Role of Logistics Integration Capability in Enhancing Performance in Omni-Channel Retailing: Supply Chain Integration as Mediator

Although the importance and benefits of logistics integration in omni-channel (OC) retailing have been discussed in the literature, the impacts of logistics integration from the dimension of internal and external logistics remain unknown. To fill this gap, this study aims to investigate the relationships among internal and external logistics integration capabilities, supply-chain integration (SCI), and financial performance (FP) in OC retailing based on the dynamic capability view. An empirical study is conducted based on a survey of 230 OC retailers in China's market. Factor analysis and regression analysis are conducted to examine the hypotheses of the proposed conceptual model. The quantitative analyses show that the internal logistics integration capability is significantly related to the external logistics integration capability, and they both have positive effects on SCI, while the external logistics integration capability generates a higher impact (i.e., almost 1.5 times that of the internal logistics integration capability). The numerical results also demonstrate that the logistics integration capabilities and SCI have similar positive effects on FP (i.e., all the relevant regression coefficients show values around 0.25), and SCI plays a partial intermediary role in the relationships between logistics integration capabilities and FP. Furthermore, the quantitative evidence addresses the fact that the FP is not influenced by OC retailers' characteristics, indicating a fair business environment in the OC retail industry.

Variables Affecting Congregation's Trust and Mosque Organizational Performance: Acces la Success

Mosque is a non-profit community organization, where the purpose of its establishment is not to seek profit, so this objective makes it different from commercial organizations. "Takmir" (manager of a mosque)as a manager, has the responsibility and trust of the congregation. This was explanatory research with a quantitative approach. The level of a good trust can be improved by consinously improving the quality of variabels so that the mosque organization managed can run properly and correctly and the congregation's trust can be achived. When the good mosque governance concept with the principles, internal control and services are used properly, it will be able to improve organization performance. Congregation's trust in the takmir to improve the performance of the mosque's organization can be achieved by increasing the ability, kindness and integrity of the takmir. The congregation's trust in the takmir will affect its intensity in participating in activities organized by the mosque, in which it will directly affect the performance of the mosque's organization. For Next research, it is recommended to add a variable of the concept of leadership from organizational managers. The participation variable from the congregation and the community, and professional variables, Professional someone will have a positive and significant impact on the quality of work.

Current Advances in Nanomaterial-associated Micro and Nano-devices for SARS-CoV-2 Detection

COVID-19 spread rapidly around the world in 18 months, with various forms of variants caused by severe acute respiratory syndrome (SARS-CoV). This has put pressure on the world community and created an urgent need for understanding its early occurrence through rapid, simple, cheap, and yet highly accurate diagnosis. The most widely adopted method as of today is the real-time reverse-transcriptase polymerase chain reaction. This test has shown the potential for rapid testing, but unfortunately, the test is not rapid and, in some cases, displays false negatives or false positives. The nanomaterials play an important role in creating highly sensitive systems, and have been thought to significantly improve the performance of the SARSCoV- 2 protocols. Several biosensors based on micro-and nano-sensors for SARS-CoV-2 detection have been reported, and they employ multi-dimensional hybrids on sensing surfaces with devices having different sizes and geometries. Zero-to-three-dimension nanomaterial hybrids on sensing surfaces, including nanofilm hybrids for SARS-CoV-2 detection, were employed with unprecedented sensitivity and accuracy. Furthermore, the sensors were nanofluidic and mediated high-performance SARS-CoV-2 detection. This breakthrough has brought the possibility of making a biosystem on a chip (Bio-SoC) for rapid, cheap, and point-of-care detection. This review summarises various advancements in nanomaterial-associated nanodevices and metasurface devices for detecting SARS-CoV-2.

Guest editorial: Performance measurement in supply chains during disruptions: lessons from the COVID-19 pandemic

