Aaron McCune Stein and Yan Ai Min
Based on social exchange theory and the substitutes for leadership theory, this paper aims to investigate whether an organization’s high-commitment HRM strategy can substitute for…
Abstract
Purpose
Based on social exchange theory and the substitutes for leadership theory, this paper aims to investigate whether an organization’s high-commitment HRM strategy can substitute for the effect of servant leadership in promoting employees’ affective commitment, psychological empowerment and intent to remain with the organization.
Design/methodology/approach
This study’s hypotheses were tested with moderation and mediation analyses conducted on a sample of 172 Chinese employees.
Findings
The results show significant negative interaction effects between high-commitment HRM systems and servant leadership, such that high levels of one will reduce the positive effect of the other on affective commitment and psychological empowerment. Further, the effects of high-commitment HRM systems and servant leadership on turnover intentions are mediated through affective commitment and psychological empowerment. Finally, support was found for a mediated moderation model where the negative interaction effect between high-commitment HRM systems and servant leadership on turnover intentions is mediated through affective commitment.
Practical implications
The results of this study can help practitioners identify alternative means to influence employees’ positive attitudes and work motivation when implementing high-commitment HRM systems is not feasible for the organization.
Originality/value
This study contributes to the leadership literature by providing evidence supporting the substitutes for leadership theory and describing the specific conditions under which this theory is valid, as well as contributing to the HRM literature by examining the dynamic interaction of HRM and leadership.
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Rizwan Ali, Jin Xu, Mushahid Hussain Baig, Hafiz Saif Ur Rehman, Muhammad Waqas Aslam and Kaleem Ullah Qasim
This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates…
Abstract
Purpose
This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators.
Design/methodology/approach
In this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR.
Findings
This study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy.
Originality/value
According to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.
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The study investigated the feedback seeking abilities of learners in L2 writing classrooms using ChatGPT as an automated written corrective feedback (AWCF) provider. Specifically…
Abstract
Purpose
The study investigated the feedback seeking abilities of learners in L2 writing classrooms using ChatGPT as an automated written corrective feedback (AWCF) provider. Specifically, the research embarked on the exploration of L2 writers’ feedback seeking abilities in interacting with ChatGPT for feedback and their perceptions thereof in the new learning environment.
Design/methodology/approach
Three EFL learners of distinct language proficiencies and technological competences were recruited to participate in the mixed method multiple case study. The researcher used observation and in-depth interview to collect the ChatGPT prompts written by the participants and their reflections of feedback seeking in the project.
Findings
The study revealed that: (1) students with different academic profiles display varied abilities to utilize the feedback seeking strategies; (2) the significance of feedback seeking agency was agreed upon and (3) the promoting factors for the development of students’ feedback seeking abilities are the proactivity of involvement and the command of metacognitive regulatory skills.
Research limitations/implications
Additionally, a conceptual model of feedback seeking in an AI-mediated learning environment was postulated. The research has its conceptual and practical implications for researchers and educators expecting to incorporate ChatGPT in teaching and learning. The research unveiled the significance and potential of using state-of-the-art technologies in education. However, since we are still in an early phase applying such tools in authentic pedagogical environments, many instructional redevelopment and rearrangement should be considered and implemented.
Originality/value
The work is a pioneering effort to explore learners' feedback seeking abilities in a ChatGPT-enhanced learning environment. It pointed out new directions for process-, and student-oriented research in the era of changes.
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Agustina Calatayud, John Mangan and Martin Christopher
An emerging theme in the practitioner literature suggests that the supply chain of the future – enabled especially by developments in ICT – will be autonomous and have predictive…
Abstract
Purpose
An emerging theme in the practitioner literature suggests that the supply chain of the future – enabled especially by developments in ICT – will be autonomous and have predictive capabilities, bringing significant efficiency gains in an increasingly complex and uncertain environment. This paper aims to both bridge the gap between the practitioner and academic literature on these topics and contribute to both practice and theory by seeking to understand how such developments will help to address key supply chain challenges and opportunities.
Design/methodology/approach
A multi-disciplinary, systematic literature review was conducted on relevant concepts and capabilities. A total of 126 articles were reviewed covering the time period 1950-2018.
