Key Market Research Articles

Why Did China Design Its Emission Trading System Different than the EU?

Emission trading systems (ETS) contribute to economic efficiency by facilitating emission reductions where it is cheapest to achieve them. Polluters who would find it costly to reduce their emissions can buy emission allowances from polluters that can abate at lower costs. Through the comparison between the carbon trading market of the EU and China and an overview of each market, including key strategies and market performance, the reasons why China's designed its carbon trading market most suitable for its economy, along with the edges and costs of each market will be analyzed. This study indicates that China's current ETS design is beneficial for its current economic development; however, this design limits technological developments in renewable energy and transformations in economic structure. This will impact China's economic development in the future.

Valuation and Analysis of Equity Value of Tesla

Tesla has conquered the electric vehicle market in its two-decade history. Key market indicators like stock price, sales units, and customer alignments signify a company on an upward trajectory. Its stocks soared in 2021, and Tesla became a trillion-dollar entity. The research topic will analyze Tesla's financial the electric vehicle market to forge a basis for future projections. By many measures, Tesla is in an excellent financial position. EVs demand is at historical highs, and future predictions are superb; its EV technologies, like batteries and charging stations, are optimal. It has diversified its production hubs to include Asia and Europe. The paper analysis Tesla's SEC filing to comprehend valuation, profitability, and prospects.

A Comprehensive Outlook of Scope within Exterior Automotive Plastic Substrates and Its Coatings

Among non-metal substrate coatings, plastic substrates have certainly asserted themselves as a key market, especially in the automotive industry. The substrates within exterior automotive industries are being diversified and commercialized at a rapid pace. Mainly attributed to the emergence of electric vehicles, environmental regulations, and functional requirements, there is huge scope and many opportunities for advancements and groundbreaking technologies. The above factors have led to the dawn of new and improved coating materials within the revolutionizing new substrate market. At the same time, new product innovations within ultraviolet (UV)-curable technologies, powder coating, nanotechnologies, and several other platforms have brought the possibility of novel coating systems into the picture. This review outlines the inherent challenges, the progress made thus far, and the future opportunities of two significant exterior plastic substrates: thermoplastic polyolefin (TPO) and polycarbonate (PC). While the former serves as the historical choice for plastic bumpers across the globe, the latter has captured the most research attention in recent times as a lightweight option against glass coatings. A meticulous overview of the existing literature was conducted to summarize the recent approaches with a few critical analyses. To surpass the challenges to the equivalent performances, a few of the most promising research studies were also examined, including the sustainability aspect.

Strategic Imperatives of Sustainable Marketing of Religious Tourism in Nepal

The growth of tourism industry presents both opportunities and challenges and requires effective marketing strategies to promote sustainable development. In this context, the study examines the current state and identifies sustainable marketing strategies for religious tourism in Nepal. The study is based on a qualitative research design and utilizes self-administered open-ended questions to collect data. It employs snowball sampling to select samples from two distinct groups: tourism professionals and entrepreneurs, who are acknowledged for their remarkable abilities and expertise in the field and have achieved high levels of professional recognition at the national level. The analysis involves identifying key themes related to the data and relevant literature using a thematic analytical approach. The study is limited due to the small sample size. Results of the study indicate that a balanced approach, which integrates economic, social, and environmental marketing initiatives, is crucial for the sustainable development of Nepal’s religious tourism industry. The study emphasizes the interdependent nature of these initiatives and their significant impact on the industry’s long-term viability. The study provides insights into the key marketing strategies and best practices of religious tourism for governments, tourism boards, entrepreneurs and stakeholders to drive long-term success and growth in Nepal.

PREDICTING BITCOIN PRICE WITH THE LSTMA MODEL

Purpose- Forecasting techniques and models are extremely important for people and organizations that are in the right decision making and investment stage. Forecast accuracy enables successful decisions and allows investors to maximize their profits. The development of finance and related technologies in the world and innovative financial instruments have made it interesting for investors. The most popular of these developments is undoubtedly Bitcoin, a product of blockchain technology. The purpose of this study is to predict the future values of Bitcoin. Methodology- In this study, future predictions are made using an LSTM model based on Bitcoin's historical data and indicators of key market forecasters. In this study, 3 different data sets were created by selecting 1 indicator from 4 different indicator types. The 10 Bitcoin data coming after the last value is estimated. Findings- In this study, 3 different data sets were created by selecting an indicator from 4 different indicator groups. These datasets were first trained with the iterative neural network LSTM model and then tested with real values. At the same time, the next 10 bitcoin price values were also predicted in a 15-minute period. Error rates at the end of the model were compared with each other. The 1st dataset, with the most used indicators in the datasets, produced the lowest error rate. Conclusion- The dataset 1, which consists of the most used indicators of the datasets, gave the lowest error rate. According to this result, the rate of reaching realistic values increases as the use of indicators increases. Keywords: LSTM, bitcoin, cryptocurrency, neural network, prediction JEL Codes: C53, C45, G10

