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The research enterprise within higher education is becoming more competitive as funding agencies require more collaborative research projects, higher-level of accountability and competition for limited resources. As a result, research analytics has emerged as a field, like many other areas within higher education to act as a data-informed unit to better understand how research institutions can effectively grow their research strategy. This is a new and emerging field within higher education.

As businesses and other industries are embracing recent advances in data technologies such as cloud computing and big data analytic tools to inform decision making, research administration in higher education is seeing a potential in incorporating advanced data analytics to improve day-to-day operations and strategic advancement in institutional research. This paper documents the development of a survey measuring research administrators’ perspectives on how higher education and other research institutions perceive the use of data and analytics within the research administration functions. The survey development process started with composing a literature review on recent developments in data analytics within the research administration in the higher education domain, from which major components of data analytics in research administration were conceptualized and identified. This was followed by an item matrix mapping the evidence from literature with corresponding, newly drafted survey items. After revising the initial survey based on suggestions from a panel of subject matter experts to review, a pilot study was conducted using the revised survey instrument and validated by employing the Rasch measurement analysis.

After revising the survey based on suggestions from the subject matter experts, a pilot study was conducted using the revised survey instrument. The resultant survey instrument consists of six dimensions and 36 survey items with an establishment of reasonable item fit, item separation and reliability. This survey protocol is useful for higher educational institutions to gauge research administrators’ perceptions of the culture of data analytics use in the workplace. Suggestions for future revisions and potential use of the survey were made.

Very limited scholarly work has been published on this topic. The use of data-informed and data-driven approaches with in research strategy within higher education is an emerging field of study and practice.

Environmental, social, and governance (ESG) issues have recently received much attention. This research investigates the daily performance of socially responsible investment (SRI). To do that, the authors construct portfolios consisting of the SRI, non-SRI, and matched non-SRI. The portfolios can be compared with the market benchmark based on α adjusted asset pricing models. Due to using high-frequency data, the authors use ARCH/GARCH to deal with time-varying volatility. Moreover, the authors also utilized Fama–MacBeth pooled regression to confront the SRI stocks and the non-SRI counterpart. In sum, the findings of this study confirm the superior performance of the value-weighted (VW) SRI portfolio against the market. On a head-to-head basis, the SRI yields a higher return than the non-SRI. The results are robust in the quarterly analysis. It is essential for investors that put their money in socially responsible (SR) portfolios to either promote sustainable development or chase a return on it.

The purpose of this paper is to empirically analyze the effects of managerial overconfidence on debt maturity structure decisions in terms of liquidity risk and asset match in Chinese listed companies.

Combining data from CSMAR with some default data collected by hand, this paper selects age, tenure, education, education background and whether the board chair and CEO positions are consolidated in Chinese listed companies as proxies of managerial overconfidence. Thus, the authors acquired needed and credible empirical data.

It was found that, the younger the CEO, the shorter the tenure, the lower the education, having economics or management education and being chairman concurrently, CEOs have stronger managerial overconfidence. Thus, corporate debt maturity structure is more weakly correlated with debt ratio and asset structure.

The findings in this study suggest that managerial irrationality, especially overconfidence, does have an effect on the financing decisions of firms.

This is the first paper to analyze the effects of managerial overconfidence on debt maturity structure decisions in terms of liquidity risk and asset match. The findings inspire firm risk management policies from managerial overconfidence.

The purpose of the study has been to fill the gap detected in the literature and to analyze whether the application of management of R&D in accordance with UNE 166002:2021 allows companies to obtain higher product innovation and better performance, specifically incremental and radical product innovations.

The population used in this study included Spanish manufacturing organizations that were active, had more than 50 employees according to the SABI. The information was collected through a structured questionnaire previously tested using a company specializing in the sector under the supervision of the authors. A total of 1,154 companies were randomly contacted in order to reach an acceptable number of 225 valid questionnaires. The data analysis has been carried out with structural equation methodology.