Findings
The results show that both IoT and AI are the technologies most frequently associated with the anticipated autonomous and predictive capabilities of future supply chains. In addition, the review highlights a lacuna in how such technologies and capabilities help address key supply chain challenges and opportunities. A new supply chain model is, thus, proposed, one with autonomous and predictive capabilities: the self-thinking supply chain.
Originality/value
It is our hope that this novel concept, presented here for the first time in the academic literature, will help both practitioners to craft appropriate future-proofed supply chain strategies and provide the research community with a model (built upon multidisciplinary insights) for elucidating the application of new digital technologies in the supply chain of the future. The self-thinking supply chain has the potential in particular to help address some of today’s key supply chain challenges and opportunities.
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Min-Ren Yan, Lin-Ya Hong and Kim Warren
This paper proposes an integrated knowledge visualization and digital twin system for supporting strategic management decisions. The concepts and applications of strategic…
Abstract
Purpose
This paper proposes an integrated knowledge visualization and digital twin system for supporting strategic management decisions. The concepts and applications of strategic architecture have been illustrated with a concrete real-world case study and decision rules of using the strategic digital twin management decision system (SDMDS) as a more visualized, adaptive and effective model for decision-making.
Design/methodology/approach
This paper integrates the concepts of mental and computer models and examines a real case's business operations by applying system dynamics modelling and digital technologies. The enterprise digital twin system with displaying real-world data and simulations for future scenarios demonstrates an improved process of strategic decision-making in the digital age.
Findings
The findings reveal that data analytics and the visualized enterprise digital twin system offer better practices for strategic management decisions in the dynamic and constantly changing business world by providing a constant and frequent adjustment on every decision that affects how the business performs over both operational and strategic timescales.
Originality/value
In the digital age and dynamic business environment, the proposed strategic architecture and managerial digital twin system converts the existing conceptual models into an advanced operational model. It can facilitate the development of knowledge visualization and become a more adaptive and effective model for supporting real-time management decision-making by dealing with the complicated dependence of constant flow of data input, output and the feedback loop across business units and boundaries.
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Wei-Zhen Wang, Hong-Mei Xiao and Yuan Fang
Nowadays, artificial intelligence (AI) technology has demonstrated extensive applications in the field of art design. Attribute editing is an important means to realize clothing…
Abstract
Purpose
Nowadays, artificial intelligence (AI) technology has demonstrated extensive applications in the field of art design. Attribute editing is an important means to realize clothing style and color design via computer language, which aims to edit and control the garment image based on the specified target attributes while preserving other details from the original image. The current image attribute editing model often generates images containing missing or redundant attributes. To address the problem, this paper aims for a novel design method utilizing the Fashion-attribute generative adversarial network (AttGAN) model was proposed for image attribute editing specifically tailored to women’s blouses.
Design/methodology/approach
The proposed design method primarily focuses on optimizing the feature extraction network and loss function. To enhance the feature extraction capability of the model, an increase in the number of layers in the feature extraction network was implemented, and the structure similarity index measure (SSIM) loss function was employed to ensure the independent attributes of the original image were consistent. The characteristic-preserving virtual try-on network (CP_VTON) dataset was used for train-ing to enable the editing of sleeve length and color specifically for women’s blouse.
Findings
The experimental results demonstrate that the optimization model’s generated outputs have significantly reduced problems related to missing attributes or visual redundancy. Through a comparative analysis of the numerical changes in the SSIM and peak signal-to-noise ratio (PSNR) before and after the model refinement, it was observed that the improved SSIM increased substantially by 27.4%, and the PSNR increased by 2.8%, serving as empirical evidence of the effectiveness of incorporating the SSIM loss function.
Originality/value
The proposed algorithm provides a promising tool for precise image editing of women’s blouses based on the GAN. This introduces a new approach to eliminate semantic expression errors in image editing, thereby contributing to the development of AI in clothing design.
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Arne Walter, Kamrul Ahsan and Shams Rahman
Demand planning (DP) is a key element of supply chain management (SCM) and is widely regarded as an important catalyst for improving supply chain performance. Regarding the…
Abstract
Purpose
Demand planning (DP) is a key element of supply chain management (SCM) and is widely regarded as an important catalyst for improving supply chain performance. Regarding the availability of technology to process large amounts of data, artificial intelligence (AI) has received increasing attention in the DP literature in recent years, but there are no reviews of studies on the application of AI in supply chain DP. Given the importance and value of this research area, we aimed to review the current body of knowledge on the application of AI in DP to improve SCM performance.
