Search results

This study aims to analyze the efficient market hypothesis (EMH) of cryptocurrencies on multiple platforms by observing whether there is a discrepancy in the levels of efficiency between different exchanges. Additionally, EMH is tested in a multivariate way: whether the prices of the same cryptocurrencies traded on different exchanges are temporally related to each other. ADF and KPSS tests, whereas the vector autoregression model of order p – VAR(p) – for multivariate system.

Both Bitcoin and Ethereum show efficiency in the weak form on the main platforms in each market alone. However, when estimating a VAR(p) between prices among exchanges, there was evidence of Granger causality between cryptocurrencies in all exchanges, suggesting that EMH is not adequate due to cross information.

It is essential to assess the cryptocurrency market in a multivariate way, not only to favor its maturation process, but also to promote a broad understanding of its inherent risks. Thus, it will be possible to develop financial products that are actively managed in a more sophisticated cryptocurrency market.

There is a possibility of performing arbitrage on different exchanges and market assets through cross-exchanges. Thus, emphasizing the need for regulation of exchanges in the digital asset market, as an eventual price manipulation on a single platform can impact others, which generates various distortions.

This study is the first to find evidence of cross-information for the same (and other) cryptocurrencies among different exchanges.

This comparative book review is concerned with two recent studies of essential workers in Germany: Jana Costas’ Dramas of Dignity and Peter Birke’s Grenzen aus Glas [literally ‘borders made from glass’]. While Costas is interested in studying how individual cleaners preserve their sense of dignity despite their widely believed stigmatizing work roles, Birke is interested in the power resources migrant workers can potentially mobilize for improving their working conditions despite the multi-dimensional (inter-sectional) precarity they confront in their life situation. In the context of German industrial and organizational sociology, both studies represent comparatively rare exemplars of detailed qualitative and ethnographic work that illuminate the labour process from taking a workers’ perspective. Using different approaches to fieldwork, both studies reveal the precarious nature of being an essential worker in areas such as meat packing, warehouse work, and cleaning. This general observation gives rise to some concluding speculations about the emancipatory potential of ethnographic research, in labour studies and beyond.

This study aims to identify the determinants of customer disengagement (CD) and negative customer engagement (NCE) behaviours following service failure.

This study distributed a survey on negative service experiences to 404 customers in Vietnam and analysed the data using structural equation modelling.

Based on the findings, this paper developed a comprehensive model of the determinants of CD and NCE behaviours. CD manifests as “neglect”, while NCE manifests as vindictive, third-party and online complaints and negative word of mouth. The key drivers of CD and NCE are negative expectancy disconfirmation and perceived injustice, mediated by customer outrage. A novel finding is that self-efficacy and risk-taking traits enhance NCE behaviours. Vietnamese customers tend to adopt less confrontational NCE behaviours.

The findings provide brand managers with insights into unfavourable customer responses to service failure, including CD and NCE behaviours. Customers in Vietnam were predominantly found to disengage. Fulfilling the firm’s promises and treating customers fairly are paramount for preventing customer outrage, CD and NCE.

This study identifies the determinants of CD and NCE, namely, disconfirmation of service quality expectations and perceived injustice, in the context of an emerging market.

This study aims to supplement the international knowledge on factors determining retention in the teaching profession with findings from the Czech Republic. The study aims to answer the question of what factors on the part of schools and teachers are related to teachers’ decision to leave the teaching profession, either temporarily or permanently. It also examines the differences between teachers at the beginning and end of their professional careers.

This study presents a secondary analysis of the data collected in a survey of 1,230 Czech secondary school teachers implemented in 2021 in the same schools as the TALIS survey in 2018. The longitudinal design makes it possible to observe the impact of school variables on teachers’ willingness to remain in the profession. Data are analysed through logistic regression.

The analysis shows the importance of sufficient financial evaluation, the composition of the student body, instructional leadership, and school innovativeness for the retention of teachers in the profession. The analysis further shows that the factors causing teachers to leave the profession differ for those at the beginning and at the end of their teaching careers.

The limitations of the study lie in the fact that this is a secondary analysis. The questionnaires were not designed in accordance with the aim of our study, and so the variables were constructed from existing data to best fit our chosen framework.

The results of our analysis indicate that the Czech education policy should focus not only on general increases in teachers’ salaries, but also on the adjustment of increases over the course of a teacher’s career. The starting salary may be important for retaining young teachers in the profession, while at later stages in their careers their decision to stay may be more influenced by other factors. Our research also showed the importance of leadership. In schools where the principal supports his or her teachers and takes care of their professional development, teachers have a greater tendency to stay in the profession than in schools where the management does not perform this function.