The results obtained with a sample of 225 companies show that the application of this standard for innovation management promotes the development of new products with incremental and radical changes, and improves business performance. It has also been found that incremental and radical product innovations mediate the relationship between this standard and performance.

Firstly, the survey is only addressed to the company's operations manager. Secondly, the sample used is cross-sectional, whereas innovation management implies a broad implementation process.

Managers must know that radical and incremental product innovation can improve the company's operational performance. And the most direct implication of this work is that, those companies that are committed to the development of innovations should seriously consider the application of the principles incorporated in Standard 166,002, as an instrument that improves the results of innovation in the organization. Since this SIMS promotes both types of innovations, it improves results directly and indirectly through these product innovations.

The existing literature indicates that no empirical study has focused on the benefits of this SIMSs for innovation and BP. This paper fills this gap detected in the literature and analyzes the results of the implementation of this standard on incremental and radical product innovations and business performance.

This study aims to investigate the effect of electrodeposition parameters (i.e. time and voltage) on the properties of hydroxyapatite (HA) coating fabricated on Ti6Al4V surface.

A full factorial design along with response surface methodology was utilized to evaluate the main effect of independent variables and their relative interactions on response variables. The effect of electrodeposition voltage and deposition time on HA coatings Ca/P molar ratio and the size of deposited HA crystals were examined by structural equation modeling (SEM). The formation of plate-like and needle-like HA crystals was observed for all experiments.

The results obtained showed that the higher electrodeposition voltage leads to lower Ca/P values for HA coatings. This is more significant at lower deposition times, where at a 20-minute deposition time, the voltage increased from 2 to 3 V and the Ca/P decreased from 2.27 to 1.52. Full factorial design results showed that electrodeposition voltage has a more significant effect on the size of the deposited HA crystal. With increasing the voltage from 2 to 3 V at a deposition time of 20 min, the HA crystal size varied from 99 to 36 µm.

The investigation delved into the impact of two critical parameters, deposition time and voltage, within the electrodeposition process on two paramount properties of HA coatings. Analyzing the alterations in coating characteristics relative to variations in these process parameters can serve as a foundational guide for subsequent research in the domain of calcium-phosphate deposition for implants.

Business management, entrepreneurship, strategic management and business environment.

Undergraduate and Masters level business and management programmes.

This case features a small labour intensive Chinese company, Bags of Luck (BoL), located in the south-eastern Fujian province. BoL makes ladies fashion handbags, unisex fashion backpacks and trendy lightweight cases for laptop and netbook computers for export to the US market. BoL have done very well over the years as a small private enterprise focusing on low-tech manufacturing and have managed to stay afloat through the most difficult period of the recent world recession. Currently troubled by fast changing market trends, rising material and employment costs, continuing appreciation of the Chinese currency, severe labour shortage, declining production volume and profitability, dated machinery, passive and reactive nature of business model, ineffective management structure and a complete lack of strategic vision, BoL is in deep crisis with its fate now hanging on the balance.

The case provides encourages students to: research into a range of current business management issues; analyse the impact of environmental changes on the survival and growth of a business organisation; develop their strategic thinking informed by real life and real-time research and assess the impact of exchange rate changes on the Chinese economy and the sustainability of Chinese model of economic growth.

Teaching note.

The paper aims to find the correlation between the microparameters and the macroparameters of the soil. The study aims to calibrate the macroscale and microscale parameters of rolling resistance contact models to successfully apply the discrete element method to do some research of the geotechnical problem.

The paper opted for an exploratory study using the PFC3D to simulate the triaxial tests that include more than 50 cases and the coupling analysis method, which considering several effect of various factors.

The paper provides a quantitative relationship between the macroparameters and microparameters of the rolling resistance linear model and a method for fast calibration of macroscopic parameters is proposed and verified by a triaxial test example.

