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This paper aims to explore the effects of director network on open innovation. As an informal institutional arrangement, the director network is an important source for the enterprise to obtain external information, which provide resource basis for open innovation. Chief Executive Officer (CEO) as the top of management team could make short-sighted decisions for personal interests; this paper also investigates the moderating role of CEO short-sightedness between director network and open innovation.

This paper takes 4,102 Chinese listed companies from 2007 to 2020 as the research sample. By introducing network centrality and structural hole to measure director network and using data mining to extract key words related to CEO short-sightedness from annual reports, this paper constructs several multiple linear regression models to analyze the impact of director network on open innovation and the moderating role of CEO short-sightedness.

The analysis finds that director network can facilitate corporate open innovation. Enterprises can acquire more external resources in high centrality and structural hole of director network and promote ability for corporate open innovation. The relationship between director network and open innovation is negatively moderated by CEO short-sightedness. When the level of corporate governance and analyst attention is high, the negative effect of CEO short-sightedness on the innovation effect of directors’ networks is suppressed.

This is the first empirical paper to investigate the promotion effect of director network on open innovation as well as the negative moderating role of CEO short-sightedness. The findings bring new perspectives to the open innovation and enlightenments for practical activities from social relationship aspect.

This paper is a case study of student response systems used in large-class teaching. It considers the benefits, including the engagement of students and academic gains such as reduced administration. The constraints and impacts in classroom teaching are noted, drawing upon the experience of two teachers with their learning captured as a means of dissemination of practice to support other teachers who may be considering adopting and later adapting such practices (Gribble and Beckmann, 2023).

An autoethnographical account was undertaken using an action-learning approach as a sense-making exercise. These accounts enabled a depth of insight beyond the anecdotal evidence experienced by an individual teacher alone.

The findings show that while student response systems have constraints, these can be addressed by putting pedagogical concerns in front of any technology deployment, reaping benefits for students and teachers. Once engaged in using the system, students become more willing to enter further discussions. However, the limitations of both systems indicate that there may be a need for multiple systems to be available based on the pedagogical needs of the class.

The exploration of student response systems and outcomes of positive engagement by students in classroom settings provides insight to those wishing to explore such systems for use in large-class teaching settings.

This work extends discussions surrounding interactivity using student response systems. Additionally, practical insights from the users into their experiences with their students in using such systems provide alternatives for engagement in delivering large-class learning at scale.

This paper explores the potential that block teaching offers to enhance employability in the context of large-scale classes. It suggests that block teaching, with its condensed structure, necessitates curriculum innovation, fosters participatory learning and peer-to-peer networking, and has been shown to increase student focus and enhance engagement and attainment, especially amongst diverse learners. As these are the same challenges that large-scale teaching faces, it is proposed that intensive modes of delivery could be scaled up in a way that may help to mitigate such problems as cohorts in business schools continue to increase in size.

The paper is based on secondary research and provides an overview of literature that looks at block teaching, followed by that which explores the challenges of large-scale teaching contexts. It compares and contrasts the gaps in both to suggest a way that they could be combined.

The paper provides key insights into changes in the contemporary landscape of teaching within UK business schools, which have seen increasingly large cohorts and draws out the key strengths of intensive modes of delivery, which include helping students to time manage effectively, encouraging curriculum innovation and the creation of participatory learning opportunities as well as providing closer personal relationships between students and staff. Outlining some of the well-documented issues that can arise when teaching larger cohorts, the paper suggests that scaling up blocked delivery may offer a new way help to overcome them.

Because of the chosen research approach, the research results are subject to generalisation. Therefore, researchers are encouraged to test the proposed propositions in large-scale teaching scenarios.

This paper includes implications for the development of innovative modes of teaching in the context of large cohorts, an experience that is increasingly common amongst British business schools and beyond.

This paper brings together two bodies of literature for the first time – that of intensive modes of teaching and that focuses on large-scale teaching contexts – for the first time to show how the former may help to overcome some of the key issues arising in the latter.

