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To support achieving sustainable development goals (SDGs), we integrated science, technology, engineering and math (STEM) and extended reality technologies into an artificial intelligence (AI) learning activity. We developed Feature City to facilitate students' learning of AI concepts. This study aimed to explore students' learning outcomes and behaviors when using Feature City.

Junior high school students were the subjects who used Feature City in an AI learning activity. The learning activity consisted of 90-min sessions once per week for five weeks. Before the learning activity, the teacher clarified the learning objectives and administered a pretest. The teacher then instructed the students on the features, supervised learning and unsupervised learning units. After the learning activity, the teacher conducted a posttest. We analyzed the students' prior knowledge and learning performance by evaluating their pretest and posttest results and observing their learning behaviors in the AI learning activity.

(1) Students used Feature City to learn AI concepts to improve their learning outcomes. (2) Female students learned more effectively with Feature City than male students. (3) Male students were more likely than female students to complete the learning tasks in Feature City the first time they used it.

Within SDGs, this study used STEM and extended reality technologies to develop Feature City to engage students in learning about AI. The study examined how much Feature City improved students' learning outcomes and explored the differences in their learning outcomes and behaviors. The results showed that students' use of Feature City helped to improve their learning outcomes. Female students achieved better learning outcomes than their male counterparts. Male students initially exhibited a behavioral pattern of seeking clarification and error analysis when learning AI education, more so than their female counterparts. The findings can help teachers adjust AI education appropriately to match the tutorial content with students' AI learning needs.

The purpose of this paper is to present a system that analyzes trustworthiness and ranks applications to improve the search experience.

The system adopts pointwise mutual information to calculate comment semantics. It examines subjective (signed opinions, anonymous opinions and star ratings) and objective factors (download numbers, reputation ratings) before filtering, ranking and displaying). The authors invited three experts to check three categories and compared the results using Spearman and two statistics.

A high correlation between the proposed system and the expert ranking system suggests that the system can act as decision support.

First, the authors have only tested the correlation between the proposed system and an expert ranking system; user satisfaction was not evaluated. The authors plan to conduct a later survey to gather user feedback. Second, the ranking system evaluates applications using fixed weights and disregards time. Therefore, in the future, the authors plan to enable their system to weight recent records over older ones.

User discussion forums, although helpful, have drawbacks. Not all reviews are trustworthy, and forums provide no filtering mechanisms to combat information overload. The solution to this is the authors’ system that crawls a forum, filters information, analyzes the trustworthiness of each comment and ranks the application for the user.

This paper develops a formula to analyze the trustworthiness of opinions, enabling the system to act as decision support when no professional advice is available.

The logistics industry has benefited hugely from the growth of e-commerce. The logistics and transportation industry operators have realized that higher-quality service and logistics management provides the competitive edge as the primary channel of communication with consumers. Digital voice assistants (DVA) is an information system based on an artificial intelligence framework that can interact through voice, such that a deliveryman can query for or use information in a relatively short time. The purpose of this paper is to explore the value of DVA in logistic service.

This study aims to develop a framework for innovation and logistics service capabilities of logistics and transportation services to structure a model based on the analysis hierarchy process method to discuss the factors considered when adopting DVA.

The results of this study implied that common problem and expectations of current operators in the delivery of goods and their expectations of DVA.

Innovative operations and planning are possible with information technology-enabled logistic services. It is important to identify relevant DVA development avenues.

The purpose of this study is to show which factors are significant to the logistics and transportation industry using DVA to aid the deliverymen, and it provides guidance for manager evaluating adopted DVA and its object.

This study aims to explore the relationships among the five components of supply chain (SC) resilience (SCRES): visibility, velocity, flexibility, robustness and collaboration and their impacts on the SC performance under disruption (SCPUD).

Five SCRES components are identified from the literature review and data are collected using an web survey from 113 manufacturing companies in Taiwan. The data are analyzed by structured equation modeling with the partial least square solution. Two-stage least-squares (2SLS) regression was used to test the potential endogeneity of SC collaboration (SCC).