Data-Driven Deployment of Cargo Drones: A U.S. Case Study Identifying Key Markets and Routes

Electric and autonomous aircraft (EAA) are set to disrupt current cargo-shipping models. To maximize the benefits of this technology, investors and logistics managers need information on target commodities, service location establishment, and the distribution of origin–destination pairs within EAA’s range limitations. This research introduces a three-phase data-mining and geographic information system (GIS) algorithm to support data-driven decision-making under uncertainty. Analysts can modify and expand this workflow to scrutinize origin–destination commodity flow datasets representing various locations. The algorithm identifies four commodity categories contributing to more than one-third of the value transported by aircraft across the contiguous United States, yet only 5% of the weight. The workflow highlights 8 out of 129 regional locations that moved more than 20% of the weight of those four commodity categories. A distance band of 400 miles among these eight locations accounts for more than 80% of the transported weight. This study addresses a literature gap, identifying opportunities for supply chain redesign using EAA. The presented methodology can guide planners and investors in identifying prime target markets for emerging EAA technologies using regional datasets.

“You are a virtual influencer!”: Understanding the impact of origin disclosure and emotional narratives on parasocial relationships and virtual influencer credibility

Virtual influencers have become increasingly prevalent because of technological advancements and the fact that they offer benefits similar to human influencers. Studies have found that virtual influencers engage consumers just as effectively as humans, but their artificial identity poses credibility concerns that can hinder the effectiveness of influencer marketing. Given this, two experiments were conducted to explore key marketing and communication factors (i.e., the disclosure of the virtual influencer's origin and the use of positive vs. negative emotional narratives) that influence virtual influencers' credibility. A pre-study investigated the effect of disclosing a virtual influencer's origin on perceived humanness. Additionally, the main study examined whether the virtual influencer's origin disclosure and emotional narratives jointly influenced the virtual influencer's credibility through changes of perceived parasocial interaction. The results are further presented in detail, offering both theoretical and practical implications.

A Unified Switched Nonlinear Dynamic Model of an Electric Vehicle for Performance Evaluation

The advanced modeling and estimation of overall system dynamics play a vital role in electric vehicles (EVs), as the selection of components in the powertrain and prediction of performance are the key market qualifiers. The state-space averaged model and small-signal transfer function model are useful for assessing long-term behavior in system-level analysis and for designing the controller parameters, respectively. Both models take less computation time but ignore the high-frequency switching dynamics. Therefore, these two models could be impractical for the development and testing of EV prototypes. On the other hand, the multi-domain model in available simulation tools gives in-depth information about the short-term behavior and loss analysis of power electronic devices in each subsystem, considering the switching dynamics in a long computation time. In this paper, a general mathematical framework for the dynamical analysis of complete EVs is presented using a unified, switched nonlinear model. This equation-based model runs faster than the available module-based simulation tools. Two other models, namely the time domain state-space averaged model and frequency domain small-signal transfer function model, are also developed from the switched nonlinear model for the analysis with less computation time. The design and performance of an EV with two different motors and its controllers are evaluated using the general mathematical framework.

Mergers & Acquisition Decisions in the Energy Sector Based on Financial Transparency and Audit Opinions

ABSTRACT The BRICS countries are known to be a key market for foreign direct investment. This paper analyzes the decision of EU acquirers from developed economies to invest in the energy sector in BRICS countries. Using empirical evidence on 4,222 cross-border M&As and fuzzy logic, the research results show that acquirers consider macroeconomic factors as significant. When choosing a company located in the BRICS, its audit status is significant, but financial returns are not. This fact indicates that for sustainable decisions, industry is importantly correlated with financial information quality but not with company performance.

