INCOME TAX PREFERENCE AND R&D INVESTMENTS OF HIGH-TECH ENTERPRISES IN CHINA

This study explores the relationship between income tax preference and R&D investments of high-tech enterprises. This study selects listed high-tech enterprises in China from 2013 to 2018 as samples. The empirical results show that the effective income tax rate among high-tech enterprises in China differs widely. The findings suggest that high-tech enterprises in China have to take advantage of preferential income tax, pay more attention to R&D investments, and strive to improve R&D ability and market competitiveness. In addition, there is a significantly positive relationship between income tax preference and R&D investments of high-tech enterprises, indicating that the preferential tax rate policy and other tax incentives such as additional tax deduction increase R&D investments of high-tech enterprises effectively. State-owned enterprises (SOEs) are enterprises in which the state has ownership or control over its capital. The positive relation between income tax preference and R&D investments of hightech enterprises is more significant for non-SOEs. Non-SOEs have stronger governance efficiency. Therefore, SOEs should make better use of income tax preference and improve innovation enthusiasm. Moreover, this study finds a more positive relationship between income tax preference and R&D investments among high-tech enterprises in the introduction phase than in the growth and mature phases. However, the relation between income tax preference and R&D investments is insignificant for high-tech enterprises in the decline phase. The findings seem to provide a new perspective for the life cycle characteristics of enterprises and the theoretical guidance to enterprises in phases of growth, mature and decline to develop R&D investments better. Finally, loss enterprises or enterprises in geographical units with the innovative environment are eliminated in this study to avoid extra interference. The results remain robust, indicating that preferential income tax policies applied in high-tech enterprises are significantly and positively associated with R&D investments.

DEVELOPMENT AND STANDARDIZATION OF A TOOL TO MEASURE KNOWLEDGE OF LABOUR LAWS AMONG EMPLOYEES

Labour legislations provide guidelines to prevent unfair labour practices and organizational injustice. All employment and labour laws propose standards regarding wages, wellbeing, safety, and equitable treatment, which reduces conflicts. However, informational imbalances between employers and employees prevent employees from having adequate knowledge about labour legislation. Consequently, some employers treat employees inconsistently and that could be detrimental to them. A practical solution to this problem is making employees aware of the provisions of labour laws. As an initial step towards this, there is a definite need to assess employees’ awareness regarding labour laws. Nevertheless, there is a lack of a standardized tool to measure the knowledge level of labour legislation, which has impaired empirical examinations. Using randomly collected 346 samples from two different types of industries, the present study constructed and validated a tool to measure labour law knowledge. The study has followed a simple scale development framework consisting of item generation, scale development, and psychometric evaluation. The measurement tool has been developed using exploratory and confirmatory factor analysis. The final questionnaire consists of 17 items under four factors, exhibited significant reliability and validity. The developed tool is expected to fill the gap in the literature and stimulate further empirical research. The study also examined the difference in the knowledge level based on the demographics of the sample. The results show a significant difference between the knowledge levels based on gender, citizenship, type of industry, and qualifications. Future research directions are proposed at the end of the paper, together with the limitations of this study.

FINANCIAL ADVICE, LITERACY, INCLUSION AND RISK TOLERANCE: THE MODERATING EFFECT OF UNCERTAINTY AVOIDANCE

The aim of this paper is to investigate the determinants of financial advice with a special focus on the cultural role in the influence of risk tolerance on seeking advice for financial issues. Financial literacy is covered by financial attitude, behaviour and knowledge. Financial inclusion is the other factor considered in the conceptual framework, as an indicator which can enhance both financial behaviour and financial advice. The research is based on primary data collected in two European nations, manifesting differences in culture, which gives the possibility to test the uncertainty avoidance role in the above relationship. This particular focus is the novelty of this work, as it sheds light on the importance of culture while designing policies with the aim to enhance individuals’ financial literacy and advice. The hypotheses are tested by using Partial Least Square- Structural Equation Modelling (PLS-SEM) method. It was found that financial behaviour improves as financial inclusion gets better, along with financial attitude and knowledge. Furthermore, financial advice is positively influenced by financial inclusion and risk tolerance and partly by financial literacy. Additionally, findings demonstrate that culture does matter in explaining differences between countries. Culture in this paper is represented by uncertainty avoidance, as one of the Hofstede’s culture dimension. Individuals from countries that manifest a very high preference for avoiding uncertainty reflect a negative relationship between risk tolerance and financial advice. The paper offers useful insights for policymakers and industry leaders in understanding the most influential factors on financial advice. This enables them to scheme policies and services aimed at equipping citizens with knowledge and skills to make the best use of their financial resources.

