150) completed the survey and provided valid responses.
Findings
Participants (43%) switched the voice interface in their VAs. They preferred American and British accents but trusted the latter. The British accent with a male voice was more trusted than the American accent with a female voice. Voice-switching decisions varied in the case of most and least preferred EVIs. Participants preferred EVIs that matched their characteristics. Most trusted their VAs’ information accuracy because they used the internet to find information, reflecting inadequate mental models. Lack of trust is attributed to misunderstanding requests and inability to respond accurately. A significant correlation was found between the participants’ perceived intelligence of their VAs and trust in information accuracy.
Research limitations/implications
Due to the wide variability in the data (e.g. 84% White, 6% Asian and 6% Black), the authors did not perform a statistical test to identify the significance between the selected EVIs and participants’ races or ethnicities. The self-reported survey questionnaire may be prone to inaccuracy. The participants’ interest in earning research credit for participation in this study and using SONA is a potential bias. The EVIs the authors used as embodiments are limited in their representation of people from diverse backgrounds, races, ethnicities, ages and genders. However, they could be examples for building prototypes to test in VAs.
Practical implications
Educators and information professionals should lead the way in offering artificial intelligence (AI) literacy programs to enable young adults to form more adequate mental models of VAs and support their learning and interactions. VA designers should address the failures and other issues the participants experienced in VAs to minimize frustrations. They should also train machine learning models on large data sets of complex queries to augment success. Furthermore, they should consider augmenting VAs’ personification with EVIs to enrich voice interactions and enhance personalization. Researchers should use a mixed research method with data triangulation instead of only a survey.
Social implications
There is a dire need to teach young adults AI literacy skills to enable them to build adequate mental models of VAs. Failures in VAs could affect users’ willingness to use them in the future. VAs can be effective teaching and learning tools, supporting students’ autonomous and personalized learning. Integrating EVIs with diverse characteristics could advance inclusivity in designing VAs and support personalization beyond language, accent and gender.
Originality/value
This study advances research on user voice-switching behavior in VAs, which has hardly been investigated in VA research. It brings attention to users’ experiential learning and the need for exposure to AI literacy to enable them to form adequate mental models of VAs. This study contributes to research on personifying VAs through EVIs with diverse characteristics to visualize voice interactions. Reasons for not switching the voice interface due to satisfaction with the current voice or a lack of knowledge of this feature did not support the status quo theory. Incorporating satisfaction and lack of knowledge as new factors could advance this theory. Switching the voice interface to avoid visualizing the least preferred EVIs in VAs is a new theme emerging from this study. Users’ trust in VAs’ information accuracy is intertwined with perceived intelligence and usefulness, but perceived intelligence is the strongest factor influencing trust.
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Yu Hin Kong and Chi Ching, Gary Chow
Multiple infectious control measures, e.g. social distancing, city lockdown and mask-wearing, have been implemented since the coronavirus disease 2019 (COVID-19) outbreak. Given…
Abstract
Purpose
Multiple infectious control measures, e.g. social distancing, city lockdown and mask-wearing, have been implemented since the coronavirus disease 2019 (COVID-19) outbreak. Given the bidirectional relationship between foundational movement skills (FoMS) and physical activity (PA), and inadequate PA in Chinese children and adolescents, FoMS tends to decrease during and after the COVID-19 pandemic. Therefore, the purpose of this paper is to systematically review the literature about the impact of the COVID-19 pandemic on FoMS of individuals aged 5–17 years in Chinese societies.
Design/methodology/approach
Preferred Reporting Items for Systematic Reviews and Meta-Analyses was followed. Peer-reviewed articles on four electronic databases (Scopus, Web of Science, EBSCOhost and PubMed) were searched on 8 May 2024. The quality of each study was evaluated by the Mixed Methods Appraisal Tool (MMAT) version 2018. Two independent reviewers were involved in all study selection and appraising procedures.