[...]it becomes essential to understand the PM aspects in the face of emergency situations such as COVID-19. Since the seminal article by Benita Beamon proposing new performance measures for evaluating supply chain performance, the literature has evolved. [...]the guest editors would also like to thank the authors for their contributions and for choosing our special issue as a relevant platform to communicate their research works. The insights drawn from this SI will provide them with effective guidance to help them design, implement and improve performance measurement systems capable of effectively measuring different supply chain processes and issues during unexpected and disruptive events.Table 1 Articles published in this special issue Article Title Purpose 1 Airline catering supply chain performance during pandemic disruption: a Bayesian network modelling approach This study aims to consider the impact of implementing Bayesian network (BN) modelling to measure SC performance in the airline catering during the pandemic context 2 The role of Industry 4.0 technologies on performance measurement systems of supply chains during global pandemics: an interval-valued intuitionistic hesitant fuzzy approach This study aims to investigate supply chain performance measurement systems (SCPMSs) that are suitable and applicable to evaluate SC performance during unexpected events such as global pandemics. [...]it considers the contribution of Industry 4.0 Disruptive Technologies (IDTs) to implement SCPMSs during such black swan events 3 A systematic literature review on supply chain resilience in SMEs: learnings from COVID-19 pandemic This paper presents the state-of-art literature on supply chain resilience in SMEs in the context of the coronavirus (COVID-19) pandemic and provides a comprehensive view of insights gained and gaps identified and suggests potential areas of future research 4 A proposed circular-SCOR model for supply chain performance measurement in the manufacturing industry during COVID-19 This study aims to determine which supply chain performance criteria come to the fore for the company under consideration to accelerate the transformation into high performance and circularity in supply chains, considering that the ability to analyse supply chain performances and ensure circularity in supply chains has become one of the factors whose importance has increased rapidly with COVID-19 5 How do food supply chain performance measures contribute to sustainable corporate performance during disruptions from the COVID-19 pandemic emergency?

Honesty-Based Social Technique to Enhance Cooperation in Social Internet of Things

The Social Internet of Things (SIoT) can be seen as integrating the social networking concept into the Internet of Things (IoT). Such networks enable different devices to form social relationships among themselves depending on pre-programmed rules and the preferences of their owners. When SIoT devices encounter one another on the spur of the moment, they seek out each other's assistance. The connectivity of such smart objects reveals new horizons for innovative applications empowering objects with cognizance. This enables smart objects to socialize with each other based on mutual interests and social aspects. Trust building in social networks has provided a new perspective for providing services to providers based on relationships like human ones. However, the connected IoT nodes in the community may show a lack of interest in forwarding packets in the network communication to save their resources, such as battery, energy, bandwidth, and memory. This act of selfishness can highly degrade the performance of the network. To enhance the cooperation among nodes in the network a novel technique is needed to improve the performance of the network. In this article, we address the issue of the selfishness of the nodes through the formation of a credible community based on honesty. A social process is used to form communities and select heads in these communities. The selected community heads having social attributes prove effective in determining the social behavior of the nodes as honest or selfish. Unlike other schemes, the dishonest nodes are isolated in a separate domain, and they are given several chances to rejoin the community after increasing their honesty levels. The proposed social technique was simulated using MATLAB and compared with existing schemes to show its effectiveness. Our proposed technique outperforms the existing techniques in terms of throughput, overhead, packet delivery ratio (PDR), and packet-delivery latency.

EfficientNet MW: A Mask Wearing Detection Model with Bidirectional Feature Fusion Network

To solve the problem of missing model detection for small targets, occluded targets, and crowded targets scenarios in mask detection, we propose an end-to-end mask-wearing detection model based on a bidirectional feature fusion network. Firstly, to improve the ability of the model to extract features, we introduce the modified EfficientNet as the backbone network in the model. Secondly, for the prediction network, we introduce depth-wise separable convolution to reduce the amount of model parameters. Lastly, to improve the performance of the model on small targets and occluded targets, we propose a bidirectional feature fusion network and introduce a spatial pyramid pooling network. We evaluate our proposed method on a real-world data set. The mean average precision of the model is 87.54%. What’s more, our proposed method achieves better performance than the comparison approaches in most cases.

An Innovative School Design Based on a Biophilic Approach Using the Appreciative Inquiry Model: Case Study Scandinavia

To understand the school’s role in society and its works, it became essential to reevaluate its functions and importance for society after the aggressive attack of the COVID-19 pandemic. Thus, a new educational space design represents a powerful and required tool for stimulating creativity and increasing concentration, motivation, and assimilation of knowledge for future generations. The article will use appreciative inquiry as a method that works with perspective ideas readings doted by high positive human sensitivity. It also represents a powerful tool for the students’ opinions about the teaching spaces and environments. To improve the performance of educational institutions and schools, considering the sustainability concepts and biophilic designs has become an urgent necessity within the Scandinavian countries and in the world in general. The scientific research and theoretical analysis within the biophilic theory have been conducted to see how the designer can integrate the nature components holistically in the educational environment based on spatial, visual, and ecological integration concepts. The study aims to develop knowledge about applying biophilia as a phenomenon in educational institutes of Scandinavia where the students among others are the main decision-maker. The article’s main finding is that students dream of free open teaching spaces integrated with nature, where the biophilic theory frameworks are suitable to form this sustainable model that enables educational institutions and schools to improve their performance within different stages of the study.

