Empirical Analysis Research Articles

Synergizing shale enhanced oil recovery and carbon sequestration: A novel approach with dual lateral horizontal wells

The dual challenge of enhancing oil recovery while sequestering carbon dioxide (CO2) in oil reservoirs is a pivotal concern in the energy sector. CO2 injection is recognized for its ability to decrease oil density and viscosity, thereby improving oil mobility and recovery rates. Traditionally, efforts have been concentrated either on enhancing oil recovery (EOR) or carbon storage, but not many efforts spent to couple EOR and CO2 sequestration. Hence, novel techniques to optimize engineering designs to synergize EOR with CO2 sequestration is the best approach to maximize the opportunities of storing emission gas and contribute to the global world decarbonization goals.This study introduces an innovative dual lateral horizontal well design, aimed at simultaneously boosting oil recovery from shale reservoirs and enhancing CO2 retention. By integrating a conceptual understanding of oil recovery mechanisms with empirical data from the field, this research contrasts the proposed dual lateral design with the conventional Huff-n-Puff gas injection technique, commonly employed in shale oil formations.Our findings demonstrate that the dual lateral horizontal wells significantly outperform other injection methods in both oil recovery and CO2 storage. Upon optimization, the dual lateral injection design continues to surpass the Huff-n-Puff method in terms of CO2 storage, oil recovery, and net present value (NPV). This investigation not only presents innovative gas injection strategies in shale reservoirs but also provides insights into optimizing gas injection methods to enhance production efficiency and contribute to climate change mitigation through improved carbon capture and storage capacities.Through comprehensive numerical simulations and empirical data analysis, the study explores the optimization of well spacing, revealing that a dual lateral well with optimized spacing between 20 and 30 feet achieves the best outcomes in terms of oil recovery, CO2 retention, and net present value (NPV). The research presents a unique coupling of economic and environmental benefits, supported by economic analysis that includes the potential impact of CO2 tax credits.The novelty of this research is underscored by its integrated approach to CO2-EOR, the development of a dual lateral well design, and the optimization of well spacings for maximized efficiency. By providing a scalable solution that is both economically viable and environmentally sustainable, this research contributes a significant paradigm shift in the field of EOR and CO2 sequestration, with implications for policy and investment strategies in the energy sector. The findings propose a new direction for shale reservoir exploitation, promising to enhance production efficiency while contributing to global efforts in greenhouse gas reduction.

Do sanctions affect the environment? The role of trade integration

Environmental degradation is one of the potentially unforeseen consequences of sanctions, yet few studies have investigated how sanctions affect the environment. For instance, by restricting the acquisition of more efficient technologies, sanctions can strain natural resources and erode environmental performance. Therefore, this study investigates the moderating effects of trade integration on the underlying links between several types of sanctions (i.e., military, arms, trade, financial, and travel) and the environmental quality of the target country. A rich dyadic dataset consisting of 214 sender countries/states, 135 target countries/states, and 28,532 country pairs is used for the empirical analysis. A multi-way panel fixed effects model is employed for the empirical analysis. This approach allows us to control for the unobserved sender and target country-specific as well as time-specific characteristics that might affect environmental performance. The results show that most sanctions result in the deterioration of the environmental quality. In contrast, trade sanctions promote the environmental quality of the target country. We also find that sanctions adversely impact the ecological aspect of the environment while positively influencing the climate change aspect of environmental quality. The results also show that greater bilateral trade interdependence between the sender and the target country helps to mitigate the adverse impacts of most sanctions. The findings of this study suggest that the real effects of sanctions on environmental quality cannot be empirically disentangled if several types of sanctions are clubbed under the umbrella of ‘sanctions’ and captured using a single dummy variable, as is often done in the extant literature. Policies that encourage greater economic integration, such as trade liberalization with multiple countries, can be used strategically by a country to reduce threats of being sanctioned or vulnerabilities to the negative effects of sanctions on the environment.