Findings
Among 18,450 records identified, 10 quantitative studies analysing student participants were included. The overall quality of these studies was high, with an average score of 86% in MMAT. The variations among these studies led to inconclusive evaluations. So as to advance the quality of future research and assessments, investigating more aspects of FoMS, standardising physical test protocols and report styles and adopting multiple research designs should be achieved.
Originality/value
To the best of the authors’ knowledge, this is the first review synthesising evidence about FoMS for Chinese children and adolescents. A definitive conclusion cannot be provided due to certain methodological issues. The current situation of FoMS and future research directions were illustrated.
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Jiaqi Liu, Jialong Jiang, Mingwei Lin, Hong Chen and Zeshui Xu
When recommending products to consumers, it is important to be able to accurately predict how consumers will rate them. However, existing collaborative filtering models are…
Abstract
Purpose
When recommending products to consumers, it is important to be able to accurately predict how consumers will rate them. However, existing collaborative filtering models are difficult to achieve a balance between rating prediction accuracy and complexity. Therefore, the purpose of this paper is to propose an accurate and effective model to predict users’ ratings of products for the accurate recommendation of products to users.
Design/methodology/approach
First, we introduce an attention mechanism that dynamically assigns weights to user preferences, highlighting key interaction information and enhancing the model’s understanding of user behavior. Second, a fold embedding strategy is employed to segment user interaction data, increasing the information density of each subset while reducing the complexity of the attention mechanism. Finally, a masking strategy is integrated to mitigate overfitting by concealing portions of user-item interactions, thereby improving the model’s generalization ability.
Findings
The experimental results demonstrate that the proposed model significantly minimizes prediction error across five real-world datasets. On average, the evaluation metrics root mean square error (RMSE) and mean absolute error (MAE) are reduced by 9.11 and 13.3%, respectively. Additionally, the Friedman test results confirm that these improvements are statistically significant. Consequently, the proposed model more accurately captures the intrinsic correlation between users and products, leading to a substantial reduction in prediction error.
Originality/value
We propose a novel collaborative filtering model to learn the user-item interaction matrix effectively. Additionally, we introduce a fold embedding strategy to reduce the computational resource consumption of the attention mechanism. Finally, we implement a masking strategy to encourage the model to focus on key features and patterns, thereby mitigating overfitting.
Details
Keywords
Jiaqi Fang, Kun Ma, Yanfang Qiu, Ke Ji, Zhenxiang Chen and Bo Yang
The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant…
Abstract
Purpose
The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant difference in length between the content and its title. In addition, relying solely on textual discrepancies between the title and content to distinguish between real and fake news has proven ineffective. The purpose of this paper is to develop a new approach called semantic enhancement network with content–title discrepancy (SEN–CTD), which enhances the accuracy of fake news detection.
Design/methodology/approach
The SEN–CTD framework is composed of two primary modules: the SEN and the content–title comparison network (CTCN). The SEN is designed to enrich the representation of news titles by integrating external information and position information to capture the context. Meanwhile, the CTCN focuses on assessing the consistency between the content of news articles and their corresponding titles examining both emotional tones and semantic attributes.
Findings
The SEN–CTD model performs well on the GossipCop, PolitiFact and RealNews data sets, achieving accuracies of 80.28%, 86.88% and 84.96%, respectively. These results highlight its effectiveness in accurately detecting fake news across different types of content.
Originality/value
The SEN is specifically designed to improve the representation of extremely short texts, enhancing the depth and accuracy of analyses for brief content. The CTCN is tailored to examine the consistency between news titles and their corresponding content, ensuring a thorough comparative evaluation of both emotional and semantic discrepancies.
Details
Keywords
Crystal T. Lee, Zimo Li and Yung-Cheng Shen
The proliferation of non-fungible token (NFT)-based crypto-art platforms has transformed how creators manage, own and earn money through the creation, assets and identity of their…
Abstract
Purpose
The proliferation of non-fungible token (NFT)-based crypto-art platforms has transformed how creators manage, own and earn money through the creation, assets and identity of their digital works. Despite this, no studies have examined the drivers of continuous content contribution behavior (CCCB) toward NFTs. Hence, this study draws on the theory of relational bonds to examine how various relational bonds affect feelings of psychological ownership, which, in turn, affects CCCB on metaverse platforms.
