Industry Characteristics Research Articles

The Impact of Sustainability Practices on the Financial Performance of Listed Companies in Saudi Arabia

This research explores the relationship between sustainability and financial performance of Saudi listed firms. Using a mixed-methods approach, it examines financial data and sustainability indicators from various industries. Initial findings show a positive impact of sustainability on financial performance, suggesting that organizations adopting sustainable practices tend to have improved financial outcomes. The study also explores the underlying mechanisms driving this relationship, including corporate governance, stakeholder engagement, innovation, and strategic alignment. It also considers contextual factors like industry characteristics, geographical location, regulatory frameworks, and firm size. The findings have implications for academia and practitioners, providing empirical evidence of the benefits and challenges of sustainability practices. The research aims to provide a holistic perspective on guiding organizations towards more sustainable and financially resilient futures.

Analytical framework for industry-oriented leading cyclical indicators: Empirical investigation of EU economic cycles

AbstractThis research developed an analytical framework for industry-oriented leading cyclical indicators (CII), focusing on monitoring and forecasting economic cycles within the European Union (EU). Various methodologies for constructing these indicators were examined through an exhaustive sector analysis. A salient conclusion drawn is the non-feasibility of a one-size-fits-all composite leading indicator for all EU members. It underscores the imperative to tailor these indicators in congruence with the unique industrial characteristics of each country. The study provides empirical evidence that countries like Denmark, Germany, Austria, Estonia, Lithuania, Latvia, Finland and Sweden can benefit from high-caliber composite leading indicators tailored to their economies. Our analysis suggests that GDP is a more robust metric than the Industrial Production Index for predicting economic cycles for the EU countries.

Analysis of China Eastern Airlines Financial Statements Based on Harvard Analysis Framework

The financial analysis method adopted in this paper refers to the analysis method in the "Harvard Analytical Framework System". Through the business strategy of the enterprise, the accounting policies adopted, the analysis of the company's finances and the analysis of the company's prospects, etc. The business situation, financial situation and development situation were analyzed and elaborated.This paper takes China Eastern Airlines' financial statements as an example, combines the characteristics of the air transport industry, analyzes the company's financial status, operating results and cash flow, and systematically evaluates the company's past and present operating results and financial status, and judges the company accordingly investment value.

Comparative Analysis of Key Success Factors in S&OP: Focusing on Manufacturing and Retail Industry in South Korea

The global supply chain is facing unprecedented challenges due to pandemics, geopolitical tensions, volatile commodity prices, and rapid changes in consumer demand. The Sales and Operations Planning (S&OP) process can be an important solution by integrating supply and demand with business strategy and operational planning. However, previous studies have developed single frameworks or maturity models without considering industry-specific factors, overlooking the structural and operational characteristics of different industries that can have a significant impact on the approach and success factors of S&OP. Therefore, this study identifies and compares the key success factors of S&OP in the manufacturing and retail industries using the Analytic Hierarchy Process (AHP) technique. The results show clear differences in the prioritization of success factors between two industries. In the manufacturing industry, strategic support and leadership were evaluated as the most critical factor, while in the retail industry, operational processes were emphasized. In addition, the relative importance was concentrated on the top-ranked factor in the manufacturing industry, but the relative importance was distributed among several factors in the retail industry. This study provides valuable insights into the critical success factors for S&OP implementation in different industries and offers an understanding of how industry-specific characteristics influence the effectiveness of S&OP strategies.

‘On the cusp of something huge’: Anticipatory subjectivities in freelance fashion work

This article draws on an (auto)ethnographic study of a group of freelance fashion professionals, known as ‘fashion agents’, with a particular focus on their relationship to time. It aims to elucidate the temporalities that permeate their work lives and shape their subjectivities, which I coin as anticipatory subjectivities. Such subjectivities, I posit, are emblematic of the conditions of flexibilisation, precarity and hopeful investment in the future characteristic of contemporary cultural industries and, more broadly, late capitalist knowledge economies. I propose that agents’ life-worlds are permeated by a promissory regime – i.e., living in anticipation of constantly deferred, adjourned and unsecured rewards. Such a regime produces subjectivities that are premised on a hopeful emotional investment in the future, coupled with a preparedness for, and a capacity to navigate, incalculable risks. Another feature of the ‘timescapes’ that agents have to negotiate is the simultaneity of multiple fluid, unstructured and often conflicting temporalities and tempos, which requires a particular kind of time-competence. Finally, the article also foregrounds the importance, in agents’ temporal orientations, of kairos: the right, opportune moment. Such a moment is normally perceived as being just about to happen; yet, it cannot be orchestrated or precipitated. As a result, one of agents’ key temporal experiences is that of waiting, which de-subjectivises them and inhibits their capacity to act. Together, these time-orientations – anticipation, managing multiple temporalities and a hopeful waiting for kairos – underpin and foster subjectivities and forms of affective labour that sustain the cultural industries; they are also emblematic of the temporal conditions of late capitalism.

