Search results

Stakeholder theory understands business in terms of relationships among stakeholders whose interests are mainly joint but may be occasionally conflicting. In the latter case, managers may need to make trade-offs between these interests. The purpose of this paper is to explore the nature of managerial decision-making about these trade-offs.

This paper draws on the ordonomic approach which sees business life to be rife with social dilemmas and locates the role of stakeholders in harnessing or resolving these dilemmas through engagement in rule-finding and rule-setting processes.

The ordonomic approach suggests that stakeholder interests trade-offs ought to be neither ignored nor avoided, but rather embraced and welcomed as an opportunity for bringing to fruition the joint interest of stakeholders in playing a better game of business. Stakeholders are shown to bear responsibility for overcoming the perceived trade-offs through the institutional management of social dilemmas.

For many stakeholder theorists, the nature of managerial decision-making about trade-offs between conflicting stakeholder interests and the nature of trade-offs themselves have been a long-standing point of contention. The paper shows that trade-offs may be useful for the value creation process and explicitly discusses managerial strategies for dealing with them.

Among the proposed radio access strategies for improving system execution in 5G networks, the non-orthogonal multiple access (NOMA) scheme is the prominent one.

Among the most fundamental NOMA methods, power-domain NOMA is the one where at the transmitter, superposition coding is used, and at the receiver, successive interference cancellation (SIC) is used. The importance of power allocation (PA) in achieving appreciable SIC and high system throughput cannot be overstated.

This research focuses on an outage probability analysis of NOMA downlink system under various channel conditions like Rayleigh, Rician and Nakagami-m fading channel. The system design's objectives, techniques and constraints for NOMA-based 5G networks' PA strategies are comprehensively studied.

From the results of this study, it is found that the outage probability performance of downlink ordered NOMA under Rayleigh, Rician and Nakagami-m fading channel was good.

Outage probability analysis of downlink ordered NOMA under various channel conditions like Rayleigh, Rician and Nakagami-m fading channels were employed. Though the performance of Nakagami-m fading channel is lesser compared to Rayleigh channel, the performance for user 1 and user 2 are good.

This paper is devoted to prepare micro-cone structure with variable cross-section size by Stereo Lithography Appearance (SLA)-based 3D additive manufacturing technology. It is mainly focused on analyzing the forming mechanism of equipment and factors affecting the forming quality and accuracy, investigating the influence of forming process parameters on the printing quality and optimization of the printing quality. This study is expected to provide a µ-SLA surface preparation technology and process parameters selection with low cost, high precision and short preparation period for microstructure forming.

The µ-SLA process is optimized based on the variable cross-section micro-cone structure printing. Multi-index analysis method was used to analyze the influence of process parameters. The process parameter influencing order is determined and validated with flawless micro array structure.

After the optimization analysis of the top diameter size, the bottom diameter size and the overall height, the influence order of the printing process parameters on the quality of the micro-cone forming is: exposure time (B), print layer thickness (A) and number of vibrations (C). The optimal scheme is A1B3C1, that is, the layer thickness of 5 µm, the exposure time of 3000 ms and the vibration of 64x. At this time, the cone structure with the bottom diameter of 50 µm and the cone angle of 5° could obtain a better surface structure.

This study is expected to provide a µ-SLA surface preparation technology and process parameters selection with low cost, high precision and short preparation period for microstructure forming.

The purpose of the paper is to present an empirical study that examines the impact of digitalization on informative, strategic and operational marketing activities in manufacturing companies from the entrepreneurial perspective.

A research project was carried out in 205 Italian manufacturing companies by using the questionnaire method. An exploratory research study was conducted with hierarchical cluster analysis.

The analysis shows the existence of seven clusters of manufacturing companies that differ by the impact of digitalization on marketing activities from the entrepreneurial perspective. Two clusters have a high positive impact of digitalization, primarily on informative and strategic marketing activities. Two clusters are characterized by a low positive impact of digitalization and three clusters perform an intermediate level of digitalization. Furthermore, these groups of clusters differ in terms of the influence of digitalization on customer value.

The small size of the sample and the geographic origin of the companies imply limited generalizability; further research on the topic is thus recommended.

The study suggests that companies should digitalize many key marketing activities to increase marketing effectiveness and customer value. To achieve high levels of digitalization and thus increase their competitiveness, manufacturing companies should consider the importance of relevant technologies and skills.