Impact of industrial robot on labour productivity: Empirical study based on industry panel data

This empirical analysis explores the impact of industrial robots on labour productivity using panel data of 17 Chinese industries from 2006 to 2021. The results reveal that the development of industrial robots significantly improves labour productivity; a series of robustness tests validate this outcome. However, the impact of industrial robots on labour productivity varies across industry types. The influence coefficient of the low-density robotics industry is larger than that of the high-density robotics industry. Furthermore, although the scale of industrial robot usage before 2012 was smaller than that after 2012, its effect on labour productivity was more significant. Our findings indicate the possibility of diminishing marginal effect of industrial robots in promoting labour productivity. The mechanism analysis demonstrates that human capital level has a complete intermediating effect between industrial robots and labour productivity. Thus, industrial robot applications can contribute to labour productivity by optimising human capital structure. These findings provide crucial insights for governments and policy makers to improve labour productivity and economic growth.

Exploring educational trends and challenges in the MENA region amidst a global crisis: An empirical analysis of the pandemic’s impact on SDG4

The paper presents a cross-country analysis of seven countries in the MENA region and explores the effects of COVID-19 on educational policymaking. The paper uses a quasi-thematic literature review to highlight the emerging themes related to the quality of education during the pandemic. The paper investigates the educational trends in seven MENA region countries and provides descriptive analysis to understand if there were changes from 2016 to 2020. In order to investigate these trends, the study uses the conceptualization of Sustainable Development Goals (SDG 4) “Quality of education.” The analysis is based on the data from the World Bank Development indicators. The paper’s evidence identifies a difference in the level of government expenditure on education within the MENA region. Pre and during COVID-19 provides crucial insights such as all countries increased their government expenditure on education except Egypt and Bahrain. The paper further addresses the contributing factors to the educational gaps in the region, such as digital exclusion, inequality (income and gender), and the government’s budget constraints during the pandemic. This study presents recommendations to bridge these identified gaps in the post-pandemic era.

A Fake Products Identification by Using Blockchain

Abstract: Counterfeiting of products is a significant challenge across various industries, leading to economic losses and risks to consumer safety. This paper proposes a novel approach to combat counterfeit products using blockchain technology. By leveraging the immutable and transparent nature of blockchain, a framework is devised to authenticate products throughout their lifecycle. The system utilizes unique identifiers assigned to each product during manufacturing, which are then recorded on a blockchain ledger. Through a decentralized network of nodes, stakeholders including manufacturers, distributors, retailers, and consumers can verify the authenticity of products in real-time. Smart contracts are employed to automate verification processes, ensuring integrity and efficiency. Additionally, the framework incorporates mechanisms for data privacy protection, ensuring sensitive information remains secure. Through empirical analysis and simulations, the effectiveness and scalability of the proposed solution are evaluated. Results demonstrate a significant reduction in counterfeit incidents and enhanced trust among stakeholders. This research contributes to the advancement of anti-counterfeiting measures, offering a robust solution to safeguard product authenticity in the digital age.

The Impact of Social Media on Consumer Behavior and Purchasing Decisions in the Fashion Industry

Abstract: The present study aims to examine the noteworthy impact of social media platforms on customer behavior and purchase decisions in the ever-changing fashion business. Fashion marketers now need to grasp how social media usage affects consumer behavior due to its exponential expansion worldwide, especially among fashion-conscious populations. This study investigates the complex relationship between social media involvement and several aspects of consumer behavior, such as brand awareness, brand loyalty, buy intentions, and actual purchase behavior. It does this by conducting a thorough review of the existing literature and doing empirical analysis. Additionally, the study explores the ways in which brand advertising, influencer endorsements, and user-generated material on social media influence customer perceptions and preferences in the fashion industry. This study integrates theoretical understandings with real-world applications is to offer insightful information to fashion firms and marketers who want to improve their social media tactics and gain a competitive edge in a sector that is becoming more and more digital

