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The current study aims to empirically analyze the influence of different information sources, together with the persuasiveness of recommender systems (RSs) on the consumer’s purchase intention (PI). It also expands the research on RSs from the point of view of consumer behavior and psychology, considering perceived usefulness and relevance. In addition, it analyzes how different types of personalized recommendations, along with non-personalized ones, influence PI.

The proposed model has been validated using partial least squares structural equation modeling (PLS-SEM), based on the data collected from 597 online shoppers.

This study proves that both information search and RSs influence PI, being complementary rather than mutually exclusive. Recommender systems’ findings indicate that the PI is primarily influenced by the perceived relevance of RSs, the information provided by manufacturers and reviews. Moreover, only the influence of the perceived usefulness of personalized recommendations strongly affects PI. Conversely, non-personalized recommendations do not affect PI.

Developers should focus on increasing the perceived usefulness and relevance of RSs. Thus, they could adopt the hybridization of RSs with the aggregation of both personal shopping behavior and social network contacts. It should integrate information signals from multiple sources to include sentiment extracted from reviews or links to the manufacturer’s page. Furthermore, the recommendation of discounted products must be only for products preferred by customers, because only these influence the PI.

This research provides a structural model that examines together, for the first time, the influence on the PI of the main RSs and sources of information.

This study has two objectives: to explore the factors that influence student self-efficacy regarding engagement and learning outcomes in a business simulation game course and to compare the difference between hierarchical and general teaching methods.

From September 2021 to May 2022, a questionnaire was administered to 126 students in a business simulation game course at the Zhongshan Institute, University of Electronic Science and Technology of China. Data were analyzed using nonparametric paired samples tests and linear regression.

The results showed that student self-efficacy, engagement and learning outcomes were significantly higher with the hierarchical teaching method than with the general teaching method. There were also differences in the factors that influenced self-efficacy regarding learning outcomes between the two teaching methods. With the general teaching method, student self-efficacy did not directly affect learning outcomes, but did so indirectly by mediating the effect of engagement. However, with the hierarchical teaching method, self-efficacy directly and significantly affected learning outcomes, in addition to indirectly affecting learning outcomes through student engagement.

Compared with the control group experimental research method, the quasi-experimental research method can eliminate the influence of sample heterogeneity itself, but the state of the same sample may change at different times, which is not necessarily caused by the hierarchical teaching design.

Based on the results of this study, teachers can apply hierarchical teaching according to student ability levels when integrating business simulation games. The results of this study can inspire teachers to protect student self-confidence and make teaching objectives and specific requirements clear in the beginning of the course, and also provide an important practical suggestion for students on how to improve their course performance.

The research results can be extended to other courses. Teachers can improve students' self-efficacy through hierarchical teaching design, thus improving students' learning performance and also provide reference value for students to improve their learning performance.

This study built a model based on self-system model of motivational development (SSMMD) theory, comparing factors that affect student self-efficacy regarding learning outcomes under different teaching methods. The model enriches the literature on SSMMD theory as applied to business simulation game courses and adds to our understanding of hierarchical teaching methods in this field. The results provide a valuable reference for teachers that can improve teaching methods and learning outcomes.

The main purpose of this paper is to explore whether the nature of innovation is accumulative or radical and to what extent past year accumulation of technology stock can predict future innovation. More importantly, the authors are concerned with whether a change of policy regime or a variance in the quality of technology will moderate the nature of innovation.

The authors examined a dataset of 3.6 million Chinese patents during 1985–2015 and constructed more than 5 million citation pairs across 8 sections and 128 classes to track knowledge spillover across technology fields. The authors used this citation dataset to calculate the technology innovation network. The authors constructed a measure of upstream invention, interacting the pre-existing technology innovation network with historical patent growth in each technology field, and estimated measure's impact on future innovation since 2005. The authors also constructed three sets of metrics – technology dependence, centrality and scientific value – to identify innovation quality and a policy dummy to consider the impact of policy on innovation.

Innovation growth is built upon past year accumulation and technology spillover. Innovation grows faster for technologies that are more central and grows more slowly for more valuable technologies. A pro-innovation and pro-intellectual property right (IPR) policy plays a positive and significant role in driving technical progress. The authors also found that for technologies that have faster access to new information or larger power to control knowledge flow, the upstream and downstream innovation linkage is stronger. However, this linkage is weaker for technologies that are more novel or general. On most occasions, the nature of innovation was less responsive to policy shock.

