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Coal is the basic energy and essential resource in China, which is crucial to the economic lifeline and energy security of the country. Coal mining has been ever exposed to potential safety risks owing to the complex geologic environment. Effective safety supervision is a vital guarantee for safe production in coal mines. This paper aims to explore the impacts of the internet+ coal mine safety supervision (CMSS) mode that is being emerged in China.

In this study, the key factors influencing CMSS are identified by social network analysis. They are used to develop a multiple linear regression model of law enforcement frequency for conventional CMSS mode, which is then modified by an analytical hierarchy process to predict the law enforcement frequency of internet+ CMSS mode.

The regression model demonstrated high accuracy and reliability in predicting law enforcement frequency. Comparative analysis revealed that the law enforcement frequency in the internet+ mode was approximately 40% lower than the conventional mode. This reduction suggests a potential improvement in cost-efficiency, and the difference is expected to become even more significant with an increase in law enforcement frequency.

To the best of the authors’ knowledge, this is one of the few available pieces of research which explore the cost-efficiency of CMSS by forecasting law enforcement frequency. The study results provide a theoretical basis for promoting the internet+ CMSS mode to realize the healthy and sustainable development of the coal mining industry.

This study aims to investigate the growth of Indonesian Islamic banks and explores organisational growth determinants from different perspectives, namely, organisational climate, intellectual capital (IC) and organisational service orientation. The study also attempts to develop a model to measure the growth of Islamic banks and uncovers the root causes of the stagnancy in Indonesian Islamic banking.

The study used survey questionnaires distributed to Islamic bank managers, who were considered representative experts in the field of Islamic banking. The data collected were analysed using the Statistical Package for Social Sciences (SPSS Version 21.0), and two analyses were performed with different strategies to build the regression model, namely, multiple linear regression and automatic linear regression.

The study found that IC significantly affected Islamic banks’ growth in Indonesia; however, organisational climate and service orientation did not predict such growth. Concerning service orientation as a mediating model, climate or IC had no indirect effect on growth.

This study’s results contribute to fill the gap by analysing the growth of Islamic banks. Hence, the study results will be especially practical and helpful for Islamic bank managers and policymakers to help develop mechanisms for Islamic banks in Indonesia.

By combining the aspects of organisational climate, IC and service orientation from earlier studies and categorising them by organisational growth, together with a comprehensive literature review, the study proposes a model specific to Islamic banks. It also offers new insight and discussion for determining organisational growth in Indonesian Islamic banks.

This study aims to provide a comprehensive analysis of various approaches to the residential property market evolution modelling and to examine the macroeconomic fundamentals that have shaped this market development in Ukraine in recent years.

The study uses a comprehensive data set encompassing relevant macroeconomic indicators and historical apartment prices. Multifactor linear regression (MLR) and ridge regression (RR) models are constructed to identify the impact of multiple predictors on apartment prices. Additionally, the ARIMAX model integrates time series analysis and external factors to enhance modelling and forecasting accuracy.

The investigation reveals that MLR and RR yield accurate predictions by considering a range of influential variables. The hybrid ARIMAX model further enhances predictive performance by fusing external indicators with time series analysis. These findings underscore the effectiveness of a multidimensional approach in capturing the complexity of housing price dynamics.

This research contributes to the real estate modelling and forecasting literature by providing an analysis of multiple linear regression, RR and ARIMAX models within the specific context of property price prediction in the turbulent Ukrainian real estate market. This comprehensive analysis not only offers insights into the performance of these methodologies but also explores their adaptability and robustness in a market characterized by evolving dynamics, including the significant influence of external geopolitical factors.

Properly planned road illumination systems are collectively a public wealth and the commissioning of such systems may require extensive planning, simulation and testing. The purpose of this simulative work is to offer a simple approach to facilitate luminance-based road lighting calculations that can be easier to comprehend and apply to practical designing problems when compared to complex multi-objective algorithms and other convoluted simulative techniques.