The purpose of this study is to present a system dynamic (SD)-based remanufacturing economic analysis model of used automobile engine under two recycling modes. The authors will compare the remanufacturing cost, sales profit and sales revenue from time and space dimensions incurred in different recycling modes in the long run.

The remanufacturing economic analysis model is based on SD methodology. The authors can simulate the relations of impact factors on automobile engine recycling and remanufacturing and further analyze and compare the cost, sales profit and sales revenue incurred in different recycling modes in the long term.

Sinotruk Steyr engine remanufacturing in Shandong province is taken as the research case subject. The revenue, cost and profit under the two recycling modes from 2015 to 2035 are analyzed and compared. The results show that different recycling modes have significant varying influence on the economy of engine remanufacturing.

This economic analysis model can provide a method reference to decide the recycling mode for auto components and other product remanufacturing. Moreover, this model can guide and support the sustainable development of remanufacturing industry.

This study synthesises the self-determination theory (SDT), expectation-confirmation model (ECM), and protection motivation theory (PMT) to formulate an integrated theoretical framework that elucidates the process of shaping the intention to continue using facial recognition payment (FRP) under the conditional impact of perceived technology security.

Data from 667 Beijing Winter Olympics visitors with FRP experience were collected through an online survey and analysed using variance based-structural equation modelling (VB-SEM).

This study reveals that the intention to continue using FRP evolves through three key stages. Initially, in the expectation stage, the multidimensional concept of artificial autonomy (sensing, thought, and action), which is underpinned by self-determination, is pivotal, strongly influencing perceptions of service enhancement and fostering trust in FRP. Subsequently, the confirmation stage underscores the importance of perceived service enhancement and trust as vital drivers in maintaining FRP usage, while also contributing to subjective well-being. Crucially, perceived technology security emerges as a key moderating factor, enhancing positive perceptions and intentions towards FRP, thus influencing its sustained adoption.

This study stands out by revealing the nuanced interplay between artificial autonomy and user perceptions, particularly concerning service enhancement, technology security, and trust, as they influence well-being and the continued adoption of FRP. Robustly grounded in the integrated theoretical framework of SDT, ECM, and PMT, the study’s findings are critical for comprehending the core elements and specific drivers that promote sustained FRP use, especially as we consider its potential widespread implementation. Therefore, this study not only advances theoretical understanding but also offers practical guidance for optimising FRP deployment strategies in a rapidly evolving technological landscape.

House price fluctuations send vital signals to many parts of the economy, and long-term predictions of house prices are of great interest to governments and property developers. Although predictive models based on economic fundamentals are widely used, the common requirement for such studies is that underlying data are stationary. This paper aims to demonstrate the usefulness of alternative filtering methods for forecasting house prices.

We specifically focus on exponential smoothing with trend adjustment and multiplicative decomposition using median house prices for Sydney from Q3 1994 to Q1 2017. The model performance is evaluated using out-of-sample forecasting techniques and a robustness check against secondary data sources.

Multiplicative decomposition outperforms exponential smoothing at forecasting accuracy. The superior decomposition model suggests that seasonal and cyclical components provide important additional information for predicting house prices. The forecasts for 2017–2028 suggest that prices will slowly increase, going past 2016 levels by 2020 in the apartment market and by 2022/2023 in the detached housing market.

We demonstrate that filtering models are simple (univariate models that only require historical house prices), easy to implement (with no condition of stationarity) and widely used in financial trading, sports betting and other fields where producing accurate forecasts is more important than explaining the drivers of change. The paper puts forward a case for the inclusion of filtering models within the forecasting toolkit as a useful reference point for comparing forecasts from alternative models.

To the best of the authors’ knowledge, this paper undertakes the first systematic comparison of two filtering models for the Sydney housing market.