Research on the adjustable range of air conditioner setting temperature considering user comfort

Abstract Traditional air conditioner (AC) temperature control typically sets temperature goals or establishes a uniform temperature range without considering industrial characteristics and user comfort preferences. As a result, users cannot achieve differentiated and precise control. This paper proposes a data-driven approach to predict the range of AC set temperatures under the constraint of user comfort. Firstly, a random forest feature importance screening method is employed to extract key factors influencing AC set temperatures. Secondly, a convolutional neural network (CNN) model is constructed to estimate AC set temperatures for five typical user categories. Finally, based on the Predicted Mean Vote (PMV) model, user thermal comfort constraints are established to calculate the flexible adjustment range of AC for users. Through simulation analysis using real AC data from a certain region in Eastern China, the proposed standard for estimating user air conditioner set temperatures in this paper ensures a root mean square error not exceeding 0.5 and an average absolute percentage error not surpassing 1.8%. The obtained air conditioner adjustment ranges reflect the characteristics of users in different industries, meeting engineering requirements.

The Impact of Industrial Characteristics on the Capital Structure

This paper investigates the relationship between industrial characteristics and capital structure. The author selected three industries with totally different features as examples and discussed how their particular industrial functions can make a significant difference in leverage ratio. These industrial characteristics are only related to the industry, such as business model and financial statements structure, regardless of the firms size, business performance, and profitability, which can be calculated and measured. Using high-tech-driven, asset-intensive, and labor-intensive industries as samples to analyze their influences on firms leverage ratios. Their industry characteristics can explain the difference in leverage, as asset-intensive companies have the highest average level of debt while labor-intensive has the lowest one. However, the industries cannot account for all diversities in capital structure, and there are difficulties in discovering the perfect capital structure that can meet all requirements and has the lowest cost under current literature. It is necessary to ascertain all factors that will affect the capital structure and evaluate the extent of their influences in future research.

Wide-range resistivity characterization of semiconductors with terahertz time-domain spectroscopy

Resistivity is one of the most important characteristics in the semiconductor industry. The most common way to measure resistivity is the four-point probe method, which requires physical contact with the material under test. Terahertz time domain spectroscopy, a fast and non-destructive measurement method, is already well established in the characterization of dielectrics. In this work, we demonstrate the potential of two Drude model-based approaches to extract resistivity values from terahertz time-domain spectroscopy measurements of silicon in a wide range from about 10−3 Ωcm to 102 Ωcm. One method is an analytical approach and the other is an optimization approach. Four-point probe measurements are used as a reference. In addition, the spatial resistivity distribution is imaged by X-Y scanning of the samples to detect inhomogeneities in the doping distribution.

On the robust drivers of cryptocurrency liquidity: the case of Bitcoin

This study aims to identify the factors that robustly contribute to Bitcoin liquidity, employing a rich range of potential determinants that represent unique characteristics of the cryptocurrency industry, investor attention, macroeconomic fundamentals, and global stress and uncertainty. To construct liquidity metrics, we compile 60-min high-frequency data on the low, high, opening, and closing exchange rates of Bitcoin against the US dollar. Our empirical investigation is based on the extreme bounds analysis (EBA), which can resolve model uncertainty issues. The results of Leamer’s version of the EBA suggest that the realized volatility of Bitcoin is the sole variable relevant to explaining liquidity. With the Sala-i-Martin’s variant of EBA, however, four more variables, (viz. Bitcoin’s negative returns, trading volume, hash rates, and Google search volume) are also labeled as robust determinants. Accordingly, our evidence confirms that Bitcoin-specific factors and developments, rather than global macroeconomic and financial variables, matter for explaining its liquidity. The findings are largely insensitive to our proxy of liquidity and to the estimation method used.