Road illumination systems were photometrically simulated with a created model in a validated software platform for specified system design configurations involving high-pressure sodium (HPS) and light-emitting diode (LED) luminaires. Multiple regression analyses were conducted with the simulatively obtained data set to propound a linear model of estimating average luminance, overall uniformity of luminance and energy efficiency of lighting installations, and the simulatively obtained data set was used to explore luminaire power–road surface average luminance characteristics for common geometric design configurations involving HPS and LED luminaires, and four categories of road surfaces.

The six linear equations of the propounded linear model were found to be well-fitted with their corresponding observation sets. Moreover, it was found that the luminaire power–road surface average luminance characteristics were well-fitted with linear trendlines and the increment in road surface average luminance level per watt increment of luminaire power was marginally higher for LEDs.

This neoteric approach of estimating road surface luminance parameters and energy efficiency of lighting installations, and the compendia of luminaire power–road surface average luminance characteristics offer new insights that can prove to be very useful for practical purposes.

Many studies have analysed the impact of various variables on the ability of companies to raise capital. While most of these studies are sector-agnostic, literature on the effects of macroeconomic variables on sectors that established over the last 20 years like property technology and financial technology, is scarce. This study aims to identify macroeconomic factors that influence the ability of both sectors and is extended by real estate variables.

The impact of macroeconomic and real estate related factors is analysed using multiple linear regression and quantile regression. The sample covers 338 observations for PropTech and 595 for FinTech across 18 European countries and 5 deal types between 2000–2001 with each observation representing the capital invested per year for each deal type and country.

Besides confirming a significant impact of macroeconomic variables on the amount of capital invested, this study finds that additionally the real estate transaction volume positively impacts PropTech while the real estate yield-bond-gap negatively impacts FinTech.

For PropTech and FinTech companies and their investors it is critical to understand the dynamic with mac-ro variables and also the real estate industry. The direct connection identified in this paper is critical for a holistic understanding of the effects of measurable real estate variables on capital investments into both sectors.

The analysis fills the gap in the literature between variables affecting investment into firms and effects of the real estate industry on the investment activity into PropTech and FinTech.

The current estimation practice in construction projects greatly needs upgrading, as there has been no improvement in the cost overrun issue over the past 70 years. The purpose of this research was to develop a new multiple regression analysis (MRA)-based model to forecast the final cost of road projects at the pre-design stage using data from 43 projects in New Zealand (NZ).

The research used the case study of 43 completed road projects in NZ. Document analysis was conducted to collect data, and statistical tests were used for model development and analysis.

Eight models were developed, and all models achieved the required F statistics and met the regression assumptions. The models’ mean absolute percentage error (MAPE) was between 21.25% and 22.77%. The model with the lowest MAPE comprised the road length and width, number of bridges, pavement area, cut and fill area, preliminary cost and cost indices change.

The model is based on road projects in NZ. However, it was designed to be able to adapt to other contexts. The findings suggest that the model can be used to improve traditional conceptual estimating methods. Past project data is often stored by the project team but rarely used for analysing and forecasting purposes. This research emphasises that past data can be effectively used to predict the project cost at the pre-design stage with limited information.

No research was conducted to adopt cost modelling techniques into the conceptual estimation practice in the NZ construction industry.

The purpose of this paper is to examine the impact of affiliate marketing strategies as a tool for increasing customers' engagement and vulnerability over financial services. This is attempted by examining the connection between affiliate marketing factors and customers' brand engagement and vulnerability metrics.

The authors developed a three-staged methodological context, based on the 7 most known centralized payment network (CPN) firms' website analytical data, which begins with linear regression analysis, followed by hybrid modeling (agent-based and dynamic models), so as to simulate brand engagement and vulnerability factors' variation in a 180-day period. The deployed context ends by applying the cognitive modeling method of producing heatmaps and facial analysis of CPN websites to the selected 47 vulnerable website customers, for gathering more insights into their brand engagement.

Throughout the simulation results of the study, it becomes clear that a higher number of backlinks and referral domains tend to increase CPN firms' brand-engaged and vulnerable customers.

