Search results

Explores the relationship between culture and innovation. Discusses culture at some length, noting some differences between national behaviours – such as the relative importance of a group versus an individual. Infers that existing cultural conditions determine the way in which innovations are adopted. Indicates that cultures which value creativity, technical ability and higher education are more successful at adopting innovations. Identifies a relationship between innovation and the status given to entrepreneurial efforts. Suggests that cultures emphasizing individualism and freedom are more likely to be creative and, therefore, to benefit more from innovative ideas. Refers to previous studies on culture and innovation (mentioning Hofstede’s work). Investigates the role religion plays, in particular the cultural bias against technology that is prevalent in traditionalist religious countries. Concludes that strongly religious countries are not receptive to innovation.

The purpose of this paper is to investigate the impact of cloud computing (CC) on supply chain management (SCM).

The paper is conceptual and based on a literature review and conceptual analysis.

Today, digital technology is the primary enabler of supply chain (SC) competitiveness. CC capabilities support competitive SC challenges through structural flexibility and responsiveness. An Internet platform based on CC and a digital ecosystem can serve as “information cross-docking” between SC stakeholders. In this way, the SC model is transformed from a traditional, linear model to a platform model with the simultaneous cooperation of all partners. Platform-based SCs will be a milestone in the evolution of SCM – here conceptualised as Supply Chain 3.0.

Currently, SCs managed holistically in cyberspace are rare in practice, and therefore empirical evidence on how digital technologies impact SC competitiveness is required in future research.

This research generates insights that can help managers understand and develop the next generation of SCM with the use of CC, a modern and commonly available Information and Communication Technologies (ICT) tool.

The paper presents a conceptual basis of how CC enables structural flexibility of SCs through easy, real-time resource and capacity reconfiguration. CC not only reduces cost and increases flexibility but also offers an effective solution for disruptive new business models with the potential to revolutionise current SCM thinking.

In recent years, the rapid growth of big data has presented immense potential for business applications as well as raised great interest from academia. In response to this emerging phenomenon, the purpose of this paper is to provide a comprehensive literature review of big data.

A bibliometric method was used to analyze the articles obtained from the Scopus database published between 2013 and 2018. A sample size of 4,070 articles was evaluated using SciVal metrics.

The analysis revealed an array of interesting findings as follows: the number of publications related to big data increased steadily over the past six years, though the rate of increase has slowed since 2014; the scope of big data research is quite broad in regards to both research domains and countries; despite a large volume of publications, the overall performance of big data research is not well presented as measured by the field-weighted citation impact metric; collaboration between different institutions, particularly in the form of international collaboration and academic–corporate collaboration, has played an important role in improving the performance of big data research.

To the best of the authors’ knowledge, this is the first study to provide a holistic view of the big data research. The insights obtained from the analysis are instrumental for both academics and practitioners.

Corporate failure remains a critical financial concern, with implications for both firms and financial institutions; this paper aims to review the literature that proposes corporate failure prediction models for the twenty-first century.

This paper gathers information from 106 published articles that contain corporate failure prediction models. The focus of the analysis is on the elements needed to design corporate failure prediction models (definition of failure, sample approach, prediction methods, variables and evaluation metrics and performance). The in-depth review creates a synthesis of current trends, from the view of those elements.

Both consensus and divergences emerge regarding the design of corporate failure prediction models. On the one hand, authors agree about the use of bankruptcy as a definition of failure and that at least two evaluation metrics are needed to examine model performance for each class, individually and in general. On the other hand, they disagree about data collection procedures. Although several explanatory variables have been considered, all of them serve as complements for the primarily used financial information. Finally, the selection of prediction methods depends entirely on the research objective. These discrepancies suggest fundamental advances in discovery and establish valuable ideas for further research.

This paper reveals some caveats and provides extensive, comprehensible guidelines for corporate failure prediction, which researchers can leverage as they continue to investigate this critical financial subject. It also suggests fruitful directions to develop further experiments.

The purpose of this study is to establish a vibration prediction of pellet mills power transmission by artificial neural network. Vibration monitoring is an important task for any system to ensure safe operations. Improvement of control strategies is crucial for the vibration monitoring.

As predictive control is one of the options for the vibration monitoring in this paper, the predictive model for vibration monitoring was created.

Although the achieved prediction results were acceptable, there is need for more work to apply and test these results in real environment.

Artificial neural network (ANN) was implemented as the predictive model while extreme learning machine (ELM) and back propagation (BP) learning schemes were used as training algorithms for the ANN. BP learning algorithm minimizes the error function by using the gradient descent method. ELM training algorithm is based on selecting of the input weights randomly of the ANN network and the output weight of the network are determined analytically.

The purpose of this paper is to apply the conjugate gradient (CG) method, together with the adjoint operator (AO) to the pulsating heat pipe problem, including some quite interesting experimental results. The CG method, together with the AO, was able to estimate the unknown functions more efficiently than the other techniques presented in this paper. The estimation of local heat transfer coefficients, rather than the global ones, in pulsating heat pipes is a relatively new subject and presenting a robust, efficient and self-regularized inverse tool to estimate it, supported also by some experimental results, is the main purpose of this paper. To also increase the visibility and the general use of the paper to the heat transfer community, the authors include, as supplemental material, all numerical and experimental data used in this paper.

