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The purpose of this study is to develop an intelligent tutoring system (ITS) for programming learning based on information tutoring feedback (ITF) to provide real-time guidance and feedback to self-directed learners during programming problem-solving and to improve learners’ computational thinking.

By analyzing the mechanism of action of ITF on the development of computational thinking, an ITF strategy and corresponding ITS acting on the whole process of programming problem-solving were developed to realize the evaluation of programming problem-solving ideas based on program logic. On the one hand, a lexical and syntactic analysis of the programming problem solutions input by the learners is performed and presented with a tree-like structure. On the other hand, by comparing multiple algorithms, it is implemented to compare the programming problem solutions entered by the learners with the answers and analyze the gaps to give them back to the learners to promote the improvement of their computational thinking.

This study clarifies the mechanism of the role of ITF-based ITS in the computational thinking development process. Results indicated that the ITS designed in this study is effective in promoting students’ computational thinking, especially for low-level learners. It also helped to improve students’ learning motivation, and reducing cognitive load, while there’s no significant difference among learners of different levels.

This study developed an ITS based on ITF to address the problem of learners’ difficulty in obtaining real-time guidance in the current programming problem-solving-based computational thinking development, providing a good aid for college students’ independent programming learning.

This study aims to examine the impact of board characteristics on debt choice.

The sample comprises of unique nonfinancial firms listed in the FTSE 350 over the period 2011–2018. This study uses Tobit and OLS regressions to check the impact of board characteristics on debt choice. The results are robust to the battery of robust checks.

This study finds that board size and board independence are positively associated with public debt. However, CEO duality and board meetings frequency are inversely associated with public debt. Overall, the findings are consistent with the “financial intermediation theory” that the firms with weak governance rely on bank financing, and firms with better corporate governance go for public debt.

This study offers significant insights for investors and policymakers.

This study offers new insights regarding the role of board characteristics in firms’ debt choice by showing the significant impact of board characteristics on debt choice. The findings indicate that the board’s efficient internal monitoring may substitute external monitoring by the bank.

Platform-based enterprises, as micro-entities in the platform economy, have the potential to effectively promote the low-carbon development of both supply and demand sides in the supply chain. Therefore, this paper aims to provide a multi-criteria decision-making method in a probabilistic hesitant fuzzy environment to assist platform-type companies in selecting cooperative suppliers for carbon reduction in green supply chains.

This paper combines the advantages of probabilistic hesitant fuzzy sets (PHFS) to address uncertainty issues and proposes an improved multi-criteria decision-making method called PHFS-DNMEREC-MABAC for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Within this decision-making method, we enhance the standardization process of both the DNMEREC and MABAC methods by directly standardizing probabilistic hesitant fuzzy elements. Additionally, a probability splitting algorithm is introduced to handle probabilistic hesitant fuzzy elements of varying lengths, mitigating information bias that traditional approaches tend to introduce when adding values based on risk preferences.

In this paper, we apply the proposed method to a case study involving the selection of carbon emission reduction collaboration suppliers for Tmall Mart and compare it with the latest existing decision-making methods. The results demonstrate the applicability of the proposed method and the effectiveness of the introduced probability splitting algorithm in avoiding information bias.

Firstly, this paper proposes a new multi-criteria decision making method for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Secondly, in this method, we provided a new standard method to process probability hesitant fuzzy decision making information. Finally, the probability splitting algorithm was introduced to avoid information bias in the process of dealing with inconsistent lengths of probabilistic hesitant fuzzy elements.

Based on signaling theory, this paper aims to explore the impact of supply chain transparency (SCT) on firms' bank loan (BL) and supply chain financing (SCF) in the context of voluntary disclosure of supplier and customer lists.

Based on panel data collected from Chinese-listed firms between 2012 and 2021, fixed-effect models and a series of robustness checks are used to test the predictions.