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

The abstract is "The rapid evolution of cyber threats necessitates innovative defenses, particularly in the domains of risk assessment and fraud detection. This paper explores the integration of Artificial Intelligence (AI) and Adversarial Machine Learning (ML) techniques as a formidable strategy against increasingly sophisticated cyber-attacks. We present a comprehensive framework that leverages AI to dynamically assess cybersecurity risks and detect fraudulent activities with unprecedented accuracy and speed. Firstly, we delve into the foundational principles of adversarial machine learning, outlining how these techniques can be employed to simulate potential cyber threats, thereby enabling the development of more resilient AI-driven cybersecurity systems. We highlight the dual role of adversarial ML in both enhancing security defenses and potentially serving as a vector for sophisticated attacks, underscoring the importance of developing robust, adversarial-resistant models. Subsequently, we introduce a novel adaptive risk assessment methodology that incorporates real-time data analysis, machine learning algorithms, and predictive modeling to accurately identify and prioritize threats. This method adapts to the evolving digital landscape, ensuring that cybersecurity measures are always one step ahead of potential attackers. In the context of fraud detection, we explore -how AI algorithms can sift through vast datasets to detect anomalies and patterns indicative of fraudulent behavior. Through case studies and empirical analysis, we demonstrate the effectiveness of AI in identifying fraud across various sectors, from financial transactions to online identity verification processes. Our research contributes to the cybersecurity field by providing a detailed examination of how AI and adversarial ML can be harnessed to fortify digital defenses, improve risk assessment techniques, and enhance fraud detection capabilities. The insights garnered from this study not only advance theoretical understanding but also offer practical guidance for organizations seeking to implement AI-driven security solutions. As cyber threats continue to evolve, the integration of AI and adversarial ML in cybersecurity strategies will be paramount in safeguarding digital assets and maintaining the integrity of online systems."

Empirical investigation of drivers, motivations and barriers for implementation of integrated continuous improvement system: a case of Ethiopian manufacturing industries

PurposeContinuous improvement in an integrated approach is a philosophy developed over decades based on a set of management practices. It comprises enhancement methodologies that escalate success as well as diminishes letdowns. The state-of-the-art literature finds a variety of practices for the execution of continuous improvement (CI) system. However, it is rare to find an empirical study with an inclusive system that considers various practices in one frame for improving competitiveness. With this outlook, therefore, this study aims originally to identify drivers, motivations and barriers for the application of integrated CI system, and then conduct an empirical investigation within the context of Ethiopian manufacturing industries.Design/methodology/approachJustification of the problem, extensive review of literature, identification of practices, developing research framework, investigational analysis of the empirical study using reliability and descriptive statistical analysis and identifying leading drivers (unique practices and common factors), motivations and barriers are the research approaches used in this study. Furthermore, the primary data were collected through a self-administered questionnaire and were analyzed using a statistical package for social science (SPSS) 23 and the findings were triangulated to relate to the existing state-of-the-art literature.FindingsThe findings indicate 17 common factors associated with human, strategic, operational, technology, structure, resource and information factors; 21 unique practices of just-in-time (JIT), lean-six sigma (LSS), supply chain management (SCM), total productive maintenance (TPM), and total quality management (TQM) methodologies; 20 barriers connected with internal and external issues and 17 motivations linked to operational, innovation and business results are identified. In general, the empirical analysis discovers the practices are noteworthy and commonly supported by a least of 52% of the respondents. As a result, the first five prominent common factors, barriers, unique practices and motivations are also supported by a minimum of 72%, 73.9%, 65.8% and 75% of the respondents, respectively.Research limitations/implicationsEven though this is the first-ever study in the Ethiopian manufacturing sector with the focus on integrated CI practices of JIT, TQM, TPM, SCM and LSS initiatives, some limitations have existed and the major limitations of the study are, targeted merely small number manufacturing companies despite the fact that there are hundreds of companies implementing CI system in Ethiopia, and the data collected were only based on the perception of the respondents, and other information’s like annual reports was not employed to support the findings.Practical implicationsThe findings of this study underlined that the implementation of drivers in relation to common factors and unique practices supported by exterminating barriers in manufacturing industries of Ethiopia enables to enhance competitiveness through attaining operational, innovation and business results though it requires a practical case application to prove this perception-based analysis.Originality/valueThough there are a number of studies published on integrated continuous improvement, currently it is found that there is no literature focused on the identification and empirical investigation of drivers (common factors and unique practices), motivations and barriers for application of integrated CI system with the focus of JIT, TQM, TPM, SCM and LSS initiatives. Therefore, this is the first-ever study, and the empirical analysis of the study discovered that identified practices are providing valuable insights for manufacturing industries which will be on board on this voyage including institutions, practitioners and other sectors.