The approach was established on the solution of the inverse heat conduction problem in the wall by using as starting data the temperature measurements on the outer surface. The procedure is based on the CG method with AO. The here proposed approach was first verified adopting synthetic data and then it was validated with real cases regarding pulsating heat pipes.

An original fast methodology to estimate local convective heat flux is proposed. The procedure has been validated both numerically and experimentally. The procedure has been compared to other classical methods presenting some peculiar benefits.

The approach is suitable for pulsating heat pipes performance evaluation because these devices present a local heat flux distribution characterized by an important variation both in time and in space as a result of the complex flow patterns that are generated in this type of devices.

The procedure here proposed shows these benefits: it affords a general model of the heat conduction problem that is effortlessly customized for the particular case, it can be applied also to large datasets and it presents reduced computational expense.

This study aims to develop and test a multivariable psychosocial prediction model of subjective well-being in Ukrainian adults (n = 1,248) 1.5 years after the 2022 Russian invasion of Ukraine.

The research design followed the “Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis” checklist. The online survey combined a questionnaire on sociodemographic characteristics and specifics of living in wartime, as well as validated self-reported inventories: The Modified BBC Subjective Well-being Scale, Acceptance and Action Questionnaire – Version 2 and Connor–Davidson Resilience Scale-10.

The initially developed model was tested through regression analysis, which revealed nine variables as predictors of the subjective well-being scores within the sample, explaining 49.3% of its variance. Among them, the strongest were living with a friend and receiving mental health care systematically. They were almost twice as influential as forced displacement abroad and trauma exposure, which predicted lower well-being, and living with a spouse, which forecasted higher well-being scores. Two resilience subscales – adjustment and restoring and resistance – as predictors of better well-being and perceived unsuccess in life and age as predictors of lower well-being were relatively weaker but statistically significant.

The obtained results support the previous evidence on the essential role of accessible mental health services and social support in times of war, as well as the deteriorative effect of trauma exposure and forcible taking refuge on subjective well-being.

Tourists' environmentally responsible behaviour (ERB) is very important for the environmental sustainability of popular tourist destinations. The main purpose of this research paper is to explore the relationship between environmental citizenship behaviour (ECB) and trust in local government with ERB.

A theoretical model was proposed and tested. Data were collected from domestic tourists (n = 555) visiting Antalya, on the Mediterranean coast of Turkey.

The results show that all dimensions of ECB (helping, engagement and initiatives) have a positive effect on the formation of ERB among domestic tourists. The results also show that trust in the local government of the destination has a moderating effect on the relationship between the two dimensions of ECB (engagement and initiatives) and ERB. The helping dimension of ECB has the highest positive impact factor on ERB. The results provide a comprehensive framework for further research. The proposed model is strongly supported by the findings.

Local policy makers can focus on how to increase the environmental motivation of domestic tourists. Environmentally friendly projects can be developed to attract their attention. This will ensure that they take more responsibility for the environment at the destination.

Understanding the potential impact of local government on tourist behaviour is crucial for sustainability. However, there are few studies that directly examine the moderating effect of trust in local government on the relationship between ECB and ERB. This study addresses this gap and makes novel contributions to the relevant literature.

The purpose of this paper is to investigate the relevance of authenticity as a possible attribute of employer attractiveness. Additionally, the study compares authenticity to other factors of attractiveness, such as economic, development, social, interest and application values.

A survey was conducted with a total of 937 respondents. The questionnaire consisted of the employer attractiveness scale developed by Berthon, Ewing, and Hah (2005) and an adapted version of the authentic living scale (Wood et al., 2008).

The results show that workplace authenticity is equally valued as an attractiveness attribute as having opportunities for economic and personal development, and that it is significantly more highly valued than other attractiveness dimensions of the work environment, such as interest value, social value, and application value. The results also show that authenticity matters more as an attribute of attractiveness for top management, older professionals as well as women.

The findings suggest that firms become more competitive in attracting talent if their recruitment strategies place more emphasis on authenticity as a psychological benefit that can be obtained through working in the company. The use of social media (e.g. employee testimonials, chats, and blogs) can help to this end.

The subject of workplace authenticity has been receiving increasing attention in the academic literature, and the studies reveal the benefits that it may entail for both developing and retaining a more engaged and productive workforce. However, previous research has not considered how perceptions of workplace authenticity may also help organizations become more attractive in the eyes of potential job applicants.

This paper aims to examine for differences in potentially productive fear versus all other fear scores comparing adolescents and young adults with intellectual disabilities versus their typically developing peers without intellectual disabilities while statistically controlling for chronological age.

The authors examined for differences in potentially productive fear scores versus all fear scores comparing adolescents and young adults with intellectual disabilities versus their typically developing peers without intellectual disabilities.

Results of the current study highlight the importance of examining and statistically acknowledging the form of the relationship between age and fear. As previous research has indicated the relationship of age with fear, this form of the relationship should be acknowledged in subsequent analyses. Results also present a conceptualization of fear with some fears being considered as potentially productive fears as it relates to independent living.

As previous research has indicated the relationship of age with fear, this form of the relationship should be acknowledged in subsequent analyses. Finally, results present a conceptualization of fear with some fears being considered as potentially productive fears as it relates to independent living as an outcome.

The current study presents a conceptualization of fear with some fears being considered as potentially productive fears as it relates to independent living as an outcome.

Results support that potentially productive fears can be considered fears that can be used to promote learning that leads to independent living outcomes such as safety and security.