The role of marketing metrics in social media: A comprehensive analysis

In the digital era, social media has emerged as a powerful marketing tool, revolutionizing the way businesses interact with consumers. As social media platforms continue to evolve, it is crucial for marketers to effectively measure and analyze their efforts. This article explores the significance of marketing metrics in the realm of social media, highlighting the key metrics used to evaluate marketing campaigns and their impact on business success. Through an in-depth analysis of various marketing metrics, this article provides insights into optimizing social media strategies for enhanced customer engagement, brand awareness, and return on investment. The primary objective of this article is to delve into the significance of marketing metrics in the realm of social media. By examining various marketing metrics and their relevance in evaluating social media campaigns, this article aims to provide marketers with insights on how to optimize their strategies and achieve desired outcomes. The scope of this article encompasses a comprehensive analysis of key marketing metrics, including reach and impressions, engagement metrics, conversion metrics, customer satisfaction metrics, brand awareness metrics, and influence and authority metrics. This article is based on a thorough review of existing literature, industry reports, case studies, and expert opinions on marketing metrics in social media. Primary and secondary sources were consulted to gather information on the various metrics used in social media marketing and their impact on business success. The analysis and insights presented in this article are derived from a synthesis of the available knowledge and experiences shared by marketing professionals and researchers in the field of social media marketing. By exploring the importance of marketing metrics in the context of social media, this article aims to equip marketers with the necessary knowledge and tools to make data-driven decisions, optimize their social media strategies, and achieve meaningful results.

The Role of Customer Engagement in Sustaining Subjective Well-being After a Travel Experience: Findings From a Three-Wave Study

Although customer engagement’s (CE) effects on marketing-related outcomes are well documented, its broader impacts on life domain constructs (e.g., subjective well-being [SWB]) have received less attention. We propose CE as a viable mechanism for prolonging travel’s positive effects on SWB. Specifically, this study adopts a three-wave design to investigate the linkages between destination brand experience (DBE), CE, and SWB over time. Our results indicate that sensory destination experience ( t1) and affective destination experience ( t1) stimulated CE with a destination ( t2), which contributed significantly to SWB ( t3). Findings from this longitudinal study contribute to the literature by demonstrating that CE significantly mediates the effects of the sensory and affective dimensions of DBE on tourists’ SWB over time. The study highlights the importance of CE beyond key marketing performance indicators.

MFNs: the latest turn in the policy debate – key lessons from the CAT’s judgment on CompareTheMarket’s use of wide MFNs in home insurance

For the last ten years, most-favoured-nation clauses (MFNs), also known as price parity clauses, have remained a hotly debated topic in competition policy. The direction of travel has generally been one-way, culminating with the UK and EU vertical block exemption regimes designating wide MFNs as ‘hardcore’ or excluded restrictions respectively. However, the UK Competition Appeal Tribunal’s judgment in BGL (Holdings) Limited v. Competition and Markets Authority injects nuance into the debate. The CAT found that, not only had the Competition and Markets Authority not proved its case on anti-competitive effects, but that it was unlikely that, in this case, wide MFNs had any effect on the key market outcomes of retail prices (i.e. insurance premiums), commissions paid by insurers to CompareTheMarket or increased barriers to entry. The CAT’s judgment and the economic evidence presented throughout the case provide valuable insights into how the competitive effects of wide MFNs should be assessed, why harmful effects may not arise and the role that econometric evidence can play. As explained in this article, at most, MFNs (whether wide or narrow) only directly restrict intra-brand competition and, if coverage and adherence to the MFNs are limited, competitive effects (whether pro-competitive or anti-competitive) may not arise. In this case, there was an opportunity to test empirically for any competitive effects, as CompareTheMarket removed its wide MFNs shortly after the CMA’s investigation opened (and no other price comparison website had them in place). As described by the CAT, ‘such a “before and after” consideration at least prima facie lends itself to econometric analysis’, which can provide a robust comparison to the situation that prevailed when wide MFNs were applied. In contrast, the CAT raised concerns with the qualitative evidence relied on by the CMA and its absence of any quantitative analysis of the competitive effects, if any, of the wide MFNs.

