You have a product or service for which the world has been waiting. You've scraped together enough cash to get your idea off the ground. You are confident that your business will succeed. And yet, it is often the case that a short time later you are out of business. Numbers from the U.S. Bureau of Labor Statistics indicate that about half of new businesses fail within the first five years. And with those business failures go the life savings and the dreams of the person who started the business. This article describes five finance and accounting pitfalls that should be avoided in order to increase the likelihood that a small business will succeed. Of course, the most common reason for a small business to fail is that customers don't want the product or service. But small businesses are also often killed because of predictable and preventable finance and accounting mistakes: insufficient capital, poor cash management, poor record keeping and controls, improper product pricing, and uncontrolled growth.

In this paper, we develop a model in which a monopolistic firm manufactures and sells a digital product, by incorporating digital rights management (DRM), quality degradation of pirated products, and government copyright enforcement into the consumer’s utility function. We determine the monopolist’s optimal pricing strategies and the appropriate DRM protection level through mathematical deduction. Our results show that when the government copyright enforcement is moderate and the quality of pirated products is relatively high, implementing a DRM system is optimal for the monopolist. However, in most other cases, DRM-free is better for the monopolist. This result may explain why DRM is not very popular in some industries. Our results suggest that choosing the right price, focusing on content innovation, and weakening DRM protection may be a better strategy for firms now. The results also indicate that DRM-free may be more prominent in the digital music industry than in the software and video games industries.

Technological advancements in the last few decades have created energy and cost-efficient power plants, but it is not a prominent feature in the electricity supply-chain. While many long-term and short-term energy alternatives are available, Pakistan still has a significant number of people who do not have access to electricity; as there are many areas that are neither connected to the national grid nor the provincial grids. This paper aims to find the reasons behind the energy crisis in Pakistan, keeping Islamabad as the focus of study, and to find the effect of use of renewable resources on the reduction of shortage of electricity. We will be focusing on the facts that how energy crisis has been affecting the economic growth of the country.  Our data collection will be based on primary sources and secondary sources along with which we will be conducting pilot study to check the feasibility of using the solar energy as an alternative form of electricity.

The digital economy industry plays a transformative role in reshaping the global production networks, the local division of production, and the integration and innovation of business forms. However, to what extent the industry enables the high-quality development and upgrading of urban economies is unclear. Based on the census data of digital economy enterprises in Hangzhou in 2008, 2013 and 2018, this paper verifies the characteristics of the digital economy industry and its enabling mechanism for high-quality urban economic development. It is found that: firstly, the digital economy industry has three main forms for the economy, namely, the sharing, scale and scope economy. Secondly, the industry has four major mechanisms to improve the quality of urban economies. That is, it can restructure the global industrial division of labor and value chains, promote the transformation of scale economy from the supply side to the demand side, enable the softening and servitization of industrial structure, and generate the scale of economies. This paper thus contributes to understanding the evolution of enterprise organizational structure and industrial competitiveness and the endogenous technological change under the digital economy.

The precise prediction of carbon emissions trading prices is the foundation for the stable and sustainable development of the carbon financial market. In recent years, influenced by a combination of factors such as the pandemic, trading regulations, and policies, carbon prices have exhibited strong random volatility and clear non-stationary characteristics. Traditional single-perspective prediction methods based on conventional statistical models are increasingly inadequate due to the homogenization of features and are struggling to adapt to China's regional carbon emissions trading market. Therefore, this paper proposes a multi-perspective fusion-based prediction method tailored to the Chinese market. It leverages carbon emissions trading information from key cities as relevant features to predict the price changes in individual cities. Inspired by the development of artificial intelligence, this paper implements various time series models based on deep neural networks. The effectiveness of the multi-perspective approach is validated through multiple metrics. It provides scientific decision-making tools for domestic carbon emissions trading investors, making a significant contribution to strengthening carbon market risk management and promoting the establishment and rational development of a unified carbon market in China.