This paper contributes to the debate on the nature of innovation by determining whether upstream innovation has strong predictive power on future innovation. The authors develop the assumption used in the technology spillover literature by considering a time-variant, directional and asymmetric matrix to model technology diffusion. For the first time, the authors answer how the nature of innovation will vary depending on the technology network configurations and policy environment. In addition to contributing to the academic debate, the authors' study has important implications for economic growth and industrial or innovation management policies.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

With the development of science and technology, more creators are trying to use new crafts to represent the cultural trends of the social media era, which makes cultural heritage innovative and new genres emerge. This compels the academic community to examine craft from a new perspective. It is very helpful to understand the hidden representational structure of craft more deeply and improve the craft innovation system of cultural and creative products that we deconstruct the craft based on Complex Network and discover its intrinsic connections.

The research crawled and cleaned the craft information of the top 20% products on the Forbidden City’s cultural and creative products online and then performed Complex Network modeling, constructed three craft representation networks among function, material and technique, quantified and analyzed the inner connections and network structure of the craft elements, and then analyzed the cultural inheritance and innovation embedded in the craft representation networks.

The three dichotomous craft representation networks constructed by combining function, material and technique: (1) the network density is low and none of them has small-world characteristics, indicating that the innovative heritage of the craft elements in the Forbidden City’s cultural and creative products is at the stage of continuous exploration and development, and multiple coupling innovation is still insufficient; (2) all have scale-free characteristics and there is still a certain degree of community structure within each network, indicating that the coupling innovation of craft elements of the Forbidden City’s cultural and creative products is seriously uneven, with some specific “grammatical combinations” and an Island Effect in the network structure; (3) the craft elements with high network centrality emphasize the characteristics of decorative culture and design for the masses, as well as the pursuit of production efficiency and economic benefits, which represent the aesthetic purport of contemporary Chinese society and the ideological trend of production and life.

The Forbidden City’s cultural and creative products should continue to develop and enrich the multi-coupling innovation of craft elements, clarify and continue their own brand unique craft genes, and make full use of the network important nodes role.

This study aims to realize efficient, fast and safe robot search task, the belief criteria decision-making approach is proposed to solve the object search task with an uncertain location.

The study formulates the robot search task as a partially observable Markov decision process, uses the semantic information to evaluate the belief state and designs the belief criteria decision-making approach. A cost function considering a trade-off among belief state, path length and movement effort is modelled to select the next best location in path planning.

The semantic information is successfully modelled and propagated, which can represent the belief of finding object. The belief criteria decision-making (BCDM) approach is evaluated in both Gazebo simulation platform and physical experiments. Compared to greedy, uniform and random methods, the performance index of path length and execution time is superior by BCDM approach.

The prior knowledge of robot working environment, especially semantic information, can be used for path planning to achieve efficient task execution in path length and execution time. The modelling and updating of environment information can lead a promising research topic to realize a more intelligent decision-making method for object search task.

Metadiscourse is an important dialogue technique used in productive knowledge building to help a group evaluate and advance their knowledge progress. Previous studies have identified and defined various types of metadiscourse. However, there is scant knowledge about how different metadiscourse types emerge among different groups or what implicit correlations lie between progressive discourse and metadiscourse. Moreover, research on how different types of metadiscourse influence groups' knowledge advancement and artifacts is still inadequate. Therefore, this study aims to further examine the roles that different types of metadiscourse play in the collaborative knowledge building community on both a fine-grained (i.e. progressive discourse) and coarse-grained (i.e. group knowledge advancement and group artifacts) level.

Data for this study are drawn from the behaviour of undergraduate students participating in a 12-week course at a key university in China. On the fine-grained level, epistemic network analysis (ENA) is applied to illustrate how metadiscourse promotes the development of progressive discourse. On the coarse-grained level, two different chi-square tests are conducted to examine the roles of different types of metadiscourse in groups' knowledge advancement and artifacts.

