The Role of Entrepreneurial Intentions, Perceived Risk and Perceived Trust in Crowdfunding Intentions

Crowdfunding is a new financial and marketing tool, which is used to raise money for new projects and to promote innovative products. The aim of this paper is to investigate the influencing factors of crowdfunding intentions among students as future or current entrepreneurs. Drawing from the Theory of Planned Behaviour and the Unified Theory of Acceptance and Use of Technology, we analyse the data from two culturally and entrepreneurship-wise different countries, Romania and South Korea using PLS-SEM (N=441). Entrepreneurial intentions, perceived risk and perceived trust are found to positively influence crowdfunding intentions among business, economics and management students in both countries. We further check the influence of attitude towards entreprenurship, social norms and perceived behavioural control, entrepreneurial education and desire for success on entre-preneurial intentions, and report positive correlations for the whole analyzed sample. Moreover, social influence and facilitating conditions positively influence the entrepreneurs’ perceived risk, and effort expectancy and performance expectancy positively influence perceived trust. Whereas the Romanian sample does not exhibit any influence of social norms on entrepreneurial intentions, entrepreneurial education is not correlated with entrepreneurial intentions in South Korea.

Traditional and Developing Segments of the Sharing Economy from the Point of View of Customers

The sharing economy represents a phenomenon which is increasing in importance as time goes by, even from the point of view of less developed countries. The aim of the study is to evaluate the knowledge of traditional and developing segments of the sharing economy and their platforms and to analyse and evaluate the level of their use employing the example of customers (the younger and the older generation) in one of the post-communist countries – the Czech Republic. Data was collected on the basis of a questionnaire survey (N=614). This was processed using descriptive statistics tools. The study shows that respondents are the most familiar with forms of sharing not only from traditional segments (passenger transportation and accommodation) but also from the financial segment. The best-known platforms include Uber, Airbnb and Zonky. Platforms from traditional segments are used the most. The younger generation has better awareness, both about possible forms of sharing and also about the existing platforms. However, the study did not prove any differences in the level of use of platforms between the younger and older generation.

Assessment of the Degree of Implementation of Industry 4.0 Technologies: Case Study of Murcia Region in Southeast Spain

This work analyzes how the so-called Industry 4.0 technologies are being implemented in companies in the Region of Murcia, in Southeastern Spain. The objective was to determine through questionnaires and face-to-face interviews the current state of 4.0 technologies in Murcia, including additional data of the companies, such as age, number of employees and turnover. Most types of companies in the Region were represented in terms of size, age, turnover, profits and profitability. This study analyzes the relationship between the degree of implementation of 4.0 technologies, investment and training of workers, with companies’ seniority, number of employees, turnover, profits and profitability. The results obtained are significantly higher in companies with higher turnover, profits and profitability, which in turn, have the best levels of investment and training of their workers in 4.0 technologies. The opinions of the companies determined the factors that drove the companies to implement these technologies, the factors perceived as barriers, the opportunities in the current context that encourage the adoption of technologies, as well as the threats that may jeopardize their progress in digital transformation. The conclusions obtained can be taken into account in regional policies that implement appropriate actions to help drive the fourth industrial revolution in the region.

The Impact of E-Commerce Sales on Capacity Utilization

This study aims to examine the relationship between e-commerce sales and capacity utilization in China, with process innovation being the mediator and product focus being the moderator. A mediated moderation model was developed and tested using data from 804 Chinese manufacturing firms as well as two-stage least squares regression analysis. The results reveal that the relationship between e-commerce sales and capacity utilization is negative; while process innovation mediates this relationship. Furthermore, product focus not only moderates the relationship between e-commerce sales and capacity utilization, but also moderates the relationship between process innovation and capacity utilization. These findings are useful for decision-makers when formulating e-commerce sales strategies and focusing on process innovation that will help them achieve higher capacity utilization. This paper contributes to existing research by validating process innovation as mediator and product focus as moderator between e-commerce sales and capacity utilization.

Managerial Perception of Human Capital, Innovations, and Performance: Evidence from Banking Industry

This paper aims to examine the relationship between the managerial perception of human capital, innovations, and bank performance. We specifically sought to examine the influence of human capital on bank performance, by introducing the factors of innovation speed and quality. The study was taken in the Serbian banking industry, with the focus on the perception and the viewpoint of CEOs and general managers of different departments. We used a two-phase survey to design the questionnaire and the correlation and regression analyses to examine our hypotheses. Our findings propose that, from managers’ perspective, human capital is critical to the success of banks, and that innovation speed is more influential than its quality. The backward multiple regression model shows that human capital and innovation speed account for 67.5% of the variability of the bank performance. The findings of this research can contribute to bank management policies by revealing how to enhance bank performance by focusing on human capital and innovation agility and readiness. The proposed research model could potentially be implemented in other sectors and industries to hopefully endorse the significance of the detected relationships.

