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This study aims to empirically investigate how knowledge paradox affects collaboration performance. Knowledge paradox, which arises from the simultaneous need for knowledge sharing and protection, is common in interorganizational collaboration. Using the ambidexterity perspective, this paper aims to reexamine the effect of the knowledge paradox on collaborative performance to explore the moderating roles of structural and contextual ambidexterity.

This study used a sample of 153 firms involved in vertical and horizontal collaboration, collected via questionnaires. Hypotheses were tested using hierarchical regression analysis.

This study demonstrates that the stronger the knowledge paradox is, the higher the potential for value creation. Thus, knowledge paradox has a positive impact on collaborative performance. The functions of structural ambidexterity and contextual ambidexterity strengthen this positive relationship.

This paper not only expands the theoretical application of the knowledge paradox and ambidexterity theory in the context of interorganizational relationships but also provides significant managerial implications. By comprehending the dynamics of the knowledge paradox and the role of ambidexterity, managers can make well-informed decisions to enhance their collaborative performance.

Eco-innovation products, which means achieving more efficient and responsible use of resources and reducing the detrimental impact on the environment, can win a competitive advantage for the enterprises. But it is not easy to implement due to the high cost of eco-innovative technologies development, the uncertainty of market needs and return risk of investment. Many enterprises seek collaborations from their upstream suppliers to jointly carry out eco-innovation, such as Apple, IBM and Nike. A unique feature of collaboration is that efforts by one party enhance the marginal value of the other party's efforts. However, the collaboration will make the partner know the eco-innovation technology and prompt the partner to encroach the market to sell competitive products by herself. Motivated by this observation, this paper considers the optimal collaboration strategy on eco-innovation between upstream and downstream supply chain member and the optimal encroachment strategy of upstream supplier in a supply chain.

This paper models a supply chain wherein a supplier provides products or materials for her manufacturer and cooperates with her manufacturer in eco-innovation. Also, the supplier could encroach on the market to sell similar products by herself. Then this paper uses game theory and mathematical modeling to do relative analysis.

The analysis reveals several interesting insights. First, eco-innovation collaboration makes supplier encroachment no longer only rely on the encroachment cost. The delayed realized eco-innovation efficiency information also plays a vital role. Second, different from previous research, the authors find the manufacturer's preference for supplier encroachment depends on the uncertainty of eco-innovation efficiency and potential market demand. Third, both partial and full encroachment strategies of the supplier can effectively improve the eco-innovation level.

The paper is the first to take the interplay between collaboration and encroachment into account in a supply chain. The results caution enterprises and policymakers to take vertical collaboration and delayed realized information into account in the competitive supply chain before making any operational decisions. Furthermore, the authors propose that governmental intervention aimed at stimulating supplier encroachment in appropriate circumstances can contribute to the improved environmental performance of products.

This study seeks to establish a new framework for categorizing incivility, differentiating between explicit and implicit forms, and to investigate their respective abilities to proliferate and mobilize conversations, along with behavioral outcomes in various social contexts.

Employing computational techniques, this research analyzed 10,145 protest-related threads from the HK Golden Forum, a prominent online discussion board in Hong Kong.

Our analysis revealed divergent effects of explicit and implicit incivility on their diffusion, influences on deliberative discussions, and user participation. Explicit incivility was found to impede deliberative conversations, while implicit incivility tended to provoke more responses. Explicit uncivil expressions encouraged the propagation of incivility but reduced the likelihood of individual involvement. In contrast, implicit incivility had a stronger dampening effect on further uncivil comments and achieved greater thread popularity. The results showed strong associations between uncivil expressions and the contextual norms surrounding social movements.

Theoretically, this research introduced a classification of incivility and underscored the importance of differentiating between implicit and explicit incivility by examining their effects on deliberation and engagement. Although previous studies have extensively covered explicit incivility, this study goes further by analyzing implicit incivility and comparing both forms of uncivil discourse in a less-studied context. Methodologically, the study developed a Cantonese dictionary to differentiate between two types of incivility, providing a practical reference for more nuanced analyses. By revealing how varying movement norms moderate the interplay between deliberative and uncivil expressions, the study drew attention to the highly situational nature of incivility.

This study analyses the way various components of intellectual capital (IC), namely, human capital (HC), structural capital (SC), relational capital (RC) and innovation capital (INNC), act as mediators in the relationship between managerial ability (MA) and a firm’s ability to achieve growth.

This study employs data envelopment analysis to quantify the MA of 825 Taiwanese listed electronics companies from 2017 to 2022. The proxies of firm growth are return on asset growth, operating income growth and total asset growth. This study then utilises a three-step mediation analysis methodology to examine the relationships between MA, IC and firm growth.