The analysis allowed several conclusions to be drawn. First, the types of metadiscourse that students most often adopted were reflecting on ideas development (RD) and commenting on ideas (CI); they less frequently adopted setting group goals (SG) and making group plans (MP). Second, most types of metadiscourse correlated with developments in progressive discourse, particularly RD and CI. Third, the metadiscourse types RD, CI and coordinating group efforts (CE) played essential roles in knowledge advancement. Fourth, higher-quality artifacts could be created by using the metadiscourse type reviewing the state of knowledge building progress (RP).

A more profound comprehension of the role that metadiscourse plays in the collaborative knowledge building community not only contributes to the literature in the knowledge building field but also carries a significant meaning in regulating community, promoting learner agency and sustained knowledge, and consequently improving collaborative learning performance.

Distancing people socially as a precautionary measure against the mushrooming of COVID-19’s health and economic crisis has deleteriously affected the performance of the eatery industry to a great extent. Many food outlets failed to cope up with crisis and opted to move out, and many still vie to survive through pandemic. It becomes vital for researchers to understand what factors influence the performance and survival of eateries during the pandemic? The study makes an attempt to fabricate new factors which affect the performance and contribute significantly towards the survival of eateries in this new COVID-19-prone world.

The present study is a cross-sectional analysis with the sample of 150 eateries from the walled city of Punjab (India), i.e. Amritsar. Factor analysis is employed to scrutinise the factors which influence the performance of eateries during the pandemic, and to analyse factors which contribute significantly for the survival of eateries, logistic regression is performed.

The empirical analysis reveals that at prior psychological factor, followed by turnover factor, external factor, financial factor and marketing factor influence the performance of eateries during the pandemic. Only three factors, namely turnover factor, external factor and financial factor, turned up to be significant towards the survival rate of an eatery. The marketing factor which is a crucial contributor for survival of business in literature has turned out to be insignificant during the times of pandemic.

With the arrival of pandemic, the preference of people has changed, and the business environment in which entities operate has turned more complex. The present study is one of the pioneer attempts to evaluate whether the factors responsible for performance or survival of an eatery during normal times is relevant during the pandemic as well. The study contributes to the literature of eatery industry by adding a new variable namely psychological factor, i.e. changes witnessed in customers’ preference to visit an eatery.

The study aims to investigate the effects of intellectual capital and its constituents on the performance of listed companies operating in China's construction sector. The study also intends to examine the moderating role of digital transformation.

Hypotheses will be tested using Modified Value-Added Intellectual Capital (MVAIC). The sample will be comprised of 93 Shenzhen and Shanghai A-share listed companies within the construction industry from the period of 2015–2021. Multiple regression analysis was employed to investigate the influence of intellectual capital, its components and digital transformation on the performance of construction firms.

The study's results reveal that the performance of construction firms greatly depends on intellectual capital and its components. Furthermore, digital transformation plays a vital moderating role between intellectual capital and its components and construction firm performance.

This study addresses a critical inquiry on how construction managers can employ intellectual capital to enhance the performance of firms during digital transformation. Additionally, this research bridges this gap by guiding construction managers to concentrate on their external surroundings when examining firm performance.

By focusing on the predictors influencing construction firms' performance, this study contributes to the existing corpus of knowledge. This study employs resource orchestration theory (ROT) to determine how the different components of intellectual capital impact the performance of construction firms, with digital transformation acting as a moderating variable. This research will be valuable to researchers, construction industry professionals and policymakers.

This study aims to investigate the influence of parent company personnel embedding on the stock price crash risk (SPCR) of listed companies, along with the moderating effect of disparate locations between parent and subsidiary companies and other major shareholders.

This research empirically tests hypotheses based on a sample of listed subsidiaries in China during the period between 2006 and 2021.

Our results demonstrate that personnel embeddedness in the parent company significantly alleviates SPCR in subsidiaries. This effect is even more substantial when the parent and subsidiary companies are in different places. However, other major shareholders in the subsidiary company weaken it. Our additional analysis indicates that, relative to executive embeddedness, director embeddedness exerts a stronger effect on the SPCR of the subsidiary. Mechanism examination reveals that the information asymmetry and the level of internal control (IC) within the subsidiary are significant channels through which the personnel embeddedness from the parent company influences the SPCR of the subsidiary.

This study expands the literature on how personnel arrangements in corporate groups within emerging countries influence SPCR. We have extended the traditional concept of interlocking directorates to corporate groups, thereby broadening the understanding of the governance effects of interlocking directors and executives from a group perspective.