Forecasting of Automobile Sales Based on Support Vector Regression Optimized by the Grey Wolf Optimizer Algorithm

With the development of the Internet and big data, more and more consumer behavior data are used in different forecasting problems, which greatly improve the performance of prediction. As the main travel tool, the sales of automobiles will change with the variations of the market and the external environment. Accurate prediction of automobile sales can not only help the dealers adjust their marketing plans dynamically but can also help the economy and the transportation sector make policy decisions. The automobile is a product with high value and high involvement, and its purchase decision can be affected by its own attributes, economy, policy and other factors. Furthermore, the sample data have the characteristics of various sources, great complexity and large volatility. Therefore, this paper uses the Support Vector Regression (SVR) model, which has global optimization, a simple structure, and strong generalization abilities and is suitable for multi-dimensional, small sample data to predict the monthly sales of automobiles. In addition, the parameters are optimized by the Grey Wolf Optimizer (GWO) algorithm to improve the prediction accuracy. First, the grey correlation analysis method is used to analyze and determine the factors that affect automobile sales. Second, it is used to build the GWO-SVR automobile sales prediction model. Third, the experimental analysis is carried out by using the data from Suteng and Kaluola in the Chinese car segment, and the proposed model is compared with the other four commonly used methods. The results show that the GWO-SVR model has the best performance of mean absolute percentage error (MAPE) and root mean square error (RMSE). Finally, some management implications are put forward for reference.

Artificial Intelligence Based Model for Prediction of Students' Performance: A Case Study of Synchronous Online Courses During the COVID-19 Pandemic

Lack of student persistence and retention is significantly hurting the US in producing the required number of qualified graduates, especially in STEM fields. Although many factors contribute to students falling off track, one of the controllable factors is the identification of at-risk students followed by early intervention. Predicting the performance of students enables educators to single out struggling and highly talented students. Struggling students are often identified very late into an academic year, thus leaving little to no time for seeking consultation and determining the best course of action to improve performance. Some of such struggling students resort to dishonest means to catch up or make up at the last minute resulting in a higher number of academic integrity violations being observed and reported. Recently, the COVID-19 pandemic further corroborated the presence of such challenges. This research explores the possibility of using artificial intelligence to identify key elements in small datasets which could contribute to the development of a predictive student performance solution. A small set of data obtained through systematic data collection was used to train a predictive algorithm and aid in the analysis of in-class learning, which would lead to a viable student performance predictive solution. The data was collected for 133 students from a total of four sections of three different courses. With a limited amount of data, we were still able to construct a predictive solution able to produce valuable insights into the behaviors of students. The model's resulting accuracy on the test set is 0.85 and the model indicates that the earliest time to begin predictions is right after the midterm exam. The model performs well in its task to predict student performance and identify correlations between different variables. However, it is at this time subject to limited data which although treatable, can affect the accuracy and its ability to predict a final score numerically. This work paves the ground for future studies on the use of machine learning using in-class learning data, analyzing student learning as a function of time within each session rather than by grades alone.

Intellectual capital and performance of automotive manufacturers: the role of strategic thinking

Purpose>The purpose of this paper is to investigate the influence of intellectual capital (IC) on the performance of Malaysian automotive manufacturing firms. It also examines the role of strategic thinking (ST) as a moderating variable in the relationship between IC and performance in these firms.Design/methodology/approach>This study used a quantitative approach, with an initial sample of 228 firms in Malaysia. Partial least squares structural equation modelling (PLS-SEM) was employed to test the study hypotheses.Findings>The results of the PLS-SEM analysis are as follows: Human capital (HC) and relational capital (RC) have significant effect on performance, but not structural capital (SC). ST has no moderating effect on the relationship between RC or SC and performance although it does moderate the relationship between performance and HC.Research limitations/implications>Together with the government, CEOs hold responsibility for ensuring that organizations practice effective ST and IC. With the assistance of government, CEOs should exert every effort to be leaders in this matter. In addition, CEOs of automotive manufacturing firm should reduce their emphasis on classical ways of managing organizations processes.Practical implications>The findings offer guidance to automotive firms considering how to develop IC and ST to improve performance, especially in Malaysia and Southeast Asia.Originality/value>This is the first study to examine the moderating effect of ST on the relationship between IC and performance worldwide.