Analysis of the Driving Mechanism of Urban Carbon Emission Correlation Network in Shandong Province Based on TERGM

Analyzing the driving factors and mechanisms of urban carbon emission correlation networks can provide effective carbon reduction decision-making support for Shandong Province and other regions with similar industrial characteristics. Based on industrial carbon emission data from various cities in Shandong Province from 2013 to 2021, the spatial correlation network of carbon emission was established by using a modified gravity model. The characteristics of the network were explored by using the Social Network Analysis (SNA) method, and significant factors affecting the network were identified through Quadratic Assignment Procedure (QAP) correlation analysis and motif analysis. The driving mechanism of the carbon emission correlation network was analyzed by using Temporal Exponential Random Graph Models (TERGMs). The results show that: (1) The spatial correlation network of urban carbon emission in Shandong Province exhibits multi-threaded complex network correlations with a relatively stable structure, overcoming geographical distance limitations. (2) Qingdao, Jinan, and Rizhao have high degree centrality, betweenness centrality, and closeness centrality in the network, with Qingdao and Jinan being relatively central. (3) Shandong Province can be spatially clustered into four regions, each with distinct roles, displaying a certain “neighboring clustering” phenomenon. (4) Endogenous network structures such as Mutual, Ctriple, and Gwesp significantly impact the formation and evolution of the network, while Twopath does not show the expected impact; FDI can promote the generation of carbon emission reception relationships in the spatial correlation network; IR can promote the generation of carbon emission spillover relationships in the spatial correlation network; GS, differences in GDP, differences in EI, and similarities of IR can promote the generation of organic correlations within the network; on the temporal level, the spatial correlation network of urban carbon emission in Shandong Province has shown significant stability during the study period.

Exploring the Successful Factors of Brand Collaborations Between KFC and Genshin Impact: A Case Study Analysis

In a post-pandemic era marked by a decline in consumer purchasing power, particularly in the restaurant industry, this paper delves into the successful collaboration between the international fast-food chain KFC and the popular open-world role-playing game, Genshin Impact. This collaboration, which occurred in 2021, generated extensive buzz and recognition on social media platforms. By analyzing this case, we explore the effective marketing strategies and communication methods that led to the success of this co-branding venture. The study uncovers the significance of cross-domain collaborations, emphasizes the impact of consumer groups, and delves into the role of social media and the fan effect in marketing strategies. It also sheds light on the social comparison effect, pivotal in influencing players' motivations and purchasing behavior. Furthermore, it examines KFC's hunger marketing approach and the integration of fast-food industry characteristics. The paper concludes with suggestions for addressing supply chain management, resource optimization, and service quality in co-branding campaigns. Despite these valuable insights, the study acknowledges certain limitations and points towards future research directions.

How do nonsubsidized incentive affect enterprises' innovation choices? A case from the new energy vehicle industry in China

Faced with the mainstream trend of subsidy reduction, how to effectively guide NEV enterprises to innovate through nonsubsidized incentive is a pressing issue to be addressed in the transformation process of China’s NEV industry. Based on the distinctive characteristics of China's NEV industry, this study proposed corresponding theoretical hypotheses combined with the smile curve theory. Propensity score matching method was used to verify the influence and mechanism of nonsubsidized incentive on the innovation choices of NEV enterprises in segments of production and consumption. The results indicate that nonsubsidized incentive in the production segment stimulates the product innovation of NEV enterprises, especially those with political and industrial chain connections through mechanism of market competition. Nonsubsidized incentive in the consumption segment stimulates service transitions among NEV enterprises, particularly those lacking political connections but possessing industrial chain connections, through mechanism of scale expansion. Therefore, to promote the innovative development of emerging industries, policy should be shifted from ‘transfusion-style’ to ‘hematopoietic-style’, gradually cultivating enterprises' independent innovation capabilities. Meanwhile, policy resources should be allocated according to resource differences among enterprises. This study provides practical policy recommendations for the transformation of China's NEV industry and offers insights into the development of emerging industries for developing countries.

Does regional gambling culture affect long-term corporate operational risk?–Empirical evidence from lottery sales

The findings of this paper indicate that regional gambling culture can increase the long-term performance volatility of enterprises. The results of heterogeneity tests show that the impact of regional gambling culture on the long-term performance volatility of enterprises is more pronounced in companies with higher information asymmetry and those in regions with lower levels of marketization. This discovery of variability offers targeted suggestions for enterprises to formulate cultural industry development strategies under different economic backgrounds and industry characteristics, providing practical guidance for policymakers and corporate managers.