Machine learning algorithms applied to the estimation of liquidity: the 10-year United States treasury bond

PurposeHaving defined liquidity, the aim is to assess the predictive capacity of its representative variables, so that economic fluctuations may be better understood.Design/methodology/approachConceptual variables that are representative of liquidity will be used to formulate the predictions. The results of various machine learning models will be compared, leading to some reflections on the predictive value of the liquidity variables, with a view to defining their selection.FindingsThe predictive capacity of the model was also found to vary depending on the source of the liquidity, in so far as the data on liquidity within the private sector contributed more than the data on public sector liquidity to the prediction of economic fluctuations. International liquidity was seen as a more diffuse concept, and the standardization of its definition could be the focus of future studies. A benchmarking process was also performed when applying the state-of-the-art machine learning models.Originality/valueBetter understanding of these variables might help us toward a deeper understanding of the operation of financial markets. Liquidity, one of the key financial market variables, is neither well-defined nor standardized in the existing literature, which calls for further study. Hence, the novelty of an applied study employing modern data science techniques can provide a fresh perspective on financial markets.

Revisiting Market Power in the Polish Power System

The consequences of the liberalisation of electricity markets have been widely discussed in the literature emphasising the successes or failures of privatisation and deregulation. While most developed power systems have undergone a form of economic transformation, they still require to be monitored and analysed to assess market power. The Polish power system is an example wherein the potential of market power examined fifteen years ago was summarised as significant. Since then, the transformation process and changes in the ownership structure have taken place. This study focuses on the assessment of the potential of market power in the Polish electricity market. For this purpose, statistics on power companies were collected and processed. Then, structural and behavioural measures were applied, including concentration ratios, the entropy coefficient, the Gini coefficient, the Herfindahl–Hirschman Index (HHI), the Residual Supply Index (RSI), and the Lerner Index. The results reveal that, despite a dynamic increase in renewable capacity, market concentration has increased in recent years, achieving an HHI of 2020.9 in 2021. An increase in the Lerner Index of lignite and hard coal-fired units is also observed, indicating high mark-ups by the key market players. Based on quantitative analysis, policy recommendations are outlined to reduce the negative impact of market power on consumers.

A Study of Digital Marketing Strategies influencing buying behaviour of e-commerce consumers

With the rapid growth of e-commerce, businesses are increasingly relying on digital marketing techniques to attract and engage customers. Understanding the impact of these strategies on consumer behavior is crucial for organizations seeking to optimize their marketing efforts in the digital landscape. Through a comprehensive analysis of relevant literature and empirical research, this study investigates the key digital marketing strategies that significantly affect consumer buying behavior in the e-commerce context. This research paper examines the influence of digital marketing strategies on the buying behavior of e-commerce consumers. The findings shed light on the dynamic relationship between digital marketing and consumer behavior, providing valuable insights for businesses to develop effective marketing strategies and enhance customer satisfaction.

Gum and Resin Production and Marketing: Implications for Pastoral Livelihood in Adadle District, Somali Region, Ethiopia

Both locally and nationally, Ethiopia’s gum resin sector has a substantial economic impact. Even though collecting and selling gum and resin is one of the main livelihood activities in Ethiopia’s remote pastoral areas, there are few case studies that adequately reflect national realities. Therefore, the goal of this study is to pinpoint and evaluate gum and resin production and sale, as well as any implications for pastoral livelihood, in the Adadle district of the Somali Region. Both purposive and random sampling techniques were employed. The primary data gathering technique used were household surveys, key informant interviews, focus groups, market surveys, direct observation, and surveys of community-based organizations. Multiple response tests, one-way ANOVA, and descriptive statistics were used in SPSS Version 26 to statistically analyze the collected data. The study discovered many Acacia, Boswellia, and Commiphora tree species that are employed in the production of gum and resin. It was also found that natural oozing and artificial tapping were the methods used for collecting and harvesting gum and resin. The average amount of gum and resin/frankincense harvested annually by each family was 219.82 kg and 58.27 kg, respectively, with gum harvesting being substantially higher ( p 0.05 ) than resin harvesting. The recurrent drought, season, and awareness level of harvesters on taping were the factors affecting the quantity and quality of production. The average annual household income from gum and resin/frankincense was 18,684 Ethiopian Birr and 43,704 Ethiopian Birr, respectively. The mean annual income from resin was substantially larger ( p 0.05 ) than that of gum. The gum and resin market chain in the area comprises eight actors with five market channels. The research also demonstrates that the income made by selling gum and resin was used to pay for basic necessities for the family, save money for health care and medications, and pay for social concerns and educational expenses. Recurrent drought, lack of market access and market expertise, lack of institutional cooperatives, and lack of infrastructure and facilities were all determined to be obstacles to the production and sale of gum and resin. Generally, resources should be conserved and interventions should be done to ensure the sustainability of the resource base and improve production potential. Proper tapping techniques should be introduced to enhance production potential, and access to market information should be ensured to maximize primary producers’ income share insured for maximizing the income share of primary producers.