First, improving SCT by disclosing major suppliers and customers promotes BL but inhibits SCF. Specifically, customer transparency (CT) is more influential in SCF than supplier transparency (ST). Second, supplier concentration (SC) weakens SCT’s positive impact on BL while reducing its negative impact on SCF. Third, customer concentration (CC) strengthens the positive impact of SCT on BL but intensifies its negative impact on SCF. Last, these findings are basically more pronounced in highly competitive industries.

This study contributes to the SCT literature by investigating the under-explored practice of supply chain list disclosure and revealing its dual impact on firms' access to financing offerings (i.e. BL and SCF) based on signaling theory. Additionally, it expands the understanding of the boundary conditions affecting the relationship between SCT and firm financing, focusing on supply chain concentration. Moreover, it advances signaling theory by exploring how financing providers interpret the SCT signal and enriches the understanding of BL and SCF antecedents from a supply chain perspective.

In the stock market, herding behavior occurs when investors mimic the actions of others in their investment decisions. As a result, the market becomes inefficient and speculative bubbles form. This study aims to investigate the relationship between information, overconfidence, market sentiment, experience and national culture, and herding behavior among Malaysian investors. A total of 400 questionnaires are distributed to bank institutions' investors. The survey design based on cross-sectional data is analyzed using the Partial Least Squares Structural Equation Model. The results indicate that information, market sentiment, experience, and national culture are positively related to herding behavior, while overconfidence has no effect. With this, the government should strengthen regulations to prevent the dissemination of misleading information. Moreover, investors are encouraged to overcome narrow thinking by expanding their understanding of different cultures when making investment decisions.

This study aims to improve the use of digitalization in facilities management (FM) for shopping complex facilities in the post-COVID-19 era. The resumption of economic activities, especially in shopping complexes, poses challenges for FM with throngs of shoppers. To tackle these challenges, enhanced and innovative FM practices are necessary.

The study used a qualitative research approach, incorporating case studies, interviews, observations and documentation. It focused on super-regional shopping complexes in the Klang Valley, Malaysia, selecting two complexes for qualitative data collection. Supplementary data were gathered from various sources, including government policy publications, websites, books, journal papers and archival records.

The research provides valuable insights into FM innovations and the application of FM digitalization in shopping complexes after the COVID-19 pandemic. It also addresses challenges faced by FM teams during this period. Recommendations for implementing FM digitalization in super-regional shopping complexes post-COVID-19 include developing skilled personnel, defining appropriate work scopes, strategies and policies, using cost-effective software, and increasing occupant awareness. The involvement of outsourced service providers is advised, emphasizing their understanding of the organization’s business model and innovative approaches.

The findings offer new perspectives on the characteristics of FM digitalization in the commercial sector during business disruptions caused by the pandemic. The proposed strategies are grounded in real industry implementations, aiming to enhance the FM digitalization approach for improved business performance.

In this study, we drew on the theories of social exchange and social learning and hypothesized that the online social capital (SC) and offline SC of social networking affect the online self-disclosure (OSD) of individuals through social self-efficacy (SSE).

After retrieving 514 valid questionnaires, we used structural equation modeling to analyze the data.

The results indicated that the users’ SSE affected their OSD, and that both online and offline bridging and bonding SC increased their SSE. However, online bonding SC directly affected their OSD, whereas online bridging SC did not considerably affect their OSD. Given these findings, we presented both theoretical and practical implications to elucidate SSE and OSD behavior from the perspective of online and offline bridging and bonding SC.

In this study, we drew on theories of social exchange and social learning to examine the effects of online and offline bridging and bonding SC on users’ SSE and OSD on SNSs. Given the importance of SC and SSE in social relationships and the effects of OSD on SNSs, our goal was to provide SNS marketers with a thorough understanding of how to facilitate SSE and OSD from the perspective of online and offline bridging and bonding SC.

The purpose of this study is to examine the underlying mechanisms of exploitative innovation and exploratory innovation between social media usage and organizational agility, and elucidate the moderating role of learning goal orientation (LGO) in the above relationships, based on adaptive structuration theory (AST).