This paper provides the quantitative relationship of micro and macroparameters in the rolling resistance linear model by studying a single factor and considering the coupling effect of various factors and a fast method for the calibration of microparameters based on the rolling resistance linear model is proposed.

With the advent of the web computing era, the transmission mode of the Internet of Everything has caused an explosion in data volume, which has brought severe challenges to traditional routing protocols. The limitations of the existing routing protocols under the condition of rapid data growth are elaborated, and the routing problem is remodeled as a Markov decision process. this paper aims to solve the problem of high blocking probability due to the increase in data volume by combining deep reinforcement learning. Finally, the correctness of the proposed algorithm in this paper is verified by simulation.

The limitations of the existing routing protocols under the condition of rapid data growth are elaborated and the routing problem is remodeled as a Markov decision process. Based on this, a deep reinforcement learning method is used to select the next-hop router for each data transmission task, thereby minimizing the length of the data transmission path while avoiding data congestion.

Simulation results show that the proposed method can significantly reduce the probability of data congestion and increase network throughput.

This paper proposes an intelligent routing algorithm for the network congestion caused by the explosive growth of data volume in the future of the big data era. With the help of deep reinforcement learning, it is possible to dynamically select the transmission jump router according to the current network state, thereby reducing the probability of congestion and improving network throughput.

This paper intends to explore the measure for aligning the goals of researchers toward achieving organizational R&D targets. The paper also explores the significance and ordering of R&D outputs and the factors that influence generation of R&D outputs, from the perspective of researchers working in the Indian public sector organizations.

Data were collected in five Indian R&D laboratories, and weighted average method Spearman correlation coefficient and rank regression were utilized for the analysis.

The findings indicated that various groups of researchers prefer to target different R&D outputs and not all the factors are considered as equally significant in influencing the generation of R&D outputs. Further, the R&D organization should include preferred real factors while policy making for achieving collaborative efforts toward fulfilling organizational objectives. The set of selected R&D outputs and influencing factors were also ordered according to the average rankings given by the researchers.

The findings can help R&D managers to identify the expectations of the researchers and include their preferences in R&D Planning. The study could be extended to a larger dataset of researchers working in other government as well as private R&D organizations.

Hardly any studies were found that explored the preferences of researchers with respect to R&D outputs and influencing factors with respect to the Indian public sector R&D laboratories.

The need to examine the integrity of infrastructure in the rail industry in order to improve its reliability and reduce the chances of breakdown due to defects has brought about development of an inspection and diagnostic robot.

In this study, an inspection robot was designed for detecting crack, corrosion, missing clips and wear on rail track facilities. The robot is designed to use infrared and ultrasonic sensors for obstacles avoidance and crack detection, two 3D-profilometer for wear detection as well as cameras with high resolution to capture real time images and colour sensors for corrosion detection. The robot is also designed with cameras placed in front of it with colour sensors at each side to assist in the detection of corrosion in the rail track. The image processing capability of the robot will permit the analysis of the type and depth of the crack and corrosion captured in the track. The computer aided design and modeling of the robot was carried out using the Solidworks software version 2018 while the simulation of the proposed system was carried out in the MATLAB 2020b environment.

The results obtained present three frameworks for wear, corrosion and missing clips as well as crack detection. In addition, the design data for the development of the integrated robotic system is also presented in the work. The confusion matrix resulting from the simulation of the proposed system indicates significant sensitivity and accuracy of the system to the presence and detection of fault respectively. Hence, the work provides a design framework for detecting and analysing the presence of defects on the rail track.

The development and the implementation of the designed robot will bring about a more proactive way to monitor rail track conditions and detect rail track defects so that effort can be geared towards its restoration before it becomes a major problem thus increasing the rail network capacity and availability.

The novelty of this work is based on the fact that the system is designed to work autonomously to avoid obstacles and check for cracks, missing clips, wear and corrosion in the rail tracks with a system of integrated and coordinated components.