From the simulation modeling process, the implication for backlinks and referral domains as factors that enhance website customers' brand engagement and vulnerability has been highlighted. A higher number of brand-engaged website customers could mean that vulnerable categories of customers would be impacted by CPNs' affiliate marketing. Improving those customers' knowledge of the financial services utility is of utmost importance.

The outcomes of the research indicate that online banking service providers can increase their customers' engagement with their brands by adopting affiliate marketing techniques. To avoid the increase in customers' vulnerability, marketers should aim to apply affiliate marketing strategies to domains relevant to the provided financial services.

The paper's outcomes provide a new approach to the literature, where the website customer's brand engagement comes out as a valuable metric for estimating online banking sector customers' vulnerability.

In the contemporary period, numerous businesses undergo significant adjustments, such as evaluating critical components of the corporate operations and relying on technology to keep operations running while conforming to an ever-changing set of norms and new tactics. The present study aims to (1) explore the relationship between Industry 4.0 (I4.0) tools and their impact on organizational performance and (2) find evidence supporting the moderating role of remote working and organizational agility (OA) in enhancing organizational performance.

The study employed the quantitative research method, and the data were collected from individuals working in different Asian IT firms using the previously established questionnaire. The data were examined using SPSS v22. Different statistical tests have been performed to find the relationship among constructs.

This study uncovers that I4.0 tools impact organizational performance, especially in the IT sector, with a particular emphasis on the moderating influence of remote work and OA. I4.0 tools encompass pivotal components such as artificial intelligence (AI), big data (BD), cloud computing (CC) and Internet of Things (IoT) indeed augment organizational performance. It can be referenced that I4.0 tools play the role of a driving force that equips organizations with the knowledge to augment their performance.

Companies should encourage remote work and use I4.0 technology to support and manage it. Enabling people to work from any location, lowering the requirement for physical infrastructure and enabling a more flexible and responsive organizational structure can increase OA. In conclusion, firms in Asia may increase the performance and agility using I4.0 technology. Organizations may innovate by putting money into these technologies, encouraging remote work and creating an innovative culture.

In this dynamic and technologically advanced environment, every industry is forced to look for latest tools, i.e. I4.0, tools to augment the performance. It has been concluded that I4.0 tools are “better practices” for boosting organizational performance; hence, the findings benefit firms working in the IT sector. The verdicts of this research can assist organizations in making decisions regarding the implementation of I4.0 tools.

To the best of the authors' knowledge, no specific study could be found in which the relationship among these constructs had been investigated earlier in the IT sector. This research work acts as value addition to the literature as it illustrates technological advancements may increase organizational performance, especially in Asia. This research work adds to the body of knowledge by amplifying the effect of latest technologies on organizational performance, via remote work and OA.

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.

The paper’s main goal is to examine the relationship between the video marketing of financial technologies (Fintechs) and their vulnerable website customers’ brand engagement in the ongoing coronavirus disease 2019 (COVID-19) crisis.

To extract the required outcomes, the authors gathered data from the five biggest Fintech websites and YouTube channels, performed multiple linear regression models and developed a hybrid (agent-based and dynamic) model to assess the performance connection between their video marketing analytics and vulnerable website customers’ brand engagement.

It has been found that video marketing analytics of Fintechs’ YouTube channels are a decisive factor in impacting their vulnerable website customers’ brand engagement and awareness.

By enhancing video marketing analytics of their YouTube channels, Fintechs can achieve greater levels of vulnerable website customers’ engagement and awareness. Higher levels of vulnerable customers’ brand engagement and awareness tend to decrease their vulnerability by enhancing their financial knowledge and confidence.

Fintechs should aim to increase the number of total videos on their YouTube channels and provide videos that promote their customers’ knowledge of their services to increase their brand engagement and awareness, thus reducing their vulnerability. Moreover, Fintechs should be aware not to over-post videos because they will be in an unfavorable position against their competitors.

This research offers valuable insights regarding the importance of video marketing strategies for Fintechs in promoting their vulnerable website customers’ brand awareness during crisis periods.