THE IMPACT OF INTRADAY MOMENTUM ON STOCK RETURNS: EVIDENCE FROM S&P500 AND CSI300

This paper analyzes the statistical impact of COVID-19 on the S&P500 and the CSI300 intraday momentum. This study employs an empirical method, that is, the intraday momentum method used in this research. Also, the predictability of timing conditional strategies is also used here to predict the intraday momentum of stock returns. In addition, this study aims to estimate and forecast the coefficients in the stock market pandemic crisis through a robust standard error approach. The empirical findings indicate that the intraday market behavior an unusual balanced; the volatility and trading volume imbalance and the return trends are losing overwhelmingly. The consequence is that the first half-hour return will forecast the last half-hour return of the S&P500, but during the pandemic shock, the last half-hour of both stock markets will not have a significant impact on intraday momentum. Additionally, market timing strategy analysis is a significant factor in the stock market because it shows the perfect trading time, decides investment opportunities and which stocks will perform well on this day. Besides, we also found that when the volatility and volume of the S&P500 are both at a high level, the first half-hour has been a positive impact, while at the low level, the CSI300 has a negative impact on the last half-hour. In addition, this shows that the optimistic effect and positive outlook of the stockholders for the S&P500 is in the first half-hours after weekend on Monday morning because market open during the weekend holiday, and the mentality of every stockholder’s indicate the positive impression of the stock market.

STOCK PRICE PREDICTION USING MARKOV CHAINS ANALYSIS WITH VARYING STATE SPACE ON DATA FROM THE CZECH REPUBLIC

The article describes empirical research that deals with short-term stock price prediction. The aim of this study is to use this prediction to create successful business models. A business model that outperforms the stock market, represented by the Buy and Hold strategy, is considered to be successful. A stochastic model based on Markov chains analysis with varying state space is used for short-term stock price prediction. The varying state spate is defined based on multiples of the moving standard deviation. A total of 80 state space models were calculated for the moving standard deviation with 5-step lengths from 10 to 30 in combination with the standard deviation multiples from 0.5 to 2.0 with the step of 0.1. The efficiency of the business models was verified for 3 long-term, liquid stocks of the Czech stock market, namely the stocks of KB, CEZ, and O2 within a 14-year period – from the beginning of 2006 to the end of 2019. Business models perform best when they use a state space defined on the length of a moving standard deviation between 15 and 30 in combination with multiples of the standard deviation between 1.1 and 1.2. Business models based on these parameters outperform the passive Buy and Hold strategy. In fact, they outperform the Buy and Hold strategy for both the entire period under review and the yielded five-year periods (including transaction fees). The only exception is the five-year periods covering 2015 for O2 stocks. After the end of the uncertainty period caused by unclear intentions of the new majority stockholder, the stock price rose sharply. These results are in conflict with the efficient markets theory and suggest that in the period under review, the Czech stock market was not effective in any form.

IMPRESSION MANAGEMENT STRATEGY OF COPING WITH ENTREPRENEURIAL FAILURE STIGMA: A TWO-PATH THEORETICAL MODEL

Impression management strategy is an important way to cope with the stigma of failed entrepreneurial firms. However, most existing studies only focused on the process of impression management with a single strategy. Few studies have provided a systematic theoretical explanation on how to use different types of impression management strategies to cope with stigma. To fill this theoretical gap, a two-path model of impression management of entrepreneurial failure stigma was constructed, based on the two-component model of impression management. In addition, the mechanism of impression management strategy selection for failed entrepreneurial firms to cope with stigma was discussed. The findings of the theoretical model reveal two paths for the stigma management strategy of failed entrepreneurial firms: “avoidance motivation → defensive strategy of impression management” and “diluted motivation → acquisitive strategy of impression management.” Moreover, in the selection mechanism of strategy, the formation of impression motivation is affected by the stigma type of entrepreneurial failure, the social status of the firm organization, and the degree of stigma threat. In the face of justifiable stigma, the failed entrepreneurial firms form the avoidance motivation and then implement a defensive strategy of impression management. High social status firms adopt an acquisitive strategy of impression management to cope with the negative impact of entrepreneurial failure stigma. As the threat level of entrepreneurial failure stigma increases, the dilution motivation of the failed entrepreneurial firms to stigma becomes stronger, and the firms are more likely to adopt the acquisitive strategy of impression management. The two-path theoretical model provides decision support for failed entrepreneurial firms to formulate stigma management strategies and expands the research scope of entrepreneurial failure stigma.

THE DEFENSE OF PRICE DISCRIMINATION IN NETWORK AND INFORMATION GOODS MARKETS

It is not uncommon that articles focused on consumer-price interaction in the network and information goods market swiftly condemn price discrimination as an obfuscation, on-purpose price complexity, or market failure. The reason is a general neoclassical rule of an efficient market where prices are set at marginal cost with no price discrimination. However, the matter is more complicated. This review provides authors an overview of why, where, and which type of price discrimination should be viewed by different optics. Goods such as software, cell carrier services, electronic newspapers subscription, electric energy supply, payment accounts, books, copyrighted content streaming, etc, cannot be treated like manufactured goods. The reasons are specific conditions – substantial and/or repeated fixed/sunk cost, economies of scale, and demand heterogeneity. Recognized economist W. J. Baumol described marginal cost set prices under these conditions as an ‘economic suicide’. Reviewed articles showed that firms are forced to adopt price discrimination in order to recover their costs and to serve more consumer segments. Reviewed authors provided facts to support the use of multipart tariffs, dynamic pricing, versioning, bundling, and Ramsey pricing. These conclusions are used for suggestions on how several studies of information and network goods should be modified. Modifications are related mostly to model assumptions and pricing conclusions. I argue that, in the case of information and network goods, there is justified price discrimination. Hence, there is a certain justified level of price complexity that has to be accepted and not taken as automated evidence of inefficiency, market power, and consumer exploitation.