By focussing on the impact of digitalization on informative, strategic and operational marketing, which has not yet been empirically investigated, the present study reveals many new elements concerning the marketing process in the digital era from the entrepreneur's point of view.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

Bi-slant submanifolds of S-manifolds are introduced, and some examples of these submanifolds are presented.

Some properties of D_i-geodesic and D_i-umbilical bi-slant submanifolds are examined.

The Riemannian curvature invariants of these submanifolds are computed, and some results are discussed with the help of these invariants.

The topic is original, and the manuscript has not been submitted to any other journal.

Using composite indices, the paper examines the nexus between militarization, globalization and liberal democracy. The democratic peace theory, the conflict inhibiting effects of international trade – a key and dominant facet of globalization – and the democracy promoting globalization hypothesis form the theoretical underpinnings of the empirical investigation.

To probe into the issue at hand, the paper adopts a dynamic panel VAR estimation procedure. Given the usual data constraints, the sample consists of 113 countries, and the estimations span the period 1995–2019.

The findings from the dynamic panel VAR estimations suggest the presence of a negative and statistically significant nexus between the level of globalization and the level of militarization. No statistically traceable nexus between globalization and liberal democracy was found.

The findings offer empirical support to the hypothesis that the strong links of interdependence shaped by globalization reduce the need for military preparedness. The results lead to a tentative inference in favor of the doux commerce thesis. Nonetheless, given that the estimations span a historically specific period – the entire post-bipolar era – the inferences that stem from the findings should be treated with caution.

To the best of the authors’ knowledge, the composite indices Bonn International Centre for Conflict Studies (BICC) militarization index, the globalization index of the Swiss Economic Institute (Konjunkturforschungsstelle) (KOF), LibDem, polyarchy have not hitherto been jointly used in previous studies to examine the nexus between militarization, globalization and liberal democracy.

We aimed to identify factors that influence student engagement in distance learning.

The research involved a group of 671 students from economic and technical higher education institutions in Poland. We collected the data with the CAWI technique and an original survey. Next, we processed the data using principal component analysis and then used the extracted components as predictors in the induced smoothing LASSO regression model.

The components of the students’ attitude toward remote classes learning conditions are: satisfaction with teachers’ approach, attitude to distance learning, the system of students’ values and motivation, IT infrastructure of the university, building a network of contacts and communication skills. The final model consisted of seven statistically significant variables, encompassing the student’s sex, level of studies and the first five extracted PCs. Student’s system of values and motivation as well as attitude toward distance learning, were those variables that had the biggest influence on student engagement.

The research result suggests that in addition to students’ system of values and motivation and their attitude toward distance learning, the satisfaction level of teachers’ attitude is one of the three most important factors that influence student engagement during the distance learning process.

The main value of this article is the statistical model of student engagement during distance learning. The article fills the research gap in identifying and evaluating the impact of various factors determining student engagement in the distance learning process.

The authors’ goal is to provide an overview and historical context for the various alternatives to the efficient market hypothesis (EMH) that have emerged over time. The authors found eight current alternatives that have emerged to address the EMH's flaws. Each of the proposed alternatives improves some of the assumptions made by the EMH, such as investor homogeneity, the immediate incorporation of information into asset values and the inadequacy of rationality to explain asset prices.

To come up with the list of studies relevant to this review article, the authors used three databases, namely Scopus, Web of Science and Google Scholar. The first two were mostly used to get peer-reviewed articles while Google Scholar was used to extract articles that are still work in progress. The following words were used as the search queries; “efficient market hypothesis” and “alternatives to the efficient market hypothesis”.

The alternatives to the EMH presented in this article demonstrate that market efficiency is a dynamic concept that can be best understood with a multidisciplinary approach. To better comprehend how financial markets work, it is crucial to draw on concepts, theories and ideas from a variety of disciplines, including physics, economics, anthropology, sociology and others.

The authors comprehensively summarise the current state of the behavioural finance literature on alternatives to the EMH.

Purpose: The purpose of this paper is to identify the factors involved in the success of a ‘first-time first-line Manager’ (FTFLM), and establish a model for the success of a FTFLM using these identified factors.

Methods: Empirical factors influencing the success of a first-line manager were gathered through the literature review and an experience survey of managers. Based on these factors, a questionnaire for primary research was developed and used for the survey. An exploratory factor analysis of the collected data yielded a nine-factor model which was validated using confirmatory factor analysis.

Findings: A major portion (74%) of the FTFLM’s success factors are under the direct control of the FTFLM. The remaining portion (26%) are influenced by the organization’s procedures and policies for grooming the FTFLM and the attitude and behaviour of management towards the FTFLM.