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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.

This article investigates the impact of consumers’ promotion strategies on the effectiveness of promotional campaigns for subscribing to Over-the-Top (OTT) streaming services, from a prospective theoretical perspective. Additionally, it explores the moderating effects of platform type and subscriber type on promotion strategies in the context of OTT streaming services.

This study employs an experimental design to separately examine the promotion strategies of subscribers. The first part investigates the effect of promotion strategies on promotional effectiveness, regardless of platform and subscriber types, while the second part considers the moderating effects of both platform type and subscriber type on promotional effectiveness.

The analysis reveals that renewed subscribers exhibit a positive attitude toward incremental promotions and have stronger intentions to subscribe and recommend. Nonrenewed subscribers, in contrast, prefer discount promotions, demonstrating a positive attitude toward them and expressing higher intentions to subscribe and recommend.

This study applies prospect theory to OTT promotion strategies, uncovers the psychological drivers behind promotion effectiveness and examines the moderating role of subscriber type and platform type, thereby providing actionable insights to improve consumers’ attitudes and behavioral intentions.

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 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.

The purpose of this paper is to find the optimal values of process parameters in injection molding when both warpage and shrinkage are minimized.

In finding the optimal values, advantages of finite element software, Moldflow, and dual response surface method (dual RSM) combined with the nonlinear programming technique by Lingo are exploited. Considering the nine process parameters, injection time, injection pressure, packing pressure, packing time, cooling time, coolant temperature, mold‐open time, melting temperature and mold surface temperature, a series of mold analyses are performed to exploit the warpage and shrinkage data. In the analyses, warpage is considered the primary response, whereas shrinkage is the secondary response.

The results indicate that dual RSM combined with the nonlinear programming technique can outperform the Taguchi's optimization method. The optimal process values are also confirmed by re‐running experiments on Moldflow. Additionally, an auxiliary dual RSM model is proposed to search for a better result based on the given findings by dual RSM at the cost of running extra experiments. Based on dual RSM, a multiple objective optimization for the whole plastic product is finally suggested to integrate the dual RSM models that are developed for the individual nodes or edges.

This paper proposes a new method to find the optimal process for plastic injection molding.

This paper examines the framing of digital transformation. The research questions are specified as follows: what are the different types of framing strategies in response to digital transformation? How do the strategies differ across organizations? Theoretically, the authors draw on the framing perspective to emphasize the use of linguistic frames in shaping innovation and change processes. Empirically, the authors choose to study the Taiwanese sectors, including publicly governed entities, traditional private business or technology-based ventures.

The authors’ approach combines topic modeling and qualitative analysis. Using data collected from newspaper and magazine articles, the authors employ topic modeling to generate a set of distinctive framings that Taiwanese actors typically adopt to motivate and justify their digital move. The authors also conduct personal interviews to qualitatively complement the authors’ topic modeling analysis and to identify the rationale behind the linguistic framings and the strategic differences brought about by the various organizations.

The authors identify five topics that the Taiwanese actors commonly used in the framing of digital transformation. These topics or frames are labeled as cross-domain coordination, market demand, intelligent technology, global trend and competition and digital innovation. The practical use of the framings is contingent on organizational characteristics. Furthermore, the authors show how the framings can be classified as either positive framing (e.g. winning the next war) or negative framing (e.g. innovate or die), generally applicable to organizations around the world struggling to cope with digital disruption.

The authors’ study has two research implications. First, the authors extend the appreciation of the digital transformation from the usual concern with technological and business model innovations to linguistic or framing practices. Second, the authors enrich the framing analysis by emphasizing a practice or contingency perspective based on sector difference. The findings are subject to the limitations of the choice of only established and reputable media outlets, the diatextual reading and filtering of useful articles for topic modeling analysis and the use of world frequency to account for frame significance.

The authors shift actors' attention from improving technical efficiency to acquiring linguistic resources in the pursuit of digitalization. For example, framing the digital transformation in terms of creating a market orientation calls for not only real consumer power but also strategic discursive competence that enables the move to change. The findings also point out that practitioners can enlarge the scope of their agency rather than being trapped in the habituated routine of practices. Despite social embeddedness, organizations are more often widely connected and built enough to call for more of the cognitive frames to appeal to heterogeneous stakeholders.

The authors study contributes to the literature by developing a linguistic or socio-cognitive view of digital transformation strategy that is capable of expanding organizational attention toward change and innovation. The authors explore menus of strategic frames employed by actors in response to digital transformation. We also address the application of a machine-learning tool such as topic modeling to explore the socio-cognitive dimensions of digital transformation. Furthermore, the analysis leads us to identify the outcomes or effects – either positive or negative – that move beyond the particular Taiwanese case to explain the framing of digital transformation in general.

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.

This study employs a new time series representation of persistence in conditional mean and variance to test for the existence of the long memory property in the currency futures market. Empirical results indicate that there exists a fractional exponent in the differencing process for foreign currency futures prices. The series of returns for these currencies displays long-term positive dependence. A hedging strategy for long memory in volatility is also discussed in this article to help the investors hedge for the exchange rate risk by using currency futures.