The study enriched international knowledge about factors affecting teachers' retention in the profession with findings from the Czech educational system characterized by high school autonomy and low teacher salaries.

The purpose of this paper is to develop an inorganic-organic hybrid emulsion polymer using grafted hyperbranched alkyd modified with nanometal oxide and to study the performance of the developed hybrid emulsion in paint formulation with respect to antimicrobial and other properties.

A novel hybrid emulsion polymer was synthesized by grafting of vinyl acetate (VAM), vinyl ester of versatic acid (VeoVa 10) monomers onto hyperbranched alkyd resin and incorporation of nano magnesium oxide (MgO) into the hybrid resin matrix during dispersion of resin in water. Subsequently, paint was prepared by using this hybrid emulsion followed by a performance study.

The performance of nano MgO modified into VAM and VeoVa 10 grafted hyperbranched alkyd resulted in unique properties of coating especially antimicrobial activity.

In the present study, soya fatty acid, polyol and di basic acid have been used to prepare hyperbranched alkyd by condensation polymerization. Monomers like VAM and VeoVa 10 used for grafting onto hyperbranched alkyd and nano MgO have been incorporated into hybrid resin matrix during emulsification of hybrid resin in water.

Grafting of VAM and VeoVa 10 onto hyperbranched alkyd along with in situ incorporation of nano MgO in resin matrix is an effective technique to achieve excellent coating properties.

Nano MgO modified VAM and VeoVa 10 grafted hyperbranched alkyd-based hybrid emulsion can be used as binder in water-based metal primer, direct to metal (DTM) coating as well as topcoat application. The developed system has antimicrobial properties as well as superior mechanical properties.

The purpose of this study is to begine to address this question. The concept of the ideas-informed society (IIS) represents a desired situation in which citizens actively and critically engage with new ideas, developments and claims to truth. Its successful actualisation is dependent on high-quality educational opportunity at all stages of the life course. Social networks represent our connections to one another. Features of our social networks impact on how we engage with ideas. For instance, homophily dictates that individuals form networks with others seen as being like themselves. A key question, however, is whether there are forms of homophily that, by the nature of those they bring together, promote ideas engagement by individuals and the implications of consequent networks for the IIS?

This study re-analysed survey data from 1,000 voting-age citizens in England. Focusing on friendship networks, the authors used a structural equation model approach to explore the existence and potency of homophilic friendship networks; whether such networks drive respondents’ ideas-engagement with friends; and whether ideas discussions with friends impacts on the importance respondents place on staying up to date.

Political homophily has the strongest influence on whether people discuss new ideas with their friends (ES = 0.326, p < 0.01). In turn, ideas discussion has a significant impact on the extent to which people value engaging with ideas (ES = −0.345, p < 0.01).

The authors consider whether ideas-related discussion within politically homophilous networks is problematic for the IIS and what is required from education systems if we are to build individuals’ capacity to engage with ideas while escaping echo chambers.

This study aims to use deep neural networks (DNNs) to learn the conduction heat transfer physics and estimate temperature distribution images in a physical domain without using any physical model or mathematical governing equation.

Two novel DNNs capable of learning the conduction heat transfer physics were defined. The first DNN (U-Net autoencoder residual network [UARN]) was designed to extract local and global features simultaneously. In the second DNN, a conditional generative adversarial network (CGAN) was used to enhance the accuracy of UARN, which is referred to as CGUARN. Then, novel loss functions, introduced based on outlier errors, were used to train the DNNs.

A UARN neural network could learn the physics of heat transfer. Within a few epochs, it reached mean and outlier errors that other DNNs could never reach after many epochs. The composite outlier-mean error as a loss function showed excellent performance in training DNNs for physical images. A UARN could excellently capture local and global features of conduction heat transfer, whereas the composite error could accurately guide DNN to extract high-level information by estimating temperature distribution images.

This study offers a unique approach to estimating physical information, moving from traditional mathematical and physical models to machine learning approaches. Developing novel DNNs and loss functions has shown promising results, opening up new avenues in heat transfer physics and potentially other fields.

The purpose of this paper is to research the impact of digitalization on employees' future competencies and the conditional role of human resource development (HRD) in the relationship between independent and dependent variables.

Empirical research covered 1209 enterprises from all of Slovakia, Poland and the Czech Republic. The research was conducted from 2019 to 2021. Using structural equation modeling (SEM), a theoretical model was tested and verified.