Methods for Measuring the Impact of Sustainable Tourism Development on Climate and Environment

Climate and environmental change is a growing topic in the post-Covid-1, when tourism growth is recovering. Tourism is a significant polluter of the environment and a rapid response to climate change is needed. Methods for assessing the climate and environmental impact of sustainable tourism development help to measure and communicate the environmental impact of tourism activities, but they are difficult to select and often lack indicators. The aim of the study was to choose methods for assessing the climate and environmental impact of sustainable tourism development in the context of Latvia. The study was based on the analysis of scientific literature, the grouping of methods for assessing the climate and environmental impact of sustainable tourism development according to their functional characteristics i.e. "preventive", "protective", "estimating" and "carbon emission determining" methods. A multi-criteria decision-making method was used for the selection of evaluation methods - Analytical hierarchy process with empirical analysis (AHA) of the obtained weights. By summarising the environmental and climate indicators available for a specific region of Latvia regarding the impact of tourism and experts evaluating the obtained results, the most suitable method for evaluating the impact of tourism development on the climate and environment is the Tourism value chain for low carbon content and efficient use of resources. However, the authors' recommendation for researchers: 1) when evaluating the impact of tourism development on the climate and environment, the choice of method ought to be based on evaluation indicators available for a specific study; 2) in research combinations of sets of "evaluative" and "carbon emission determining” methods should be used.

Energy Efficiency Improvement for Manufacturing Companies in Latvia

Concerns about climate change and environmental issues are becoming more and more popular nowadays and are causing concerns in everyone's life and beyond. One of significant causes of climate change is energy consumption, especially in the manufacturing sector. Even though, energy efficiency (EE) topic has been extensively discussed, not all EE aims are met. For the manufacturing sector the reason is that EE is often not a top priority for the companies. Currently the companies are also faced with another big challenge – in this digital era of rapidly developing technologies they have to adjust their practices to seize the opportunities provided by digitalization and automatization. Therefore, the aim of this research is to explore how digitization can be combined with EE to promote the resilience and increase performance of manufacturing companies, and how remote data analysis, also by using machine learning tools, can help improve EE. Literature review, content analysis, empirical data analysis and cases studies from real production companies in Latvia are used to achieve the research goals. The results section shows how digital tools and real-time monitoring help assessing the current state of the business and make decisions on changes to future operations, helping to reduce consumption and the environmental impact of production. Manufacturing companies have the potential to improve EE through digitalisation, but the manufacturing processes are complex and Latvian companies are only slowly moving towards digitalisation. Therefore, there is a strong need to examine empirical cases to gather more perspective and to find a way to implement digitalization and EE improvement across all sectors. Conclusions suggest that manufacturing companies should be encouraged to move towards a more unified energy data collection system, enabling more efficient data analysis and proposals for energy efficiency improvements.

Centre party, district magnitude, and wage inequality in service economy

This paper aims to elucidate the variations in wage inequality within the service industry across different nations. Scholars have emphasised the pivotal role of policies designed to balance flexibility and job security in the service sector, crucial for fostering its growth while mitigating wage disparities. We argue that the balance between flexibility and security of service sector employment is most likely to emerge under centre party governments in high-magnitude electoral systems. Centre parties, owing to their political detachment from organised labour and industry, encounter fewer obstacles in representing the unorganised interests of service sector workers. Furthermore, high-district magnitude systems incentivise centre parties to build comprehensive coalitions that address the diverse concerns of service sector workers. Our empirical analysis, spanning 14 OECD countries from 1995 to 2007, demonstrates that service sector wage inequality diminishes as district magnitude and the cabinet share of centre parties increase.