Findings indicate that HC, SC, RC and INNC mediate the link between MA and firm growth. This suggests that competent managers can capitalise on the potential benefits of these investments to achieve firm growth.

Competent managers can utilise different IC investments to grow the financial performance and strength of their businesses. Managers should continually scan, secure opportunities and adjust their investments in knowledge assets in accordance with the dynamic capabilities view. That is, managers, in general, and operations managers, in particular, can implement guidelines that prioritise IC investments in the future to expedite firms’ development.

This study extends the existing frameworks that study investment variables as mediators between MA and firm outcomes. Most particularly, this study adopts four components of IC for measurement. Moreover, firm performance is measured using dynamic growth indicators rather than static measures.

This study aims to prepare an imprinted composite membrane with grafted temperature-sensitive blocks for the efficient adsorption and separation of rhenium(Re) from aqueous solutions.

PVDF resin membrane was used as the substrate, dopamine and chitosan (CS) were used to modify the membrane surface and temperature-sensitive block PDEA was grafted on the membrane surface. Then acrylic acid (AA) and N-methylol acrylamide (N-MAM) were used as the functional monomers, ethyleneglycol dimethacrylate (EGDMA) as the cross-linker and ascorbic acid-hydrogen peroxide (Vc-H₂O₂) as the initiator to obtain the temperature-sensitive ReO4⁻ imprinted composite membranes.

The effect of the preparation process on the performance of CS–Re–TIICM was investigated in detail, and the optimal preparation conditions were as follows: the molar ratios of AA–NH₄ReO₄, N-MAM and EGDMA were 0.13, 0.60 and 1.00, respectively. The optimal temperature and time of the reaction were 40 °C and 24 h. The maximum adsorption capacity of CS–Re–TIICM prepared under optimal conditions was 0.1071 mmol/g, and the separation was 3.90 when MnO4⁻ was used as the interfering ion. The quasi first-order kinetics model and Langmuir model were more suitable to describe the adsorption process.

With the increasing demand for Re, the recovery of Re from Re-containing secondary resources becomes important. This study demonstrated a new material that could be separated and recovered Re in a complex environment, which could effectively alleviate the conflict between the supply and demand of Re.

This contribution provided a new material for the selective separation and purification of ReO4⁻, and the adsorption capacity and separation of CS–Re–TIICM were increased with 1.673 times and 1.219 time compared with other Re adsorbents, respectively. In addition, when it was used for the purification of NH₄ReO₄ crude, the purity was increased from 91.950% to 99.999%.

This study aims to enhance the classification and processing of online appeals by employing a deep-learning-based method. This method is designed to meet the requirements for precise information categorization and decision support across various management departments.

This study leverages the ALBERT–TextCNN algorithm to determine the appropriate department for managing online appeals. ALBERT is selected for its advanced dynamic word representation capabilities, rooted in a multi-layer bidirectional transformer architecture and enriched text vector representation. TextCNN is integrated to facilitate the development of multi-label classification models.

Comparative experiments demonstrate the effectiveness of the proposed approach and its significant superiority over traditional classification methods in terms of accuracy.

The original contribution of this study lies in its utilization of the ALBERT–TextCNN algorithm for the classification of online appeals, resulting in a substantial improvement in accuracy. This research offers valuable insights for management departments, enabling enhanced understanding of public appeals and fostering more scientifically grounded and effective decision-making processes.

The recent proliferation of social media platforms has witnessed a growth in social commerce by using social media to facilitate interactivity between customers and vendors. While emergent studies on social commerce are growing, their focus tends to be on millennials and cross-age groups. Given the growth of digital natives in shaping the online shopping experience of the future, we deemed an application to Generation Z necessary and overdue.

We draw on the existing literature and develop a framework to understand social commerce dynamics for digital natives. We employ PLS and CB-SEM to test our proposed model.

Our findings demonstrate the importance of social commerce information sharing activities in facilitating social support, a sense of warmth and belongingness, and online trust for Generation Z platform users. We also investigate the roles of online trust and perceived risk on intention to purchase and find support for both relationships. Finally, we discuss the findings in terms of theoretical and managerial contributions and conclude the study with limitations and future research directions.

This research is unique by using social commerce theory to explore Gen Z platform users. The finding will contribute to information system literature by expanding the social commerce research stream.

The rapid development of generative artificial intelligence has witnessed its widespread integration across various industries, contributing to enhanced productivity. However, a comprehensive exploration of the underlying factors influencing the behavior of graphic designers in employing such tools remains incomplete. This research aims to amalgamate the IDT theory with the UTAUT2 model to construct a structural model, delving into the factors affecting graphic designers’ behavior in using GenAI tools.