Impact of the Economic Stimulus Measures on Lithuanian Real Estate Market under the Conditions of the COVID-19 Pandemic

The COVID-19 pandemic caused a number of challenges worldwide regarding not only the human health perspective, but also the economic situation. Quarantine, imposed in many countries, forced a substantial part of businesses to close or narrow down their activities, thus leaving corporations and employees without any or with lower income. If national governments had not undertaken any actions to save national economies, the consequences could have been even more devastating. The real estate market is an important part of economy. Instability in the real estate market can cause financial problems, vulnerability of population’s welfare and other negative effects. This research aims to assess the impact of the economic stimulus measures on the real estate market under the conditions of the COVID-19 pandemic in Lithuania. The research methods include comparative analysis, correlation analysis, stationarity test, regression analysis and the ARDL models. The results indicate that the economic stimulus measures only partially contribute to stabilization of the real estate market in Lithuania. The drop in housing prices was 2.9 percent lower because of the economic stimulus in the second quarter of 2020. Maintenance of household cash and deposits as well as lending to business enterprises are the measures that allow to stabilize the real estate market in the shortest time under the conditions of the economic shock. The other governmental support measures are also important, especially if they are aimed at preserving jobs.

E-banking Customer Satisfaction and Loyalty: Evidence from Serial Mediation through Modified E-S-QUAL Model and Second-Order PLS-SEM

This research aims to measure the service quality of e-banking through e-customer satisfaction & e-customer loyalty in the Islamic e-banking sector of Pakistan. For this purpose, we modified the multiple-item scale (E-S-QUAL) model with two new dimensions: availability of online system and personal needs of customers. Additionally, we added two mediators, for instance, religiosity and trust in the Islamic e-banking perspective. We have collected 674 responses from the consumers of the Islamic banking sector of Pakistan through a structured questionnaire. The data were analysed through second-order PLS-SEM (PLS-structural equation modeling) to examine direct and serial mediation. The direct relationship results showed a positive and compelling effect of modified E-S-QUAL dimensions on e-customer satisfaction. Additionally, the e-customer satisfaction has an affirmative and significant association with e-customer loyalty. Moreover, the serial mediation relationship results exhibited that the e-customer satisfaction, trust, and religiosity mediated the modified dimensions of the E-S-QUAL model and e-customer loyalty.

Environmental Management System towards Environmental Performance of Hotel Industry: Does Corporate Social Responsibility Authenticity Really Matter?

Environmental Management Initiatives (EMI), as part of the sustainability management movement, have become an integrated part of the organisational management practices and of the current research. Since the implementation of Environmental Management System (EMS), there have been many studies analysing this relationship with the environmental performance. Corporate Social Responsibility (CSR) also gained a great importance for the organisations’ performance, including their performance in the environmental area. However, previous studies analysed the direct link between those variables and environmental performance, while the mediating effect of those variables has not been tested yet because using a mediator in the relationship between two variables is a rather new approach in the research area used in the behavioural sciences area. This research analyses the role of EMI in defining the Environmental Performance (EP) of hotel industry, given the strong relationship between those two variables and the importance of the tourism sector for the economic development, both in the developed, but especially in the developing countries. It also investigates the CSR authenticity as mediator between EMI and EP link. Data was collected through a questionnaire of managers of the hotels in Pakistan. Correlation, Structural Equation Model and linear regressions were applied for testing the hypotheses and for checking the viability of the model. Findings revealed that EMI and CSR authenticity are important and significant determinants of EP in the hotel industry. Findings show that CSR authenticity acts as a mediator for the EMI and EP link. The stakeholder pressures and customers’ environmental awareness have forced the hotel industry to implement environmental standards and this shift of focus is more important in the hotel industry. The current research demonstrates that efforts of EMI is a prerequisite for enhancing CSR authenticity in the environmental area, and this, in its turn, contributes to the increase of the EP of hotel and tourism sector in a developing country. Given the lack of large financial resources of the developing countries, this model is an important outcome for the tourism industry that helps hotels to become green, to attract more clients and to gain competitive advantages.

Do Demographics Matter in Consumer Materialism?

This paper aims to investigate the effects of personal characteristics such as gender, income, education and age on consumer materialism. Research hypotheses are based on both existing marketing literature and an additional integration of gender studies literature in order to further the study of materialism. By using random sampling and surveying 1000 respondents living across different regions in Lithuania, the results show significant differences along gender, age, self-perceived relative income and education in terms of consumers’ materialistic dispositions. The study confirms that younger consumers are more inclined to materialistic values than older ones. Women differ from men on materialism centrality, as well as happiness and success measures. Regarding consumers’ self-perceived relative income, Lithuanians with low income are more prone to aspire to material possessions. Furthermore, research indicates higher rates of consumer materialism to appear among less educated individuals. This is one of the first representative studies in Lithuania revealing what effects different demographic consumer characteristics have on materialistic behaviour. Our findings have some practical implications; for example, they show that vulnerable segments of the population are more susceptible to materialism, necessitating educational policies to reduce such behavior and encourage a more responsible approach. As a result, these educational programs should be tailored to these individuals, with a greater emphasis on the risks associated with overconsumption.

Modelling of S&P 500 Index Price Based on U.S. Economic Indicators: Machine Learning Approach

In order to forecast stock prices based on economic indicators, many studies have been conducted using well-known statistical methods. Meanwhile, since ~2010 as the power of computers improved, new methods of machine learning began to be used. It would be interesting to know how those algorithms using a variety of mathematical and statistical methods, are able to predict the stock market. The purpose of this article is to model the monthly price of the S&P 500 index based on U.S. economic indicators using statistical, machine learning, deep learning approaches and finally compare metrics of those models. After the selection of indicators according to the data visualization, multicollinearity tests, statistical significance tests, 3 out of 27 indicators remained. The main finding of the research is that the authors improved the baseline statistical linear regression model by 19 percent using a ML Random Forest algorithm. In this way, model achieved accuracy 97.68% of prediction S&P 500 index.