Based on a multiple-respondent matched survey of 334 Chinese e-commerce firms, authors employed structural equation modeling to examine the correlations among social media usage, exploitative innovation, exploratory innovation and organizational agility. Hierarchical regression analysis was used to examine the moderating role of LGO.

This study's empirical findings demonstrate that exploitative innovation and exploratory innovation mediate the relationship between social media usage and organizational agility in different ways. Further, LGO positively moderates the relationship between social media usage for customer acquisition and exploratory innovation, as well as the relationship between social media usage for customer relationship and exploitative innovation.

Firms are advised to leverage different types of social media usage to facilitate exploitative innovation and exploratory innovation and promote organizational agility. In addition, LGO within a firm should be established to enhance the effects of social media usage on exploitative innovation and exploratory innovation.

This study adds to the literature on social media usage by proposing and examining exploitative innovation and exploratory innovation as explanatory mechanisms to facilitate organizational agility. This study further identifies LGO as a boundary condition of social media usage's effect on exploitative innovation and exploratory innovation. By contextualizing social media as advanced information technology, this study contributes to the contextualization of AST in the social media context.

The purpose of this study is to examine the impact of technological diversification (TD) on enterprise innovation performance, meanwhile focusing on the moderating effects of various organizational slack (i.e. absorbed and unabsorbed slack) and ownership types (i.e. state-owned or privately-owned) in the context of Chinese listed firms.

This study formulates five hypotheses based on organization and agency theories. Our empirical analysis employs a fixed-effect regression estimator with a unique panel dataset of Chinese-listed manufacturing firms and 13,566 firm-year observations over 9 years from 2012 to 2020.

Our findings show that an inverted U-shaped relationship exists between TD and innovation performance, varying with different types of organizational slack and ownership. In state-owned enterprises (SOEs), unabsorbed slack negatively moderates the inverted U-shaped relationship; however, in privately-owned enterprises (POEs), this relationship is positively moderated. Although absorbed slack has negative moderating effects in both SOEs and POEs, its impact is only significant for POEs.

Our results imply that organizational slack has a contrasting impact on the relationship between TD and innovation performance when the type of ownership varies. Therefore, the managers that intend to achieve optimal innovation performance through TD should understand how organizational slack can be leveraged.

This study contributes to the existing literature by applying the relationship between TD and innovative performance to the transition economy, as well as examining the double-edged sword impact of state ownership on firm innovation performance.

Green innovation in supply chains is crucial for socioeconomic development and stability. Factors that influence collaborative green innovation in the supply chain are complex and diverse. Exploring the main influencing factors and their mechanisms is essential for promoting collaborative green innovation in supply chains. Therefore, this study analyzes how upstream and downstream enterprises in the supply chain collaborate to develop green technological innovations, thereby providing a theoretical basis for improving the overall efficiency of the supply chain and advancing green innovation technology.

Based on evolutionary game theory, this study divides operational scenarios into pure market and government-regulated operations, thereby constructing collaborative green innovation relationships in different scenarios. Through evolutionary analysis of various entities in different operational scenarios, combined with numerical simulation analysis, we compared the evolutionary stability of collaborative green innovation behavior in supply chains with and without government regulation.

Under pure market mechanisms, the higher the green innovation capability, the stronger the willingness of various entities to collaborate in green innovation. However, under government regulation, a decrease in green innovation capability increases the willingness to collaborate with various entities. Environmental tax rates and green subsidy levels promote collaborative innovation in the short term but inhibit collaborative innovation in the long term, indicating that policy orientation has a short-term impact. Additionally, the greater the penalty for collaborative innovation breaches, the stronger the intention to engage in collaborative green innovation in the supply chain.

We introduce the factors influencing green innovation capability and social benefits in the study of the innovation behavior of upstream and downstream enterprises, expanding the research field of collaborative innovation in the supply chain. By comparing the collaborative innovation behavior of various entities in the supply chain under a pure market scenario and government regulations, this study provides a new perspective for analyzing the impact of corresponding government policies on the green innovation capability of upstream and downstream enterprises, enriching theoretical research on green innovation in the supply chain to some extent.