Search results

The health crisis has highlighted the shortcomings of the industry sector which has revealed its vulnerability. To date, there is no guarantee of a return to the “world before”. The ability of companies to cope with these changes is a key competitive advantage requiring the adoption/mastery of industry 4.0 technologies. Therefore, companies must adapt their business processes to fit into similar situations.

The proposed methodology comprises three steps. First, a comparative analysis of the existing CPSs is elaborated. Second, following this analysis, a deep learning driven CPS framework is proposed highlighting its components and tiers. Third, a real industrial case is presented to demonstrate the application of the envisioned framework. Deep learning network-based methods of object detection are used to train the model and evaluation is assessed accordingly.

The analysis revealed that most of the existing CPS frameworks address manufacturing related subjects. This illustrates the need for a resilient industrial CPS targeting other areas and considering CPSs as loopback systems preserving human–machine interaction, endowed with data tiering approach for easy and fast data access and embedded with deep learning-based computer vision processing methods.

This study provides insights about what needs to be addressed in terms of challenges faced due to unforeseen situations or adapting to new ones. In this paper, the CPS framework was used as a monitoring system in compliance with the precautionary measures (social distancing) and for self-protection with wearing the necessary equipments. Nevertheless, the proposed framework can be used and adapted to any industrial or non-industrial environments by adjusting object detection purpose.

The purpose of this paper is to introduce a double-stator switched reluctance machine (DS-SRM) for electric vehicles (EVs) and to propose multi-mode operations for this machine.

Analysis of flux linkage distributions and torque characteristics using finite element method (FEM). Building a dynamic simulation model based on electromagnetic characteristics, mathematical equations and mechanical motion equations of the DS-SRM drive system. The paper proposes multi-mode operations (inner-stator excitation mode, outer-stator excitation mode and double-stator excitation mode) based on motor working regions. It also conducts simulation and experimental results to verify the effectiveness of the proposed multi-mode operations strategies and control schemes.

There is almost no electromagnetic coupling between the inner and outer stators due to the specially designed rotor structure and optimized windings polarity configuration. Analysis of flux linkage distributions and torque characteristics verified the independence of inner and outer stators. Proposal of multi-mode operations and corresponding control rules achieved the smooth switching between different modes.

The paper introduced the DS-SRM for EVs and proposed multi-mode operations, along with control rules, to optimize its performance. The specially designed rotor structure, optimized winding polarity configuration, and the proposed multi-mode operations contribute to the originality of the research.

Endorsement marketing has been widely used to generate consumer attention, interest and purchase decisions among targeted audiences. Internet celebrities who become famous on the Internet are dependent on strategic intimacy to appeal to their followers. Our study aims to examine how multiple exposures to Internet celebrity endorsements influence consumers’ click and purchase decisions in the context of influencer marketing.

Based on a unique and representative dataset, the authors first model consumers’ choices for clicks and purchases with two panel fixed-effect logit models linking clicks and purchases with the frequency of exposure to Internet celebrity endorsement. To further control the endogeneity produced by the intercorrelation between the click and purchase models, the authors also adopt the two-stage Heckman probit structure to jointly estimate the two models using Maximum Likelihood Estimation. Robustness checks confirm the effectiveness of the models.

The results suggest that Internet celebrity endorsement plays a significant role in bringing referral traffic to e-commerce sites but is less helpful in affecting conversion to sales. The impact of repetitive Internet celebrity endorsements on consumers’ click decisions is U-shaped, but the role of Internet celebrities as online retailers will “shape-flip” this relationship to a negative linear relation.

Our study is the first to investigate the repetitive exposure effect of Internet celebrity endorsement. The results show a contradictory pattern with a wear-out effect of repetition in the advertising literature. This is the first study to show how the endorsing self, which is a common business model in influencer marketing, moderates the effectiveness of influencer marketing.

This paper aims to examine the causation linking financial technology to economic growth in the East African Community states from 1997 to 2019.

Autoregressive distributed lag is used. Gross domestic product per capita proxies economic growth, automated teller machines, point of sale, debit card ownership and mobile banking measure financial technology.