PREREQUISITES FOR THE CREATION OF THE "FROM FARM TO SCHOOL" MODEL IN THE KLAIPEDA REGION: THE STUDY OF TARGET GROUPS’ EXPECTATIONS AND PERCEPTIONS

The focus of the article is on school meal programs that are common throughout the world and are implemented to promote students’ healthy eating habits and bring added value to their learning outcomes. In recent years there has been increasing emphasis on the possibility of improving school meals by including locally grown products, thus contributing to the development of local economic systems. This model has acquired the name "Farm to School" in practice of some countries and, according to its supporters, emphasizes public procurement of locally grown food as a key market opportunity for farmers. The article has been prepared within the BSR Food Coalition project (funded by Interreg Baltic Sea Region Program, contract #S002). The project seeks to create the conditions for the emergence of the "From farm to school" model in the Baltic States. The study presented in the article aims at disclosing the conditions and opportunities for the promotion and use of foods produced by local farmers in general education schools in Klaipeda region, Lithuania as well as at defining necessary educational efforts to increase healthy nutrition, develop general health habits, and agricultural and food system literacy within general education schools and their communities. To achieve the aim the surveys with project target groups (school administration, students and their parents) have been carried out in Klaipeda region. The objectives of the survey were to gather data on target groups’ perceptions of local school food procurement and to disclose their opinion, needs and expectations related to model development. The findings of the study would be helpful for designing the further steps of collaboration between schools and local farmers in Klaipeda region. .

Dynamic connectedness between green energy and carbon risk during Russia-Ukraine conflict: new evidence from a wavelet analysis.

The study explores the inter-relations betweengreen and renewable energy and carbon risk. Key market participants with varying time horizons include traders, authorities, and other financial entities. This research examines these relationships and frequency dimensions from February 7, 2017, to June 13, 2022, using novel multivariate wavelet analysis approaches, such as partial wavelet coherency and partial wavelet gain. The multiple coherencies between green bond, clean energy, and carbon emission futures imply that these regions were situated at low frequencies (relating to approximately 124-day frequency) and run from the beginning of 2017 to the beginning of 2018, in the first half of 2020, and from the beginning of 2022 to the end of the sample.The relationship between the solar energy index, envitec biogas, biofuels, geothermal energy, and carbon emission futures,is significant in the low-frequency band starting from early 2020 to middle 2022 and in the high-frequency band starting fromearly 2022 to middle 2022.Our research demonstrates the partial coherencies between these indicators during the Russia-Ukraine conflict. The partial coherency between the S&P green bond index and carbon risk suggests that carbon risk pushes anti-phase connectedness. The partial phase difference S&P global clean energy index and carbon emission futures (from early April 2022 to the end of April 2022) recommend that indicators are in-phase with carbon risk pushing and the phase (from early May 2022 to middle June 2022), suggesting that carbon emission futures are in-phase with S&P global clean energy index pushing.

30‐3: Open Standard Software Defined AV over IP Framework for LED Video Wall System †

The LED video wall market is in a growth phase as an alternative to LCD based large canvases. This paper contributes to industry body of knowledge on the opportunity to optimize the LED compute architecture for the mainstream market through key tenants of an open‐industry standard and commercial‐off‐the‐shelf product approach. With the LED market being in a growth phase this motivates a look at the compute architecture which drives such displays. A key adjacent market trend is how AV over IP is replacing domain specific protocols and more vendors adopt a software approach – both trends can be considered for application to the LED compute architecture. Four key challenges are summarized from a look at the current LED compute architecture and a template new architecture shown to explore answers to these challenges. The methodology of an incubation effort to explore this architecture is described leveraging the open‐source Intel® Media Transport Library to use SMPTE ST 2110 as a building block. Project findings are correlated back to the key challenges and reflect the tenants mentioned above. The next steps include practical considerations for product readiness.

Rising to the Vegan Challenge

The food and beverage sector is constantly rising to new challenges, not least the plant-based revolution which is now a key market player. But what does this mean for the pumps at the heart of the production process?