This study considers a contracting problem between a fairness concerned entrepreneur (EN) and a fair-neutral venture capitalist (VC) to explore the effects of asymmetry, agency conflicts and fairness concerns.

The authors construct the model by assuming the EN's risk aversion degree is private information, which is more realistic but ignored in most studies. Under the principal–agent framework, the authors solve the VC's optimal contracting models by identifying the ranges of feasible solution, where the optimal solutions of these models are explicit and nicely reconcile the “private equity” puzzle. Moreover, validity of the optimal solutions is verified by numerical simulations.

In accordance with empirical evidence, information asymmetry lowers the optimal equity share that the VC provides to EN but raises EN's profit due to lower effort disutility and information rent. Moreover, the authors find that the fairness concerns is beneficial for the EN, where it not only increases the EN's optimal equity share, but also enhances the certainty equivalence of the EN's utility regarding its profit. Relative to the benchmark model where the EN's risk aversion degree is common knowledge, the EN's efforts recommended by the optimal contract is less sensitive to the EN's fairness concerns degree when the EN does not actually announce its risk aversion degree.

First, the authors incorporate asymmetry to study a two-period contracting problem and explore how it affects the equity shares allocated to the contractual parties. Second, the authors incorporate fairness concerns and analyze its effect regarding the decision-makings and profits.

With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.

Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.

The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.

This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

Visual feedback control is a promising solution for robots work in unstructured environments, and this is accomplished by estimation of the time derivative relationship between the image features and the robot moving. While some of the drawbacks associated with most visual servoing (VS) approaches include the vision–motor mapping computation and the robots’ dynamic performance, the problem of designing optimal and more effective VS systems still remains challenging. Thus, the purpose of this paper is to propose and evaluate the VS method for robots in an unstructured environment.

This paper presents a new model-free VS control of a robotic manipulator, for which an adaptive estimator aid by network learning is proposed using online estimation of the vision–motor mapping relationship in an environment without the knowledge of statistical noise. Based on the adaptive estimator, a model-free VS schema was constructed by introducing an active disturbance rejection control (ADRC). In our schema, the VS system was designed independently of the robot kinematic model.

The various simulations and experiments were conducted to verify the proposed approach by using an eye-in-hand robot manipulator without calibration and vision depth information, which can improve the autonomous maneuverability of the robot and also allow the robot to adapt its motion according to the image feature changes in real time. In the current method, the image feature trajectory was stable in the camera field range, and the robot’s end motion trajectory did not exhibit shock retreat. The results showed that the steady-state errors of image features was within 19.74 pixels, the robot positioning was stable within 1.53 mm and 0.0373 rad and the convergence rate of the control system was less than 7.21 s in real grasping tasks.

Compared with traditional Kalman filtering for image-based VS and position-based VS methods, this paper adopts the model-free VS method based on the adaptive mapping estimator combination with the ADRC controller, which is effective for improving the dynamic performance of robot systems. The proposed model-free VS schema is suitable for robots’ grasping manipulation in unstructured environments.

The authors examined how the identification mechanism of the innovation performance of knowledge employees is affected by empowering leadership by influencing the organizational identification and the moderating effect of leaders on the role expectation of knowledge employees as an essential innovation subject.

The authors employed a mixed-method research approach. The authors collected data from 378 knowledge employees and managers in 20 companies in China's Yangtze River Delta cities. The authors analyzed data using multiple regression analysis forecasting methods.

The authors found that there was an inverted U-shaped relationship between empowering leadership and the innovation performance of knowledge employees; organizational identity played a partial mediating role between empowering leadership and the innovation performance of knowledge employees; role expectation of leaders on the innovation behavior of employees regulated the relationship between the organizational identity and innovation performance of knowledge employees.

This study extends the literature on empowering leadership and innovation performance. This study empirically examines the mediating effect of organizational identity between empowering leadership and innovation performance. In addition, this study empirically examines how empowered leaders' expected innovation level moderates the association between organizational identity and innovation performance.