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This study examines the use of a dynamic value-based approach to analyze the business model structuration of smart service providers in Brazil, mapping their value creation, configuration and appropriation strategies, and determining how well-defined their current business models are.

This is a qualitative study based on semi-structured interviews with entrepreneurs (or CEOs and directors of technology) of seven business ventures in three different phases of business model structuration: (1) academic: companies or innovation and research centers linked to universities; (2) startups: technology-based companies originating from the technological needs of clients, be they new branches of the traditional business of incumbents or new entrants and (3) autonomous service providers whose offerings are related to master’s or doctoral projects.

We propose a typology of business model structuration with four stages. At first (individual or initial business model), albeit with high skilling of owners, only manual or adaptation services are offered. In the second stage (platform business model), although services offered are oriented toward the entire process automatization of the client (Factory integrated), technologies are restricted to the client company (or even one department) and these clients' needs are mainly data processing and connectivity. In the third stage (scaling digital business model), although the services offered are oriented toward greater digitalization through an entire array of field devices connected to the internet (IoT) and organized in a more formalized structure, the business model is still being constructed, companies in this stage are mainly startups. In the fourth stage (innovation ecosystem business model), the entire manufacturing process is digitized, with integration and network connectivity, both between service providers and the extended supply chain of their clients, and new technologies are customized and developed through the interaction of a whole innovation ecosystem.

Mapping value-based strategies aids in understanding business model structuration in Industry 4.0. Future research should focus on parameterizing the dimensions founded of each value strategy.

This study advances the comprehension of the business model in |Industry 4.0 by providing a value-based strategy perspective of business model structuration. Practically, by focusing on smart service providers, it contributes to a greater understanding of smart service providers in Brazil and their strategic challenges.

Untimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.

In this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.

The Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.

This study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.

In this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.

An efficient e-waste management system is developed, aided by deep learning techniques. Here, a smart bin system using Internet of things (IoT) sensors is generated. The sensors detect the level of waste in the dustbin. The data collected by the IoT sensor is stored in the blockchain. Here, an adaptive deep Markov random field (ADMRF) method is implemented to determine the weight of the wastes. The performance of the ADMRF is boosted by optimizing its parameters with the help of the improved corona virus herd immunity optimization algorithm (ICVHIOA). Here, the main objective of the developed ADMRF-based waste weight prediction is to minimize the root mean square error (RMSE) and mean absolute error (MAE) rate at the time of testing. If the weight of the bins is more than 80%, then an alert message will be sent to the waste collector directly. Optimal route selection is carried out using the developed ICVHIOA for efficient collection of wastes from the smart bin. Here, the main objectives of the optimal route selection are to reduce the distance and time to minimize the operational cost and the environmental impacts. The collected waste is then considered for recycling. The performance of the implemented IoT and blockchain-based smart dustbin is evaluated by comparing it with other existing smart dustbins for e-waste management.

The developed e-waste management system is used to collect the waste and to avoid certain diseases caused by the dumped waste. Disposal and recycling of the e-waste is necessary to decrease pollution and to manufacture new products from the waste.

The RMSE of the implemented framework was 33.65% better than convolutional neural network (CNN), 27.12% increased than recurrent neural network (RNN), 22.27% advanced than Resnet and 9.99% superior to long short-term memory (LSTM).

The proposed E-waste management system has given an enhanced performance rate in weight prediction and also in optimal route selection when compared with other conventional methods.

Internet of Things (IoT) is a network, which provides the connection with various physical objects such as smart machines, smart home appliance and so on. The physical objects are allocated with a unique internet address, namely, Internet Protocol, which is used to perform the data broadcasting with the external objects using the internet. The sudden increment in the number of attacks generated by intruders, causes security-related problems in IoT devices while performing the communication. The main purpose of this paper is to develop an effective attack detection to enhance the robustness against the attackers in IoT.

In this research, the lasso regression algorithm is proposed along with ensemble classifier for identifying the IoT attacks. The lasso algorithm is used for the process of feature selection that modeled fewer parameters for the sparse models. The type of regression is analyzed for showing higher levels when certain parts of model selection is needed for parameter elimination. The lasso regression obtains the subset for predictors to lower the prediction error with respect to the quantitative response variable. The lasso does not impose a constraint for modeling the parameters caused the coefficients with some variables shrink as zero. The selected features are classified by using an ensemble classifier, that is important for linear and nonlinear types of data in the dataset, and the models are combined for handling these data types.