Design/methodology/approach
Using structural equation modeling and importance-performance matrix analysis, an online survey of 434 content creators from prominent NFT platforms empirically validated the research hypotheses.
Findings
Financial, structural, and social bonds positively affect psychological ownership, which in turn encourages CCCBs. The results of the importance-performance matrix analysis reveal that male content creators prioritized virtual reputation and social enhancement, whereas female content creators prioritized personalization and monetary gains.
Originality/value
We examine Web 3.0 and the NFT creators’ network that characterizes the governance practices of the metaverse. Consequently, the findings facilitate a better understanding of creator economy and meta-verse commerce.
Details
Keywords
Xi Luo, Jun-Hwa Cheah, Xin-Jean Lim, T. Ramayah and Yogesh K. Dwivedi
The increasing popularity of live-streaming commerce has provided a new opportunity for e-retailers to boost sales. This study integrated signaling theory and social exchange…
Abstract
Purpose
The increasing popularity of live-streaming commerce has provided a new opportunity for e-retailers to boost sales. This study integrated signaling theory and social exchange theory to investigate how streamer- and product-centered signals influence customers’ likelihood of making an impulsive purchase in the live-streaming commerce context.
Design/methodology/approach
An online survey was designed and distributed to the target respondents in China using purposive sampling. A total of 735 valid responses were analyzed with partial least square structural equation modeling (PLS-SEM).
Findings
Both streamer-centered signals, i.e. streamer credibility and streamer interaction quality, were discovered to significantly influence product-centered signal, i.e. product information quality. Additionally, streamer interaction quality was found to have a significant impact on streamer credibility. Furthermore, it was observed that customer engagement played a significant mediating role in the relationship between product information quality and impulsive buying tendency. Moreover, the paths between product information quality and customer engagement, as well as the connection between engagement and impulsive buying tendency, were found to be moderated by guanxi orientation.
Originality/value
Despite the prevalence of impulsive purchases in live-streaming commerce, few studies have empirically investigated the impact of streamer and product signals on influencing customers’ impulsive purchase decisions. Consequently, to the best of our knowledge, this study distinguishes itself by offering empirical insights into how streamers use reciprocating relationship mechanisms to communicate signals that facilitate impulsive purchase decisions.
Details
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Vamsi Desam and Pradeep Reddy CH
Several chaotic system-based encryption techniques have been presented in recent years to protect digital images using cryptography. The challenges of key distribution and…
Abstract
Purpose
Several chaotic system-based encryption techniques have been presented in recent years to protect digital images using cryptography. The challenges of key distribution and administration make symmetric encryption difficult. The purpose of this paper is to address these concerns, the novel hybrid partial differential elliptical Rubik’s cube algorithm is developed in this study as an asymmetric image encryption approach. This novel algorithm generates a random weighted matrix, and uses the masking method on image pixels with Rubik’s cube principle. Security analysis has been conducted, it enhances and increases the reliability of the proposed algorithm against a variety of attacks including statistical and differential attacks.
Design/methodology/approach
In this light, a differential elliptical model is designed with two phases for image encryption and decryption. A modified image is achieved by rotating and mixing intensities of rows and columns with a masking matrix derived from the key generation technique using a unique approach based on the elliptic curve and Rubik’s cube principle.
Findings
To evaluate the security level, the proposed algorithm is tested with statistical and differential attacks on a different set of test images with peak signal-to-noise ratio, unified average changed intensity and number of pixel change rate performance metrics. These results proved that the proposed image encryption method is completely reliable and enhances image security during transmission.
Originality/value
The elliptic curve–based encryption is hard to break by hackers and adding a Rubik’s cube principle makes it even more complex and nearly impossible to decode. The proposed method provides reduced key size.