Prediction of energy consumption for manufacturing small and medium-sized enterprises (SMEs) considering industry characteristics

There has been a growing demand for energy consumption statistics in the manufacturing industry to establish national energy and greenhouse gas policies. Despite its importance, the Korean government faces significant challenges in collecting energy data at a facility level in a precise and timely manner. To address the lack of timely data, this paper employs machine-learning models to predict the annual total energy consumptions of each manufacturing facility. We first designed four prediction models that take into account the characteristics and energy consumption behaviors of industry sub-sectors. As input variables, these prediction models mainly included electricity consumption, employee size, energy types, gas consumption and other accessible data. Finally, we conducted numerical experiments on approximately 100,000 facilities and evaluated the prediction performance of various machine-learning algorithms such as linear regression, decision tree regression, random forest regression, gradient boost regression, and extreme gradient boosting regression. The numerical experiments provided insights into which model and algorithm offer the best prediction performance for each industry sub-sector. In addition, we identified the important variables for predicting total energy consumption, revealing that not only electricity but also various other energy sources and variables representing industry-specific characteristics play a crucial role in improving prediction performance.

Improvement of DEVA Model in Valuation Research of Internet Financial Information Service Companies

With the development of the digital economy and the internet, the internet financial information service industry has shown strong momentum. Different from traditional enterprises, internet financial information service companies possess characteristics of low assets, high risks, and difficulties in measuring non-financial indicators. As a result, traditional valuation methods exhibit inaccuracies in application and require the introduction of non-financial valuation models for validation. Therefore, considering the industry's characteristics from a user perspective, we introduce the Metcalfe valuation modelthe DEVA valuation model. Simultaneously, we improve this model and, using Hithink Royalflush Information Network Co., Ltd. as a case study, estimate the value at the evaluation point using both the original DEVA model and the revised new model. Finally, we analyze and compare the differences between the various valuations and Royalflush's total market value at the evaluation point, concluding that the improved DEVA model yields more fair and accurate valuation results.

The effect of manufacturing overhead on policies for allocating production capacity

This research explores how manufacturing overhead influences profit margins under different capacity allocation policies in a build-to-order context. By applying a multi-period Mixed-Integer-Programming model to assess industries with varying overhead structures, the study establishes a link between the levels of capacity allocation and profitability, underscoring the strategic significance of overhead costs in capacity management decisions. Our results indicate that industries with lower overhead costs benefit more significantly from increased capacity allocation, with up to a 25% improvement in profit, compared to a 5–10% improvement in higher overhead industries. Conversely, at full capacity demand rates, increased allocation levels detrimentally affect profit, particularly in industries with higher overhead ratios. The study utilises a 3-way ANOVA to confirm the significance of these findings, revealing that industry type, demand rates, and allocation levels are critical factors in optimising profit. This research highlights the need for a pragmatic approach to capacity allocation that aligns with industry characteristics and demand levels, steering away from revenue maximisation to a more holistic view of profitability that includes overhead cost management.

The stock market reaction to political and economic changes: the Spanish case

As other European countries, Spain has experienced a turmoil in which new political parties emerged with force after the global financial crisis in 2008. In this context, we analyze whether the empirical implications of the opportunistic and partisan theories, as well as the Uncertain Information Hypothesis, are met in the Spanish stock market, considering the size and the industry of the companies. The horizon of our study takes into account the seven general elections, 24 regional elections and 4 European elections held from 2002 to 2019, period in which Spain is fully integrated in the economic and monetary union and under the macroeconomic imbalance procedure. Our results do not support the opportunistic and partisan theories, or the Uncertain Information Hypothesis. Our evidence suggests that the short-term negative market reaction to the general elections is linked to the uncertainty with a change in the political sign of the incumbent. Besides, it is not related to size or industry characteristics.