The lasso regression with ensemble classifier–based attack classification comprises distributed denial-of-service and Mirai botnet attacks which achieved an improved accuracy of 99.981% than the conventional deep neural network (DNN) methods.

Here, an efficient lasso regression algorithm is developed for extracting the features to perform the network anomaly detection using ensemble classifier.

This paper aims to investigate whether negative and positive monetary policy (MP) shocks have asymmetric impacts on corporate firms’ investment decisions in Pakistan using firm-level panel data set. Moreover, the authors emphasized on symmetric effects of MP; the authors examine whether high-leverage and low-leverage firms respond differently to negative and positive unanticipated shocks in MP instruments.

In contrast to the conventional framework of VAR, it uses an alternative methodology of Taylor rule to estimate unanticipated MP shocks. The two-step system-generalized method of movement (GMM) estimation method is applied to examine the effect of MP shocks on firm investment through leverage-based asymmetry.

The two-step system-GMM estimation results indicate that unanticipated negative changes (unfavorable shocks) in MP instruments have negative, significant effects on investment. In contrast, unanticipated positive changes (favorable shocks) have statistically insignificant impacts on firm investment. The results also reveal that firm leverage has a significant role in establishing the effect of unanticipated negative changes in MP instruments on investments. Finally, the results indicate that high-leverage firms respond more to negative changes than low-leverage firms. Yet, the results show that only low-leverage firms positively respond to unanticipated positive shocks in MP.

The findings of the paper suggest that MP authorities should pay due attention to the asymmetric effects of MP shocks on firm investment while designing MP. Because firm leverage has a significant influence on the effects of MP shocks, firm managers should take into account such role of leverage while deciding capital structure of their firms.

First, unlike “Keynesian asymmetry” and most of published empirical research work, the authors use both unanticipated negative and positive MP shocks simultaneously. Departing from the conventional empirical literature, the authors differentiate between unanticipated positive and negative shocks in MP using the backward-looking Taylor rule. Second, the authors contribute to the existing literature by investigating the differential effects of positive and negative unanticipated MP shocks on firms’ investment decisions. Unlike the published studies that have emphasized on the symmetric effects of MP, the authors examine whether high-leverage and low-leverage firms respond differently to negative and positive unanticipated shocks in MP instruments.

Every business company deals with the dilemma of how much to invest in long-term (LT) versus short-term (ST) problem (LTvST problem). LT operations increase the reputation of the company, and revenue is rewarded in the future. In contrast, ST operations result in immediate rewards. Thus, every organization faces the dilemma of how much to invest in LT versus ST activities. The former deals with the “what” or effectiveness, and the latter deals with the “how” or efficiency. The role of managers is to solve this dilemma; however, they often fail to do so, mainly because of a lack of knowledge. This study aims to propose a dynamic optimal control model that formulates and solves the LTvST problem.

This study proposes a dynamic optimal control model that formulates and solves the dilemma whether to invest in short- or LT operations.

This model is illustrated as an example of an academic institute that wants to maximize its reputation. Investing in effectiveness in the academy translates into investing in research, whereas investing in efficiency translates into investing in teaching. Universities and colleges with a good reputation attract stronger candidates and benefit from higher tuition fees. Steady-state conditions and insightful observations were obtained by studying the optimal solution and performing a sensitivity analysis.

To the best of the authors’ knowledge, this paper is the first one to explore the optimal strategy when trying to maximize the short and LT activities of a company and solve the LTvST problem. Furthermore, it is applied on universities where teaching is the ST activity and research the LT activity. The insights gleaned from the application are relevant to many different fields. The authors believe that the paper makes a significant contribution to academic literature and to business managers.

This paper aims to analyze the downside risk for the stock indices of BRICS countries. The study also aimed to study the interrelationship, directional influence and interdependence among the stock exchanges of BRICS economies to provide insights for policymakers, fund managers, investors and other stakeholders.

The authors used Value at Risk (VaR) as an indicator of downside risk and time series econometrics for measuring the long run relationship, directional influence and interdependence.