A survey was conducted with 394 respondents, and the results were analyzed using PLS-SEM.

The findings reveal that most factors proposed in both the UTAUT2 and IDT theories exert positive influences. Notably, the study highlights that AI anxiety significantly influences designers’ usage behavior.

This research provides a theoretical foundation and practical guidance for both graphic designers and AI developers.

Although extensive research has been conducted on precast production, irregular component loading constraints have received little attention, resulting in limitations for transportation cost optimization. Traditional irregular component loading methods are based on past performance, which frequently wastes vehicle space. Additionally, real-time road conditions, precast component assembly times, and delivery vehicle waiting times due to equipment constraints at the construction site affect transportation time and overall transportation costs. Therefore, this paper aims to provide an optimization model for Just-In-Time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time.

In order to propose a JIT (just-in-time) delivery optimization model, the effects of the sizes of irregular precast components, the assembly time, and the loading methods are considered in the 3D loading constraint model. In addition, for JIT delivery, incorporating real-time road conditions in the transportation process is essential to mitigate delays in the delivery of precast components. The 3D precast component loading problem is solved by using a hybrid genetic algorithm which mixes the genetic algorithm and the simulated annealing algorithm.

A real case study was used to validate the JIT delivery optimization model. The results indicated this study contributes to the optimization of strategies for loading irregular precast components and the reduction of transportation costs by 5.38%.

This study establishes a JIT delivery optimization model with the aim of reducing transportation costs by considering 3D loading constraints, real-time road conditions and assembly time. The irregular precast component is simplified into 3D bounding box and loaded with three-space division heuristic packing algorithm. In addition, the hybrid algorithm mixing the genetic algorithm and the simulated annealing algorithm is to solve the 3D container loading problem, which provides both global search capability and the ability to perform local searching. The JIT delivery optimization model can provide decision-makers with a more comprehensive and economical strategy for loading and transporting irregular precast components.

This study aims to examine the efforts of two universities in Taiwan, particularly their engagement in coastal rural communities, and provides insights into the substantial contributions of University Social Responsibility (USR) projects toward achieving the sustainable development goals (SDGs) in rural settings. The objective of this research is to analyze the outcomes of these case projects on the SDGs and, consequently, to propose a theoretical model aimed at enhancing student participation in achieving SDGs through USR programs.

This study adapted the methodology comprising SDGs contribution analysis and participant observation. The SDGs contribution analysis unfolds in three steps. First, the authors conducted evaluation of the 2030 Agenda for Sustainable Development. This step involved a thorough analysis of all 17 SDGs and their respective targets to establish a foundational understanding. Second, analyzed the outcomes of the case projects to examine the specific contributions of case projects toward achieving the SDGs. Third, the information from the preceding steps is analyzed to determine the extent of the case projects’ contributions to the SDGs.

The study proposes a theoretical model to enhance student engagement in achieving SDGs, emphasizing student empowerment, community partnership and robust evaluation methods aligned with SDGs and social impacts. This model could offer guidance for higher education institutions (HEIs) globally on utilizing USR programs to contribute to the SDGs, while simultaneously enriching student learning experiences through practical engagement and empowerment.

This model can be enhanced and validated by applying more rigorous scientific methods. For instance, conducting surveys on students and community participants of events and activities, utilizing a statistically rigorous approach such as pre-post testing, can analyze the effectiveness of these programs on various SDG-related variables (e.g. awareness of SDGs). Additionally, exploring the relationships between the tested variables can be a potential research direction. For instance, examining whether community engagement can positively increase the social impacts of USR projects, or whether student empowerment can enhance community capacity building, are important issues worthy of discovery.

This model emphasizes the pivotal role of student empowerment, advocating for an educational approach that not only enhances students’ proficiency in community development but also potentially shapes their career trajectories, as evidenced by the case projects examined in this study. In essence, this model offers HEIs a structured pathway to enrich student engagement in realizing SDGs through USR initiatives. It posits student empowerment as the foundational element, fostering a learning environment where students gain valuable skills and insights into community development, potentially guiding their future professional endeavors. This research provides practical direction for those HEIs implementing USR projects, which will increase the positive impacts brought by HEIs, especially for the students and local community.

To the best of the authors’ knowledge, no previous studies have proposed a theoretical model specifically designed to engage students in achieving SDGs through USR programs in a rural context. The significance of this study lies in its potential to serve as a guide for higher education institutions globally, enabling them to effectively leverage USR programs to contribute toward SDGs. This makes the study an invaluable resource for researchers, policymakers and educators who are committed to fostering sustainability.