Impacts of Collaborative Partnership on the Performance of Cold Supply Chains of Agriculture and Foods: Literature Review

Collaboration in a supply chain continuously proves its role in increasing the performance of supply chains, which attracts the attention of both academia and practitioners, specifically, how to generate higher impacts of collaborative partnership on the performance of supply chains and measure them. In cold supply chains of agriculture and foods, the vital need for collaboration becomes even more significant to improve the performance. Therefore, this paper reviews relevant articles derived from the Web of Science and Scopus databases. Via the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), the research team classifies the types of collaborative partnership in cold agriculture and food supply chains, issues of the literature when analyzing collaboration impacts on the performance of CSCs of agriculture and foods, and finally, the opportunities for the future research to boost the collaboration practices in these cold chains. Following this sequence, 102 articles were eventually extracted for the systematic review to identify themes for not only addressing the review questions but also highlighting future research opportunities for both development of partnership integration and performance of the cold chains of agriculture and foods.

Image Encryption Using RSA Algorithm

In the current covid pandemic situation, secure online transmission of data has the highest precedence over other activities. For providing computational hardness that is for making tough to break the key for finding the unique message, there are various algorithms are present. For secure data transmission, many researchers have applied different cryptography algorithms and in order to improve the level of information security, different hybrid cryptography algorithms have been proposed. In cryptography algorithm implementation, key management plays a major role. For this reason, we have applied an image encryption technique in which a random image is considered as the key. Using the random image as a key, we have encrypted another image as information using the RSA algorithm. The comparison of the proposed method is done with the traditional approach and concluded that the cryptography algorithm implemented using an image as key provides more security in terms of encryption and decryption time. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Research on the Relationship between Digital Transformation and Performance of SMEs

Objective: Through an empirical analysis of the performance of SMEs undergoing digital transformation, this study attempts to identify the influencing factors that determine their sustainable development to provide reference for academic researchers and industrial decision makers. Method: This study first uses an interview method to investigate the impact of SMEs’ three main resources on digital transformation: digital technology, employee digital skills, and digital transformation strategy. Second, we assess the impact of digital transformation on financial performance. Using the structural equation model, 335 valid questionnaires were recovered through the questionnaire method, and the key factors were identified using SPSS and SPSSAU tools. Results: In the Chinese context, digital transformation affects SME performance, and the three resources mentioned above are positively correlated with SMEs’ digital transformation. Digital transformation is positively correlated with performance, and it is the mediator of the impact of digital transformation strategies on performance. Conclusion: For SMEs, focusing on investing in digital technologies, employee digital skills, and digital transformation strategies are three key factors that are beneficial for digital transformation, thus helping to improve performance and maintain their sustainable development.

Effect of Post-Activation Potentiation on Sprint Performance after Combined Electromyostimulation and Back Squats

Post-activation potentiation (PAP) is a phenomenon which can improve force performance executed after a previous conditioning activity. PAP is usually evoked through heavy resistance, but many new methods are being suggested that acutely improve performance in post-activation potentiation protocols. The purpose of this study was to examine the effect of simultaneous application of Smith machine back squats (BS) with electromyostimulation (EMS) on sprint performance. Sixteen male (age = 22.9 ± 2.3 years, body mass = 79.9 ± 13.8 kg, BS one-repetition maximum (1 RM) = 120.5 ± 17.3) amateur football and rugby players volunteered for this study. Participants randomly performed PAP protocols (CON = no load, BS = 3 × 85% of 1 RM BS, EMS = 3 × weightless squat with electric current and BS + EMS = 3 × 85% 1 RM BS with electric current) on four different days with at least 48 h intervals. Participants rested passively for 7 min after preloads and performed the 30 m sprint test. Sprint times for 10 and 30 m were recorded for each condition. As a result, no significant difference was found in the 10 m (p = 0.13) and 30 m (p = 0.10) sprint performance between the preload protocols. The effect size was found to be trivial (ηp2: 0.13 for 10 m;ηp2: 0.11 for 30 m). In individual results, the 10 m sprint performance of five participants and 30 m sprint performance of two participants decreased in BS, EMS, or BS + EMS conditions compared with CON. No PAP effect in other participants was observed. In conclusion, preloads did not affect 10 m and 30 m sprint performance of football and rugby players. It can be said that the applied PAP protocols or physical exertion alone may cause fatigue in some individuals.