The calculated VaR estimates, long-run linkages and strong interdependence among these indices especially with the returns of Brazil exerting a notable impact on the returns of other BRICS nations. These results emphasize the significance of taking into account cross-country spillover effects and domestic market dynamics in the context of portfolio management and risk assessment strategies. Further, from the extended results of variance decomposition analysis, the authors find that Brazil’s, China’s and South African stock market returns have a significantly lagged impact on their own stock market, while Russia’s and India stock market returns do not have a significantly lagged impact on their own stock markets.

To the best of the authors’ knowledge, this is the first study comprehensively analyzing the BRICS indices downside risk through the historical simulation method of VaR estimation, which is an unexplored area of risk management.

The restoration and strengthening of QT600 is an industry bottleneck challenge. The Co-12 cladding layer has great wear and corrosion resistance. The purpose of this paper is to quantitatively reveal the transient evolution law of the corrosion process of Co-12 cladding layer on QT600 surface.

In this paper, a three-dimensional numerical model of the corrosion process of Co-12 cladding layer by QT600 laser cladding is established. The interaction between pitting pits and corrosion medium is considered to reveal the transient evolution of ion concentration, electrode potential, pH and corrosion rate at different locations.

The calculation shows that the ion concentration in pitting pit changes Cl⁻>Co²⁺>Na⁺, pH value decreases from top to bottom and corrosion rate at bottom is greater than that at top. The electrochemical corrosion test of Co-12 cladding layer was carried out. It is shown that the current density of QT600 increases by an order of magnitude compared to the Co-12 cladding layer, and the corrosion rate is 4.862 times higher than that of the cladding layer.

The results show that Co-12 cladding layer has great corrosion resistance, which provides an effective way for QT600 protection.

Construction organizations' investment in effective talent-development programs is a key strategy in attracting, developing and retaining staff. Such programs are especially important given the current challenges in the construction workforce, including labor shortages, an aging workforce, generational differences in the workforce, supply chain disruptions and the need to effectively train staff in the skills that are essential in a constrained labor environment. To address these challenges, this study proposes a performance measurement strategy that construction companies can use as input to design their talent development programs.

The strategy intends to assess the performance of project managers and develop criteria that define categories of their performance, including the top performers' category. This enables construction organizations to provide each project manager with individualized training that addresses areas of weakness and in turn, develops the skills that correspond with being top performers. The proposed strategy was developed and tested by surveying the immediate supervisors of 187 project managers working for general and specialty contractors in the United States. Principal component analysis was used to develop a single performance construct from seven performance criteria.

This construct was used to organize the project managers into the categories of top, above-average and below-average performers. According to the findings, top-performing project managers have well-rounded skills in the areas of leadership, communication, technical proficiency and overall job knowledge.

The outcomes of this study can help construction organizations focus their talent-development programs on the skills most associated with PMs being top performers.

This study provides construction organizations with a comprehensive performance-measuring construct to focus their talent-development programs on the skills most associated with top-performing project managers. Researchers can use this study as a foundation for further understanding how performance is related to various construction professions.

This study aims to provide empirical evidence on the return and volatility spillover structures between Bitcoin, Fintech stocks and Asian-Pacific equity markets over time and during different market conditions, and their implications for portfolio management.

We use Time-varying parameter vector autoregressive and quantile frequency connectedness approach models for the connectedness framework, in conjunction with Diebold and Yilmaz’s connectivity approach. Additionally, we use the minimum connectedness portfolio model to highlight implications for portfolio management.

Regarding the uncertainty of the whole system, we show a small contribution from Bitcoin and Fintech, with a higher contribution from the four Asian Tigers (Taiwan, Singapore, Hong Kong and Thailand). The quantile and frequency analyses also demonstrate that the link among assets is symmetric, with short-term spillovers having the largest influence. Finally, Bitcoins and Fintech stocks are excellent diversification and hedging instruments for Asian equity investors.

There is an instantaneous, symmetric and dynamic return and volatility spillover between Asian stock markets, Fintech and Bitcoin. This conclusion should be considered by investors and portfolio managers when creating risk diversification strategies, as well as by policymakers when implementing their financial stability policies.

The study’s major contribution is to analyze the volatility spillover between Bitcoin, Fintech and Asian stock markets, which is dynamic, symmetric and immediate.