INTEGRATED APPROACH OF FUZZY MULTI-ATTRIBUTE DECISION MAKING AND DATA MINING FOR CUSTOMER SEGMENTATION

This research work focuses on integrating the multi attribute decision making with data mining in a fuzzy decision environment for customer relationship management. The main objective is to analyse the relation between multi attribute decision making and data mining considering a complex problem of ordering customers segments, which is based on four criteria of customer’s life time value, viz. length (L), recency (R), frequency (F) and monetary value (M). The proposed integrated approach involves fuzzy C-means (FCM) cluster analysis as data mining tool. The experiment conducted using MATLAB 12.0 for identifying eight clusters of customers. The two multi attribute decision making tools i.e., fuzzy AHP (Analytic Hierarchy Process) and fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) are used for ranking these identified clusters. The applicability of the integrated decision making technique is also demonstrated in this paper considering the case of Indian retail sector. This research collected responses from nine experts from Indian retail industry regarding their perception of relative importance of four criteria of customer life value and evaluated weights of each criterion using fuzzy AHP. Transaction data of 18 months of the case retail store was analysed to segment 1,600 customers into eight clusters using fuzzy c-means clustering analysis technique. Finally, these eight clusters were ranked using fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The findings of this research could be helpful for firms in identifying the more valuable customers for them and allocate more resources to satisfy them. The findings will be also helpful in developing different loyalty program strategies for customers of different clusters.

URBAN MAGNETISM IN THE GLOBAL CITY FRAMEWORK: EXPLORING THE LINK BETWEEN URBAN FUNCTIONS AND POPULATION GROWTH

Dynamics in global processes have led to a number of political, economic and cultural changes that have resulted in the emergence of global cities. In the hierarchy of global cities, those who successfully use the limited available resources and offer an adaptable and flexible living environment, represent the most competitive global cities. Nonetheless, accelerated globalization has conditioned direct competition of global cities for different resources, and one of the most desirable being highly-skilled, talented and creative residents. In such conditions, city leaders face the need to understand the concept and factors of urban magnetism. The analysis of the determinants of urban magnetism can facilitate the formulation of concrete actions aimed at increasing the attractiveness of the city, which ultimately leads to the preservation of long-term socio-economic development of cities. Although financial wellbeing and incentives were considered to be a key factor of urban magnetism, contemporary research approaches have emphasized other factors that may influence urban attractiveness. Starting from that point of view, this paper aims to identify the main urban functions that influence the cities’ size with an analysis focusing on global cities worldwide. The panel regression analysis is applied to the sample of 39 global cities over the period from 2013 until 2019 and the data on urban functions are obtained from the Global Power City Index (GPCI). The results indicate that the size of the global cities population is positively affected by urban functions related to the cultural interaction and accessibility, while research and development function influences negatively on the size of the urban population. Results of the paper led to the conclusion that contemporary urban management strategies need to be defined and implemented aimed at improving the urban magnetism beyond economic performance of the city, focusing on sustainability and urban quality of life.

TOWARDS AN EFFICIENT APPROACH FOR IDENTIFICATION AND SELECTION OF STAKEHOLDER ENGAGEMENT STRATEGIES: A CASE STUDY

The goals and objectives of a project as well as the needs, requirements and expectations of the project stakeholders may contradict or non-fulfillment of them may have different detrimental and negative consequences for the project. Therefore, project stakeholders should be effectively managed, but it is not possible to satisfy all project stakeholders and meet all of their expectations and requirements. As a result, project team must strike a balance between the project goals and objectives and the needs, requirements and expectations of the project stakeholders in order to complete the project successfully. Despite highlighting the significant importance of project stakeholder management, there exists a notable gap in exerting an effective decision support system to adopt stakeholder engagement strategies particularly in oil and gas construction projects. This study proposes a comprehensive framework for the identification, prioritization and selection of the stakeholder engagement strategies in one of the large size oil and gas construction projects in Iran. In this paper, a hybrid method which is the combination of the SWOT (strengths, weakness, opportunities and threat) analysis and fuzzy Delphi method is first exploited for identifying the appropriate stakeholder engagement strategies. Subsequently, fuzzy SWARA (Step-wise Weight Assessment Ratio Analysis) is employed to weight the crucial criteria, and finally, fuzzy WASPAS (Weighted Aggregated Sum Product Assessment) is utilized to prioritize the identified stakeholder engagement strategies. This research contributes to the body of knowledge on project stakeholder management by presenting a novel framework for identifying, ranking and selecting the suitable strategies for effective stakeholder engagement considering one of the largest oil and gas construction projects in the country. The value of this study is in applicability of the proposed methodology for project managers and practitioners in other oil and gas construction projects.