The results unveil a significant relationship between financial technology and economic growth. The findings show bidirectional causality between automated teller machine and economic growth, with unidirectional causation from economic growth to point of sales and internet banking, mobile banking and government effectiveness to economic growth. The error correction term is negatively significant, demonstrating a long-term convergence between Fintech measures and economic growth.

The governments should effectively enact and implement policies that protect investments in financial technologies to boost economic growth in the East African Community countries. The government should reduce taxes on financial technology equipment and related services. The use of automated teller machine, debit card ownership and internet banking should be encouraged through cashless transactions. Financial institutions should adopt cashless operation policies to encourage the use of financial technologies.

Research results on the bond between financial technology and economic growth are not conclusive. These studies demonstrate that technological innovations are double edged-swords, with both positive and negative sides. The results are conflicting; some reveal positive relationships, while others show negative links. Hence, research is required to fill the lacuna.

By introducing Stacking algorithm to solve the underfitting problem caused by insufficient data in traditional machine learning, this paper provides a new solution to the cold start problem of entrepreneurial borrowing risk control.

The authors introduce semi-supervised learning and integrated learning into the field of migration learning, and innovatively propose the Stacking model migration learning, which can independently train models on entrepreneurial borrowing credit data, and then use the migration strategy itself as the learning object, and use the Stacking algorithm to combine the prediction results of the source domain model and the target domain model.

The effectiveness of the two migration learning models is evaluated with real data from an entrepreneurial borrowing. The algorithmic performance of the Stacking-based model migration learning is further improved compared to the benchmark model without migration learning techniques, with the model area under curve value rising to 0.8. Comparing the two migration learning models reveals that the model-based migration learning approach performs better. The reason for this is that the sample-based migration learning approach only eliminates the noisy samples that are relatively less similar to the entrepreneurial borrowing data. However, the calculation of similarity and the weighing of similarity are subjective, and there is no unified judgment standard and operation method, so there is no guarantee that the retained traditional credit samples have the same sample distribution and feature structure as the entrepreneurial borrowing data.

From a practical standpoint, on the one hand, it provides a new solution to the cold start problem of entrepreneurial borrowing risk control. The small number of labeled high-quality samples cannot support the learning and deployment of big data risk control models, which is the cold start problem of the entrepreneurial borrowing risk control system. By extending the training sample set with auxiliary domain data through suitable migration learning methods, the prediction performance of the model can be improved to a certain extent and more generalized laws can be learned.

This paper introduces the thought method of migration learning to the entrepreneurial borrowing scenario, provides a new solution to the cold start problem of the entrepreneurial borrowing risk control system and verifies the feasibility and effectiveness of the migration learning method applied in the risk control field through empirical data.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

By considering the challenges of Industry 5.0, the purpose of this study is to analyze the role of heuristic factors in the technical qualities and emotions of Millennials and Generation Z (Gen Z) to assess their acceptance of the use of artificial intelligence (AI) devices such as robots. For this purpose, this paper uses the innovative AI device use acceptance (AIDUA) framework. This research evaluates the implications of human–machine interactions for the usage of robots and AI in daily life.

The proposed AIDUA model is tested using data collected from Millennials and Gen Z. First, a principal components analysis technique is used to validate each measure. Second, a multiple regression analysis using IBM SPSS 26.0 is conducted.

The results of this study suggest that human–machine interaction is a part of a complex process in which there are different elements determining individuals’ acceptance of the use of AI devices during daily life. This paper outlines both the theoretical and practical implications. This study enriches the AIDUA model by connoting it with features and emotions belonging to the younger generation. Additionally, this research offers technology companies suggestions for addressing future efforts on technical performance and on the alignments of the expectations of young people in Society 5.0.

First, the originality of this paper lies in highlighting the binary role of emotions in triggering the use of AI devices and robots. Second, the focus on Millennials and Gen Z offers a new lens for the interpretation of longitudinal phenomena in the adoption of AI. Finally, the findings of this paper contribute to the development of a new perspective regarding a “heartly collaborative” approach in Society 5.0.