Credit scoring remains one of the most important subjects in financial risk management. Although the methods in this field have grown in sophistication, further improvements are necessary. These advances could translate in major gains for financial institutions and other companies that extend credit by diminishing the potential for losses in this process. This research seeks to compare statistical and artificial intelligence (AI) predictors in a credit risk analysis setting, namely the discriminant analysis, the logistic regression (LR), the artificial neural networks (ANNs), and the random forests. In order to perform this comparison, these methods are used to predict the default risk for a sample of companies that engage in trade credit. Pre-processing procedures are established, namely in the form of a proper sampling technique to assure the balance of the sample. Additionally, multicollinearity in the dataset is assessed via an analysis of the variance inflation factors (VIFs), and the presence of multivariate outliers is investigated with an algorithm based on robust Mahalanobis distances (MDs). After seeking the most beneficial architectures and/or settings for each predictor category, the final models are then compared in terms of several relevant key performance indicators (KPIs). The benchmarking analysis revealed that the artificial intelligence methods outperformed the statistical approaches.

Green innovation, as a sustainable development approach, can synergistically promote high-quality economic and social development. We select China's A-share-listed manufacturing corporations from 2011 to 2021 as the research sample to empirically study the impact of executive academic experience on green innovation in manufacturing corporations. Further, we test the relationship between executive academic experience and green innovation in manufacturing corporations under the mediating role of corporate social responsibility and the moderating role of heterogeneous environmental regulations. The study shows that (1) executive academic experience helps to promote green innovation in manufacturing corporations; (2) executive academic experience is conducive to promoting the fulfillment of social responsibility by manufacturing corporations; (3) corporate social responsibility plays a mediating role between executive academic experience and green innovation in manufacturing corporations; (4) environmental regulation positively moderates the relationship between executive academic experience and green innovation in manufacturing corporations, and both formal environmental regulation and informal environmental regulation positively moderated the promotion of green innovation in manufacturing corporations by executive academic experience.

This study examines the role of news sentiments in the GCC equity markets’ connectedness. We collected news titles for the period from 22nd June 2006 until 31st December 2020 from Gulf News, which is the most widely read English newspaper in the Arab World. We filter these news titles using a carefully designed list of keywords that capture public sentiment on matters related to financial markets. Next, we classify the news titles to compute the geographically distinguished sentiment indexes that allow for a detailed analysis of the source of news sentiment spillovers to compare the impact of domestic versus regional sentiments on the equity markets of GCC countries. Our quantile regression results reveal that equity markets in the GCC are most sensitive to news sentiments when underperforming. Moreover, our results from the connectedness approach suggest that the UAE equity markets are most influenced by domestic sentiments, whilst the KSA equity market is most influenced by regional sentiments from the other GCC countries. Mixed results are found for other countries. The time-varying component of this study also shows that the influence of news spillovers intensified during the major crises events, including the COVID-19 outbreak.

This paper uses China Household Income Project rural household survey data spanning 30 years (1988-2018) to study the impact of China’s industrialization on rural household income. In the semiparametric regression model, we use the rural households’ distance to the manufacturing hub to analyze the spillover effect of industrialization on their income and the spatial attenuation. The results show that the income gap between rural households in Guangdong and other provinces increased from 1988 to 2002, but continuously decreased from 2002 to 2018; the rural households’ distance to the Pearl River Delta or the Yangtze River Delta, the two international manufacturing hubs, has a significant impact on their income, which has been increasing from 1988 to 1995, but has been decreasing since the new century. On the contrary, the influence of the rural households’ distance to the provincial capital on their income has been increasing since the new century.

Generating output growth by adding more inputs into the production process may not be sustainable in the long run for any economy, given the limited resources. On the other hand, if productivity growth dominates the production process, it will generate more output without excessive increase in input use. Hence, this paper examines whether the output growth in Indonesia’s manufacturing sector is excessive inputs driven or productivity driven. Productivity driven growth is measured by Total Factor Productivity (TFP) growth, which is decomposed into its major components of technological progress and technical efficiency within the framework of varying coefficients stochastic frontier analysis (VSFA) using Indonesia’s annual Large and Medium Manufacturing Industries Survey data over the period 2002–2014. The measurement of the components of TFP growth not only provides more insights and better understanding of the dynamic nature of the production processes, but also has important policy implications. The mean TFP growth during the period 2002-2014 was estimated to be 4.3 per cent and was mostly contributed by technological progress experienced by firms. The policy implication is that technical efficiency could still be improved for the selected technology to reap the full benefit of increasing output from the chosen technology.