Confirmatory factor analysis has shown a good fit for the tested model. The purpose and character of our data showed a good alignment with the SEM partial least squares method, as the goal is to predict a construct. The model showed that employee-oriented digitalization positively affected the employees' future competencies, with no impact of customer-oriented digitalization treated as a control variable. Also, the moderating role of HRD has not been shown to be significant for the “digitalization – competencies” relationship.

Previous studies on the development of personnel competencies treated these competencies as antecedents of digital transformation and examined the formal role of HRD in building the competencies. The novelty of this study lies in exploring the pattern of interactions among the impact of an environment built by innovative technologies and HRD on the competencies of the future. Also, the research embedded in the environment of Poland, the Czech Republic and Slovakia has contributed to the complex understanding of the transition to digitalization, as this region has often been omitted in the field of human resource management (HRM) research focused on exploring digital transformation.

This paper aims to help workplace ethnographers navigate and reflect on primary access negotiations by scrutinising two of the concepts mentioned in the call for papers on this special issue: workplace relations and tensions. We introduce the frames of reference (FoRs) concept as used in the field of employment relations to the ethnographic community. We propose that the implicit frames of gatekeeper and researcher influence what they deem interesting for research, thus influencing the content of access negotiations. Moreover, we propose that tensions typically emerge when gatekeepers and ethnographers do not share the same frame of the employment relationship (ER).

We explore the ER through Fox’s (1966, 1974) framework, taking inspiration from Budd et al. (2022), who applied FoRs to employer–employee relations. We adapt the framework to the relationships between workplace ethnographers and gatekeepers by theorising the characteristics of ideal types of gatekeepers and workplace ethnographers and exploring possible implications for when they meet in access negotiations. We distil lessons learnt from previous research by drawing on illustrative examples from the literature to suggest strategies for interacting with gatekeepers when tensions emerge, providing a pragmatic application of our contribution.

Assuming that their FoR of the ER contributes to what they find to be of practical relevance/academic interest, we suggest that a (mis)match of gatekeepers’ and workplace ethnographers’ FoRs can lead to tensions between workplace ethnographers and gatekeepers, either remaining latent or becoming salient. We propose three possible strategies as to how to navigate these tensions during primary access negotiations.

Whilst previous research has mainly focused on the ethnographer as an individual who needs to give gatekeepers a reassuring and enticing impression, we discuss how an important structural factor, an organisation’s ER setup, may influence access. We thus bring an important yet hitherto neglected aspect of organisational life into the debate on the pragmatic realities of ethnography, contributing to the discussion of how to navigate the tension between the “practical” need to convince gatekeepers and the need to fulfil one’s own standards of rigorous research and ethics.

New business density (NBD) is the ratio of the number of newly registered liability corporations to the working-age population per year. NBD is critical to assessing a country's business environment. The present work endeavors to discover and gauge the contribution of 28 potential socio-economic enablers of NBD for 2006–2021 across developed and developing economies separately and to make a comparative assessment between those two regions.

Using World Bank data, the study first performs exploratory data analysis (EDA). Then, it deploys a deep learning (DL)-based regression framework by utilizing a deep neural network (DNN) to perform predictive modeling of NBD for developed and developing nations. Subsequently, we use two explainable artificial intelligence (XAI) techniques, Shapley values and a partial dependence plot, to unveil the influence patterns of chosen enablers. Finally, the results from the DL method are validated with the explainable boosting machine (EBM) method.

This research analyzes the role of 28 potential socio-economic enablers of NBD in developed and developing countries. This research finds that the NBD in developed countries is predominantly governed by the contribution of manufacturing and service sectors to GDP. In contrast, the propensity for research and development and ease of doing business control the NBD of developing nations. The research findings also indicate four common enablers – business disclosure, ease of doing business, employment in industry and startup procedures for developed and developing countries.

NBD is directly linked to any nation's economic affairs. Therefore, assessing the NBD enablers is of paramount significance for channelizing capital for new business formation. It will guide investment firms and entrepreneurs in discovering the factors that significantly impact the NBD dynamics across different regions of the globe. Entrepreneurs fraught with inevitable market uncertainties while developing a new idea into a successful new business can momentously benefit from the awareness of crucial NBD enablers, which can serve as a basis for business risk assessment.

DL-based regression framework simultaneously caters to successful predictive modeling and model explanation for practical insights about NBD at the global level. It overcomes the limitations in the present literature that assume the NBD is country- and industry-specific, and factors of the NBD cannot be generalized globally. With DL-based regression and XAI methods, we prove our research hypothesis that NBD can be effectively assessed and compared with the help of global macro-level indicators. This research justifies the robustness of the findings by using the socio-economic data from the renowned data repository of the World Bank and by implementing the DL modeling with validation through the EBM method.