A Systematic Literature Review of Working Capital Management in Business Organizations

Working capital management is a crucial aspect of financial management that focuses on the effective management of a company's current assets and liabilities to ensure the smooth operation of day-to-day activities. A literature review on working capital management reveals a plethora of research studies highlighting the significance of optimizing working capital to enhance a firm's profitability, liquidity, and overall financial performance. Several studies have emphasized the importance of maintaining an optimal level of working capital to strike a balance between liquidity and profitability. By efficiently managing components such as cash, inventory, accounts receivable, and accounts payable, companies can minimize the risk of financial distress and improve their operational efficiency. Researchers have proposed various models and techniques to assess and improve working capital management practices, such as the cash conversion cycle, the operating cycle, the current ratio, and the quick ratio. Moreover, studies have also explored the impact of working capital management on firm value and performance. It has been widely documented that effective working capital management positively influences a company's profitability and shareholder value. By reducing the cash conversion cycle and operating cycle, firms can release trapped capital and reinvest it in value-creating activities. Conversely, inadequate working capital management can lead to financial constraints, increased costs, and reduced profitability. Furthermore, researchers have investigated the determinants and challenges of working capital management across different industries and regions. Factors such as industry characteristics, firm size, profitability, growth rate, and macroeconomic conditions have been identified as key determinants of working capital management practices. Cultural, regulatory, and technological factors also play a significant role in shaping companies' approaches to working capital management. In conclusion, the literature review on working capital management underscores the importance of adopting efficient strategies to manage current assets and liabilities effectively. By optimizing working capital levels, companies can improve their financial performance, enhance liquidity, and create long-term value for stakeholders.

Approaches to Developing the Manufacturing Industry of Small and Medium Enterprises for Readiness Towards Large Businesses in Thailand

This research aims to 1) study the structure and operational characteristics of the manufacturing industry in small and medium-sized enterprises (SMEs) in Thailand for readiness towards becoming large-scale businesses; 2) investigate the components of development approaches for the manufacturing industry in SMEs for readiness towards becoming large-scale businesses; and 3) develop a structural equation model (SEM) for the development approaches of the manufacturing industry in SMEs for readiness towards becoming large-scale businesses. The study employed a mixed-methodology approach, incorporating both quantitative and qualitative research. The qualitative research involved in-depth interviews with a sample of 9 experts from 3 different groups: 3 entrepreneurs or executives in the manufacturing industry, three from the government sector, and three academics. For quantitative research, data was collected via surveys from 500 entrepreneurs or executives and analyzed using frequency, percentage, average, standard deviation, and structural equation modeling (SEM) with the advanced statistical analysis software AMOS. The research on the general characteristics of organizations found that they have been operating for 10-15 years, are predominantly Thai-owned, use financial institutions for funding, are established as limited companies, and mostly have offices located in Thailand. In terms of structure and operational characteristics, the research revealed that the most suitable business strategy for these organizations is creating a product advantage, emphasizing mentorship systems from knowledgeable employees, and key leadership qualities, including treating colleagues with kindness and generosity and developing leadership at various levels focusing on experience, comprehensive knowledge, and business expertise.The study on the importance of different components indicated that all aspects were highly important, with the order of importance from highest to lowest being digital technology, entrepreneurial leadership, knowledge management, and innovative organization. The results of the analysis of the structural equation model for small and medium-sized enterprises management for sustainable strength found that the chi-squared probability (CMIN-r) was 0.128, the relative chi-squared (CMIN/DF) was 1.126, the GFI was 0.955, and the root mean squared index of error estimation (RMSEA) was 0.015. It can be concluded that the model is consistent with the empirical data.

What Attracts Green: Value Differentiation of Environmental Industry and Coagglomeration with Pollution-Intensive Industries in China

Scholars have spent decades studying how environmental (“green”) industry can promote sustainability. Few studies, however, have discussed corporate dynamics within green industry and their spatial interactions with other manufacturing and service industries. Drawing on the theoretical perspective of industrial value chain differentiation, this study unpacks the spatiotemporal dynamics of environmental enterprises by various types in China. We use the Wasserstein coagglomeration index and Poisson regression to identify the spatial coagglomeration of green and pollution-intensive (“brown”) industries. The results show that (1) China’s environmental industry is still in the early and middle stages of development with low and medium added value as the core; (2) green enterprises are mainly distributed in the eastern coastal areas and a few inland provinces with developed infrastructure; and (3) the entry and spatial distribution of equipment and engineering enterprises are highly correlated with local brown industries, whereas high-end service enterprises tend to concentrate in economically developed urban agglomerations. This research hopes to reveal the spatial characteristics of environmental industry in the regional green transition through the decomposition of its value chain and the analysis of the spatial coagglomeration effect.