The Human Development Index (HDI) aims to present a more robust picture of a country’s development status than that suggested by national income per capita. The HDI aggregates dimension indices based on transformed measures for the core values of health, education, and income per capita. Assumptions are made regarding the upper and lower bounds used to re-scale each core value into its dimension index, as well as the functional form used to create the HDI. The treatment of income in the HDI’s construction suggests incompatibilities with its underlying capability theory, which stresses the importance of individuals’ abilities to make their own consumption and life choices. We examine the currently formulated HDI, as well as two influential proposed alternatives which generalize the aggregation functions of the pre-2010 HDI formulation and the current HDI, in light of recent empirical research into the relationship between well-being and income. We use underlying data for 2016, a representative year, to examine distributional changes as well as specific country rankings. We find that the income bounds used to calculate the income dimension index in the HDI should be changed. We also suggest that the three aggregation formulations, along with prominent descriptions of the assumptions and consequent implications of each approach, should be disseminated for policymakers and the public to consider.

The vigorous development of the digital economy provides a new research perspective for alleviating local financial pressure. Based on the provincial panel data from 2013 to 2020, the mediating effect model and threshold regression model were used to explore the effect and mechanism of the digital economy on local fiscal pressure. The research shows that the digital economy has a significant alleviating effect on local financial pressure by increasing fiscal revenue, and this alleviating effect has a threshold effect on entrepreneurial activity. From the perspective of dimension, the alleviating effect of the digital economy development environment on local financial pressure is better than that of digital industrialization and industrial digitization. From a regional perspective, the digital economy has the most apparent impact on the western region. In addition, the quantile regression results show that with the increase in financial pressure, the easing effect of the digital economy shows an inverted U-shaped change. Therefore, in order to effectively alleviate the financial pressure on local governments, China should continue to promote the development of the digital economy, take multiple measures to increase entrepreneurial activity, and encourage various regions to implement differentiated digital economy development strategies according to local conditions.

Based on provincial panel data from 1998-2018, this paper estimates research and development (R&D) factors, and a stochastic frontier analysis (SFA) model is constructed to examine the effects of R&D factors on regional total factor productivity (TFP). The results show that both R&D capital stock and R&D personnel can significantly promote regional TFP, but the productivity-enhancing effect of R&D factors is different between regions. Specifically, R&D capital and R&D personnel can promote TFP in eastern and central provinces, and the promotion effect is not significant in western provinces. In addition, compared with investment-driven regions, innovation-driven regions are more likely to enhance TFP by R&D factors.

Economic globalization has intensified economic volatility around the world, and the importance of economic resilience has become increasingly prominent in the face of rising uncertainty due to frequent domestic and foreign shocks. Based on provincial panel data in China from 2008-2019, a comprehensive indicator system is constructed to measure regional economic resilience (RER), and the impact of financial driver (FD) on RER is explored from staged, pathway and non-linear perspectives. The main findings are as follows. (1) Both national and regional levels of FD and RER show an upward trend, and the impact of FD on RER has an “inverted-U” shape: as the capacity of FD increases, its impact on regional economic resilience shifts from promoting to inhibiting. The RER has significant spatial agglomeration characteristic. (2) Interestingly, staged analysis shows FD can significantly promote RER only when it is in the “recovery stage”. (3) The pathway analysis of its effect on regional economic resilience shows the following characteristics: financial scale > financial efficiency > financial deepening. (4) From different regions, there are significant differences in the effect of FD on the promotion of regional economic resilience: central region > western region > eastern region. Finally, the corresponding suggestions were provided.

This research letter investigates hedonic and utilitarian orientations to Online shopping. The data highlight a positive correlation of hedonism with both the length and the frequency of connection to the Internet. It considers two products, jeans and personal computers, that previous literature had identified as being characterized by utilitarian and hedonic features. The results show that consumers prefer to buy products and brands on the Web if they have already seen and experienced them Offline.

Human factor inputs are key to promoting agricultural modernisation. Traditional economic theory suggests that improving human capital is the key to boosting agricultural productivity, but can this conclusion hold in China's dualistic economic structure? We use Chinese provincial panel data from 2000-2017 to examine the impact of rural human capital inputs on agricultural total factor productivity. We find that, on the whole, rural human capital inputs have a negative effect on agricultural total factor productivity, and that there is a "rural human capital trap". Mechanism analysis reveals that rural human capital inputs, on the one hand, cause labour loss and reduce the quality of agricultural workers, while on the other hand, they may promote the application of mechanization, which has both positive and negative effects on agricultural total factor productivity. Heterogeneity analysis finds that this negative effect is more pronounced in the central region of China because of the serious loss of rural labour. This study provides new policy insights for further improving the structure of rural education inputs and promoting human capital accumulation in agriculture.