The Reform of Engineering Professional Online Education Courses by Artificial Intelligence and Wireless Network Technology in the Context of Engineering Certification

The problems existing in the online education courses of engineering majors in colleges and universities are studied, and the online education platform under the background of engineering certification is designed and researched. Artificial intelligence (AI) technology and wireless network technology are used in the in-depth study of online education courses. In terms of AI, the storage of data is specifically divided into three levels, namely, the source layer (operational data layer), the historical storage layer, and the data model layer. Data analysis includes the total number of data, spatial scope, and period, etc. The results show that the overall response time of the designed platform to the operation of university teachers and students is controlled within 3 s. The test results of two online education platforms show that the performance of the designed online education platform is relatively stable. It provides hardware support for online curriculum reform. In addition, in terms of the platform test pass rate, after many tests, the online education platform security test pass rate is above 98%. In the end, these conclusions are drawn: AI technology and wireless network technology can effectively improve the performance of online education platforms. Meanwhile, this can also further improve the learning efficiency of online education courses for college engineering students.

SMEs Performance in Malaysia: The Role of Contextual Ambidexterity in Innovation Culture and Performance

Small and medium-sized enterprises (SMEs) in both the industrial and service sectors have been identified as the drivers of Malaysia’s fast economic growth. However, SMEs are faced with an inherent issue of lack of resources and capabilities which constrains the ability of SMEs to improve performance. Due to this, it is critical for SMEs to understand and develop an important capability that supports them in facing a dynamic and competitive business environment. This study examines the mediating role of contextual ambidexterity as a dynamic capability in the relationship between innovation culture and SME performance. The online surveys were carried out starting from 5th July until 25th July 2021. A total of 277 SMEs in Selangor, Malaysia participated in this study and Covariance-Based Structural Equation modeling analysis was utilized to test the hypotheses. The findings show that in terms of direct relationships, innovation culture has a significant positive relationship with contextual ambidexterity, while innovation culture and contextual ambidexterity have a significant positive relationship with SME performance. The findings showed that contextual ambidexterity is significant as a mediator in the relationship between innovation culture and SME performance. This study makes an important contribution to the management field by highlighting the role of contextual ambidexterity, which is often the focus of large companies. These findings support the notion of dynamic capability that accentuates the importance of developing capabilities in dealing with dynamic and challenging situations.

Successful Reboot of High-Performance Sporting Activities by Japanese National Women's Handball Team in Tokyo, 2020 during the COVID-19 Pandemic: An Initiative Using the Japan Sports-Cyber Physical System (JS-CPS) of the Sports Research Innovation Project (SRIP).

The COVID-19 pandemic has negatively impacted sporting activities across the world. However, practical training strategies for athletes to reduce the risk of infection during the pandemic have not been definitively studied. The purpose of this report was to provide an overview of the challenges we encountered during the reboot of high-performance sporting activities of the Japanese national handball team during the 3rd wave of the COVID-19 pandemic in Tokyo, Japan. Twenty-nine Japanese national women's handball players and 24 staff participated in the study. To initiate the reboot of their first training camp after COVID-19 stay-home social policy, we conducted: web-based health-monitoring, SARS-CoV-2 screening with polymerase chain reaction (PCR) tests, real-time automated quantitative monitoring of social distancing on court using a moving image-based artificial intelligence (AI) algorithm, physical intensity evaluation with wearable heart rate (HR) and acceleration sensors, and a self-reported online questionnaire. The training camp was conducted successfully with no COVID-19 infections. The web-based health monitoring and the frequent PCR testing with short turnaround times contributed remarkably to early detection of athletes' health problems and to risk screening. During handball, AI-based on-court social-distance monitoring revealed key time-dependent spatial metrics to define player-to-player proximity. This information facilitated appropriate on- and off-game distancing behavior for teammates. Athletes regularly achieved around 80% of maximum HR during training, indicating anticipated improvements in achieving their physical intensities. Self-reported questionnaires related to the COVID management in the training camp revealed a sense of security among the athletes that allowed them to focus singularly on their training. The challenges discussed herein provided us considerable knowledge about creating and managing a safe environment for high-performing athletes in the COVID-19 pandemic via the Japan Sports-Cyber Physical System (JS-CPS) of the Sports Research Innovation Project (SRIP, Japan Sports Agency, Tokyo, Japan). This report is envisioned to provide informed decisions to coaches, trainers, policymakers from the sports federations in creating targeted, infection-free, sporting and training environments.