Design/methodology/approach
Using a systematic literature review approach, we identified 141 peer-reviewed articles and conducted content analysis to examine the body of knowledge on AI in DP in the academic literature published from 2012 to 2023.
Findings
We found that AI in DP is still in its early stages of development. The literature is dominated by modelling studies. We identified three knowledge clusters for AI in DP: AI tools and techniques, AI applications for supply chain functions and the impact of AI on digital SCM. The three knowledge domains are conceptualised in a framework to demonstrate how AI can be deployed in DP to improve SCM performance. However, challenges remain. We identify gaps in the literature that make suggestions for further research in this area.
Originality/value
This study makes a theoretical contribution by identifying the key elements in applying AI in DP for SCM. The proposed conceptual framework can be used to help guide further empirical research and can help companies to implement AI in DP.
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Roberto Palazzetti and Xiu-Tian Yan
This paper reports on the findings of first of its kind to examine motorbike’s chain transmission, focusing on chain lubrication and its effect on the temperature, efficiency and…
Abstract
Purpose
This paper reports on the findings of first of its kind to examine motorbike’s chain transmission, focusing on chain lubrication and its effect on the temperature, efficiency and vibrations of the transmission. The novelty of the paper is to investigate holistically the lubrication effect on the transmission by comparing its dynamic performances under three different lubrication conditions: chain not lubricated at all, chain lubricated with a spray polytetrafluoroethylene (PTFE) addicted lube and chain lubricated with a mineral oil at every minute of the test.
Design/methodology/approach
A novel mechatronic test rig has been designed and manufactured using a thermocamera, a dynamometer and an accelerometer respectively. The rig enables a multi-parameter dynamic and real-time sensing investigation of any motorbike chain system by recording temperature, transmission's efficiency and vibrations for any tests by. An experimental investigation has been conducted by running a chain under three aforementioned lube conditions at different speeds with the purposes of: (A) measuring the effect that each lube condition has on three critical parameters: temperature, vibrations and the efficiency of the transmission, and (B) identify the best conditions for practical use.
Findings
Results showed that proper use of lubricant can increase the efficiency of the system, by an estimated average of 4.1% is desirable. Additionally, using a continuous lubrication with a mineral oil lubricant leads to better transmission compared to the use of the spray PTFE from the efficiency and thermal points of view.
Originality/value
This work presents an experimental investigation on the effect of two different kinds of lubrication form motorbike chain on transmission’s efficiency. The findings are still valid for different applications of chain transmission in dirty environments. Novelty of the paper is highlighted as follows: this is the first work scientifically investigating the importance of lubrication on chain in harsh environments, particularly of motorbike chain. The work reports a comprehensive lete set experiments and analysis of thermal and mechanical effect because of the presence of lubricant has never been shown. Three kinds of lubricants have been used, and show their distinctive effects which are separately highlighted.
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Shaodi Zhao, Jiusheng Bao, Qingjin Zhang, Yan Yin, Xiaoyang Wang and Junwei Ai
This study aims to develop magnetic field-controlled friction braking technology, the preparation process of hard magnetic brake friction material was optimized and analyzed in…
Abstract
Purpose
This study aims to develop magnetic field-controlled friction braking technology, the preparation process of hard magnetic brake friction material was optimized and analyzed in this paper.
Design/methodology/approach
NdFeB, a rigid magnetic material, was selected as additive. Magnetic field orientation, a part of material preparation, was added to the preparation process. Experiments investigated the tribological properties of each brake lining sample. The preparation process of the hard magnetic friction material was optimized based on fuzzy theory by using analytic hierarchy process (AHP) methods and SPSS software. The microscopic morphology and the distribution and content of elements of friction lining samples prepared with or without orientation excitation voltage were analyzed by scanning electron microscope and energy dispersive X-ray microanalysis.
Findings
The results showed that the tribological properties of brake lining samples could be improved by process optimization and the oriented excitation voltage can effectively improve the properties of the brake lining.
Originality/value
The magnetic field orientation was added into the traditional preparation process, and a set of process parameters with the best tribological properties were obtained through optimization. It is believed that this research will be of great theoretical and practical significance to develop both new brake materials and active control technology of the braking process in the future.