With the advancement of science and technology and the advent of the digital economy, the digital economy has become a new driving force for economic development, and the digital economy industry at home and abroad is facing new opportunities for survival and development. However, due to the special development model and profit mode of the digital economy industry, the traditional performance evaluation method is no longer applicable, so it is particularly important to improve the previous performance evaluation method. This paper first adjusts the customer value assessment (CLV) model according to the characteristics of the digital economy industry, and then analyzes the traditional EVA based on the adjusted user value assessment (ULV) model of the digital economy industry. The performance evaluation system is improved, and the representative enterprises of Alibaba, JD and Pinduoduo in the digital economy industry are selected for performance evaluation and analysis, and the user value performance evaluation model of the digital economy industry is integrated, innovated and standardized, in order to provide reference and reference for the follow-up research and practice of performance evaluation in the digital economy industry.

Green innovation, driven by China's new development concept, plays a crucial role in high-quality economic development. In line with the green development trend, businesses increasingly prioritize whether their corporate environmental responsibilities (CER) can effectively enhance corporate green innovation (CGI) levels. This paper examines the influence and mechanism of CER on CGI using a dynamic perspective, drawing from 1,640 manually-collected panel data of Shanghai and Shenzhen A-share listed companies between 2010 and 2017. The primary findings indicate that the impact of CER on CGI possesses phase-specific characteristics and a dual effect of "crowding in" and "crowding out." The current phase of CER negatively affects green innovation, while the lag phase has a positive effect. CER's impact on various CGI types is heterogeneous: specifically, it follows an "inverted-N" trajectory (inhibition-promotion-inhibition) for "strategic green innovation" and has a promotional effect on "substantive green innovation," which is stronger and has a longer time lag. The mechanism analysis reveals that financing constraints play a critical mediating role. A heterogeneity analysis based on multiple dimensions (ownership, industry, and location) suggests that CER has a more significant driving force for CGI among state-owned firms, high-polluting industries, and enterprises in inland areas. Finally, the paper presents corresponding suggestions for government and corporate entities.

All things being equal, and in a truly democratic world, one will expect that the fact that women are the majority in almost every single community of the world should guarantee them at least equal control or ownership over resources and enjoy most of the rights and privileges in that particular community. But things seem not to be working that way as evidence from the literature shows that women are always relegated to the background when it comes to the issue of control or ownership over, and sometimes even access to property in most societies which include the right to own and use land. This undoubtedly has a consequence on the productive and overall development of such a community. In recent times, the issue of gender discrimination, disparities, and inequalities has attracted the attention of researchers over the past decade with enormous literature in this area. There are, however, few studies that provide a comparative analysis of the situation between two or more countries. This paper seeks to fill this void of knowledge based on a review of relevant literature on land rights with an emphasis on the situations in Kenya and Tanzania. The results have shown the prevalence of four types of land rights such as communal property rights, non or open-access property rights, state or crown property rights, and private property rights. The results have further indicated that the controversial land rights of Kenya have led to the reduction in the lands of certain groups of people, increasing land disputes, and also discriminating against women in their land rights. In the case of Tanzania, there were several cases of bias against women concerning the ownership of land, especially under the customary system of Tanzania. Issues of discrimination against women were also identified in the inheritance laws of the land which is a paramount feature within the norms and cultures of the people of Tanzania. The lack of equal access to land by women in both countries is found to harm productivity in several sectors of their economies, especially in agriculture. Policymakers are therefore admonished to ensure equal access by both genders to land as this will lead to efficiency in the management and usage of land which is certainly good for sustainability in the development of any economy.

This paper aims to study and quantify the impact of agricultural exports on economic growth along with other variables in Tunisia. For that purpose, we estimate the relationship between GDP, agricultural exports, other exports, and imports by applying the ARDL approach under the period 1990-2020. The results show a negative and insignificant impact of agricultural exports on economic growth in the long and short run. Other exports negatively and significantly impact economic growth, while imports are found to have a positive impact on long-term economic growth. The findings have economic implications for sectors and activities that should be promoted to accelerate economic growth.