Research on Stock Market Risk Contagion of Major Debt Crises Based on Complex Network Models—The Case of Evergrande in China

After a major debt crisis occurs in a listed company, the stock prices of related enterprises may also fluctuate sharply, resulting in the spread of debt risk to more enterprises. Taking the stocks of listed companies as network nodes, we constructed a complex stock market network over three periods of time through the logarithmic return rate of stocks for the three periods of prophase, metaphase, and anaphase of the debt crisis. We studied the topological characteristics of the network and destructiveness over the three periods. Finally, the minimum spanning tree was used to construct a network and the community structure of the network. The empirical analysis took the debt crisis of the China Evergrande Company as an example to analyze the impact of its major debt crisis on the Chinese stock market. The research findings were as follows: First, the debt crisis increased the inter-industry connections within the network, that is, the correlations between enterprises in different industries was enhanced. Second, the closeness of the cross-industry connections increased the connectivity efficiency of the network, but compared with the other two periods, the debt crisis in the metaphase was less stable. Third, community research showed that in the metaphase of the debt crisis, the enterprises became the core nodes of the network.

The Effect of Technological Innovation on Sustainability and Industry 4.0 Implementation: An Empirical Analysis of Indonesian Small and Medium-Sized Businesses

Small and medium-sized businesses, or SMEs, are the foundation of the majority of economies in the world. Small and medium-sized enterprises (SMEs) will find it extremely difficult to improve their performance and hard to meet their sustainability objectives in the absence of qualified technological innovation. Hence, Industry 4.0 (I4.0), the next technology frontier, should be embraced by small firms. This study's primary goal is to comprehend the connection between sustainability objectives and the adoption of Industry 4.0 (I-4.0) technologies. It also examines how innovative features facilitate the adoption of I-4.0 by small enterprises. In order to address the research inquiries and scrutinize the intricate data, AMOS software was used to conduct a structural equation model in this study. The findings showed that the adoption of I-4.0 and sustainability objectives are positively and significantly impacted by the features of technological innovation. With the exception of observability, effective I-4.0 implementation mediates the relationship between innovation traits and sustainability goals. According to the research's conclusions, SMEs should create an effective I-4.0, put it into practice, and cultivate innovative traits in order to meet sustainable objectives.

Empowering co-creation of services with artificial intelligence: an empirical analysis to examine adoption intention

PurposeCo-creation of services (CCOS) is a collaborative strategy that emphasises customer involvement and their expertise to increase the value of the service experience. In the service ecosystem, artificial intelligence (AI) plays a key role in value co-creation. Therefore, this study is undertaken to empirically uncover how AI can empower CCOS.Design/methodology/approachThe source data were collected from 305 service provider respondents and quantitative methodology was applied for data analysis.FindingsNew service development augmented with AI provides tangible value to service providers while also providing intangible value to supportive customers. With AI, service providers adapt to new innovations and enrich additional information, which eventually outperforms human-created services.Research limitations/implicationsAI adoption for CCOS empowerment in service businesses brings “service-market fit”, which represents the significant benefits wherein customers contribute to creativity, intuition, and contextual awareness of services, and AI contributes to large-scale service-related analysis by handling volumes of data, service personalisation, and more time to focus on challenging problems of the market.Originality/valueThis study presents theoretical concepts on AI-empowered CCOS, AI technological innovativeness, customer participation in human-AI interaction, AI-powered customer expertise, and perceived benefits in CCOS, and subsequently discusses the CCOS empowerment framework. Then, it proposes a novel conceptual model based on the theoretical concepts and empirically measures and validates the intention to adopt AI for CCOS empowerment. Overall, the study contributes to novel insight on empowering service co-creation with AI.