The advent of Internet of Things, cloud computing and advanced computing has endowed smart manufacturing environments with resilience, reconfigurability and intelligence, resulting in the emergence of novel capabilities. These capabilities have significantly reshaped the manufacturing ecosystem, enabling it to effectively navigate uncertainties. The purpose of this study is to assess the operational transformations resulting from the implementation of smart manufacturing, which distinguish it from conventional systems.

A list of qualitative and quantitative smart manufacturing performance metrics (SMPMs) are initially suggested and categorized into strategic, tactical and operational levels. The SMPMs resemble the capabilities of smart manufacturing systems to manage disruptions due to uncertainties. Then, industry and academia experts validate the SMPMs through the utilization of the Delphi method, enabling the ranking of the SMPMs.

The proposition of the SMPMs serves as a metric to assess the digital transformation capabilities of smart manufacturing systems. In addition, the ranking of the proposed SMPMs shows a degree of relevance of the measures in smart manufacturing deployment and managing the disruptions caused due to the COVID-19 pandemic

The findings benefit managers, consultants, policymakers and researchers in making appropriate decisions for deploying and operationalizing smart manufacturing systems by focusing on critical SMPMs.

The research provides a metric to assess the operational transformations during the deployment of smart manufacturing systems. Also, it states the role of the metric in managing the potential disruptions that can alter the performance of the business due to the COVID-19 pandemic.

We studied the relationship between job engagement and systematic problem solving (SPS) among shop-floor employees and how lean production (LP) and Internet of Things (IoT) systems moderate this relationship.

We collected data from a sample of 440 shop floor workers in 101 manufacturing work units across 33 plants. Because our data is nested, we employed a series of multilevel regression models to test the hypotheses. The application of IoT systems within work units was evaluated by our research team through direct observations from on-site visits.

Our findings indicate a positive association between job engagement and SPS. Additionally, we found that the adoption of lean bundles positively moderates this relationship, while, surprisingly, the adoption of IoT systems negatively moderates this relationship. Interestingly, we found that, when the adoption of IoT systems is complemented by a lean management system, workers tend to experience a higher effect on the SPS of their engagement.

One limitation of this research is the reliance on the self-reported data collected from both workers (job engagement, SPS and control variables) and supervisors (lean bundles). Furthermore, our study was conducted in a specific country, Italy, which might have limitations on the generalizability of the results since cross-cultural differences in job engagement and SPS have been documented.

Our findings highlight that employees’ strong engagement in SPS behaviors is shaped by the managerial and technological systems implemented on the shop floor. Specifically, we point out that implementing IoT systems without the appropriate managerial practices can pose challenges to fostering employee engagement and SPS.

This paper provides new insights on how lean and new technologies contribute to the development of learning-to-learn capabilities at the individual level by empirically analyzing the moderating effects of IoT systems and LP on the relationship between job engagement and SPS.

The lack of conceptual approaches for organizing and expressing capabilities, usage and impact of intelligent machines (IMs) in work settings is an obstacle to moving beyond isolated case examples, domain-specific studies, 2 × 2 frameworks and expert opinion in discussions of IMs and work. This paper's purpose is to illuminate many issues that often are not addressed directly in research, practice or punditry related to IMs. It pursues that purpose by presenting an integrated approach for identifying and organizing important aspects of analysis and evaluation related to IMs in work settings. 

This paper integrates previously published ideas related to work systems (WSs), smart devices and systems, facets of work, roles and responsibilities of information systems, interactions between people and machines and a range of criteria for evaluating system performance.

Eight principles outline a straightforward and flexible approach for analyzing and evaluating IMs and the WSs that use them. Those principles are based on the above ideas.

This paper provides a novel approach for identifying design choices for situated use of IMs. The breadth, depth and integration of this approach address a gap in existing literature, which rarely aspires to this paper’s thoroughness in combining ideas that support the description, analysis, design and evaluation of situated uses of IMs.