The results reveal that SCC is an exogenous driver of SCRES; it directly affects visibility, velocity, flexibility, robustness and SCPUD. Furthermore, SC flexibility is the only component of SC agility that directly affects SCPUD; it is influenced directly by SC velocity and indirectly by SC visibility through SC velocity. SC visibility is a vital agility component that positively influences SC velocity and SC robustness.

The data in this study are cross-sectional and the sample size of 113 is relatively small. The relationship between SC robustness and SCPUD needs a longer observation period to reveal. The logistic issue in the shortage of carriers caused by the pandemic has been overlooked.

A firm should enhance its collaboration and flexibility in the SC as they both are the critical antecedents of SC performance (SCP) during the disruption period.

This study integrates visibility, velocity, flexibility, robustness and collaboration into a complete framework of SCRES. The dependent variable, SCPUD, measures SC performance (SCP) under the disruption caused by the COVID-19 pandemic. It is the first study to investigate the associations of the six constructs in a research model.

The rise of artificial intelligence (AI) applications has driven enterprises to provide many intelligent services to consumers. For instance, customers can use chatbots to make relevant inquiries and seek solutions to their problems. Despite the development of customer service chatbots years ago, they require significant improvements for market recognition. Many customers have reported negative experiences with customer service chatbots, contributing to resistance toward their use. Therefore, this study adopts the innovation resistance theory (IRT) perspective to understand customers’ resistance to using chatbots. It aims to integrate customers’ negative emotions into a predictive behavior model and examine users’ functional and psychological barriers.

In this study, we collected data from 419 valid individuals and used structural equation modeling to analyze the relationships between resistance factors and negative emotions.

The results confirmed that barrier factors affect negative emotions and amplify chatbot resistance influence. We discovered that value and risk barriers directly influence consumer use. Moreover, both functional and psychological barriers positively impact negative emotions.

This study adopts the innovation resistance theory perspective to understand customer resistance to using chatbots, integrates customer negative emotions to construct a predictive behavior model and explores users’ functional and psychological barriers. It can help in developing online customer service chatbots for e-commerce.

The purpose of this paper is to prove the importance of both cluster leadership and identification on cluster innovation.

The case studies presented in this study involve a cluster by micro-enterprises in Yilan, Taiwan. Data collected during interviews, observations and secondary data provide understanding and practices for the impact of cluster identification on cluster innovation.

This study proved: first, the importance of cluster identification on innovation by representing the need of consensus and collaboration of the members in conducting innovation actions; and second, the cluster identification is influenced by the cluster leadership by showing high satisfaction of the leader, close interaction between the members and high identification with the cluster.

This study predicts the ongoing cluster innovation activities will be achieved due to the transformational leadership and high cluster identification.

This study enriches the factors of cluster innovation accomplishment and proposes the important of cluster identification, which has not been discussed much in the past.

The reaction dynamics of combustible clouds at high temperatures and pressures are a common form of energy output in aerospace and explosion accidents. The cloud explosion process is often affected by the external initial conditions. This study aims to numerically study the effects of airflow velocity, initial temperature and fuel concentration on the explosion behavior of isopropyl nitrate/air mixture in a semiconstrained combustor.

The discrete-phase model was adopted to consider the interaction between the gas-phase and droplet particles. A wave model was applied to the droplet breakup. A finite rate/eddy dissipation model was used to simulate the explosion process of the fuel cloud.

The peak pressure and temperature growth rate both decrease with the increasing initial temperature (1,000–2,200 K) of the combustor at a lower airflow velocity. The peak pressure increases with the increase of airflow velocity (50–100 m/s), whereas the peak temperature is not sensitive to the initial high temperature. The peak pressure of the two-phase explosion decreases with concentration (200–1,500 g/m³), whereas the peak temperature first increases and then decreases as the concentration increases.

Chain explosion reactions often occur under high-temperature, high-pressure and turbulent conditions. This study aims to provide prevention and data support for a gas–liquid two-phase explosion.

Sustained turbulence is realized by continuously injecting air and liquid fuel into a semiconfined high-temperature and high-pressure combustor to obtain the reaction dynamic parameters of a two-phase explosion.