An investigation into the relationship between quality and commercialisation channel of university patents

ABSTRACT We investigate how patent quality is correlated with different commercialisation channels for university patents. A dataset of 2052 university patents, including the market price of patents, is employed for the empirical analysis. We find that patents with more claims are less likely to be sold but more likely to be licensed, meaning that the decision of patent license is focused more on the patent’s legal stability. Furthermore, patents with more IPC classes and broader scope are more likely to be sold, meaning that the decision of patent selling is focused more on the potential of the patent to be used in different areas. The price of sold patents is positively correlated with the number of claims, number of IPC classes, scope, and family size, whereas the price of licensed patents is positively correlated with the forward citations they receive and family size. Considering the time effect of patent grants and patent commercialisation, pre-grant patents are more likely to be commercialised by selling them at a price higher than granted patents, and patents with more claims tend to be commercialised for a shorter duration.

Evaluating the influencing factors and effects of the digitalization of management control

Purpose This study aims to investigate the influences on the digitalization of management control and the effects of this digitalization on management control tasks performed, management control instruments used and the organization of management control. Design/methodology/approach This empirical analysis is based on a survey of 189 management accountants and managers responsible for management control in Germany, Austria and Switzerland. Logistic regression analyses were used to test the investigated effects. Findings This study finds that digital competencies, standardization of processes and data management contribute to the digitalization of management control. This study further finds that digitalization significantly increases the coverage of strategic and operational management control tasks and the use of operational management control instruments but not of strategic instruments. Originality This study investigates the influence of digitalization in management control on strategic and operational management control tasks, instruments and organizations. In contrast, prior research has focused on single aspects of management control or analyzed the impact on the entire company. This is also the first study, to the best of the authors’ knowledge, to systematically identify potential influences on the digitalization of management control and analyze them empirically.

Examining the impact of the stock market development on Economic growth- KSA Tadawul.

The relationship between stock market development and economic growth has been a subject of debate, with arguments centered on the impact of a well-functioning stock market on economic growth. This study argues that economic growth is influenced by stock market development and economic reform indicators, while controlling for other determinants of growth. The research employs a comprehensive theoretical framework linking stock market development to economic growth, and it specifically focuses on the Tadawul stock market in Saudi Arabia. The empirical analysis, using sophisticated panel data econometric techniques, reveals a significant positive correlation between Tadawul stock market and economic growth, particularly in the oil sector. This suggests that the Tadawul stock market plays a crucial role in influencing and contributing to economic growth, especially within the context of the oil industry.

Families, relationships and technology: empirical analysis, policy challenges and ways of thinking

Families, relationships and technology: empirical analysis, policy challenges and ways of thinking

What factors influence termination behaviour on freemium platforms? An empirical analysis of the termination probability among paying users

What factors influence termination behaviour on freemium platforms? An empirical analysis of the termination probability among paying users

A double machine learning model for measuring the impact of the Made in China 2025 strategy on green economic growth

The transformation and upgrading of China’s manufacturing industry is supported by smart and green manufacturing, which have great potential to empower the nation’s green development. This study examines the impact of the Made in China 2025 industrial policy on urban green economic growth. This study applies the super-slacks-based measure model to measure cities’ green economic growth, using the double machine learning model, which overcomes the limitations of the linear setting of traditional causal inference models and maintains estimation accuracy under high-dimensional control variables, to conduct an empirical analysis based on panel data of 281 Chinese cities from 2006 to 2021. The results reveal that the Made in China 2025 strategy significantly drives urban green economic growth, and this finding holds after a series of robustness tests. A mechanism analysis indicates that the Made in China 2025 strategy promotes green economic growth through green technology progress, optimizing energy consumption structure, upgrading industrial structure, and strengthening environmental supervision. In addition, the policy has a stronger driving effect for cities with high manufacturing concentration, industrial intelligence, and digital finance development. This study provides valuable theoretical insights and policy implications for government planning to promote high-quality development through industrial policy.