Market Drivers Research Articles

Investigating the management challenges of the EU Ecolabel through multi-stakeholder surveys

The EU Ecolabel is a voluntary scheme that aims to reduce the overall environmental impact of organisations to boost sustainable consumption in the marketplace and facilitate a transition towards a more circular economy. The main issues connected with the management of this scheme have been rarely investigated in literature. This study aims to analyse the main managerial and technical challenges linked with the EU Ecolabel such as drivers, barriers and benefits, uptake, monitoring, product portfolios and other criteria. We addressed this literature gap by administering multi-stakeholder surveys including consumers, Licence Holder’s and Non-Licence Holder’s companies, policy-makers and other main relevant organisations obtaining 442 responses. The analysis indicated the market drivers and benefits for Licence Holders, but recognised that increasing the marketing promotion of the scheme and boosting its synergies with other EU policies would attract Non-Licence Holders, which also claimed the narrowness of the EU Ecolabel portfolio as the main barrier for its adoption. Our results also confirmed the lack of demand for products bearing the EU Ecolabel, although stakeholders recognised a higher demand for environmentally friendly products. Our results pointed out the lack of a marketing/communication policy coordinated at the European level and the lack of an adequate monitoring system of the performance. Health/well-being products were recommended as main future EU Ecolabel products, while services and business to business products were not advised. Our findings provided new insights on the issues connected with the management of an ecolabelling scheme that can be useful for academics, practitioners and policy-makers. We also provided recommendations for policy makers to develop a heterogeneous (but not too broad) portfolio, with a focus on consumable goods with a health/well-being connotation, to strongly increase the promotion of the EU Ecolabel at all levels and to accelerate harmonisation with other EU policies, especially Green Public Procurement. Future research may investigate the EU Ecolabel awareness by investigating consumers’ perception in the EU context or may focus on regulatory reliefs that help to boost the adoption of the EU Ecolabel.

Sustainable development of global mineral resources market in Industry 4.0 context

The article explores the problems of sustainable development of the natural mineral resources market in the context of digital transformation of the industry. Key trends in the development of the global mineral resources market in economic, ecological, energy and digital dimensions have been identified. The features of the world distribution of raw material base of ore and energy mineral resources in terms of their economic assessment have been studied and the countries - market drivers - have been identified. A model for sustainable development of the mineral resources market and a system of criteria for its assessment in the context of Industry 4.0 have been proposed. In particular, the link between the level of digital technologies and the energy trilemma has been analysed. Based on the results of the calculations, the grouping of the leading countries in the mineral resources market by the level of achievement of sustainable development has been carried out and ways to digitize the mining industry have been outlined.

An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment

Stock market forecasting is an important research area, especially for better business decision making. Efficient stock predictions continue to be significant for business intelligence. Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices, moving averages, or daily returns. However, major events’ news also contains significant information regarding market drivers. An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market. This research proposes an efficient model for stock market prediction. The current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline, pharmaceutical, e-commerce, technology, and hospitality. We use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory (LSTM) model to improve stock prediction. The LSTM has the advantage of analyzing relationship between time-series data through memory functions. The performance of the system is evaluated by Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model.

Efficient Blockchain-Empowered Data Sharing Incentive Scheme for Internet of Things

In recent years, with many devices continuously joining the Internet of Things (IoT), data sharing as the main driver of the IoT market has become a research hotspot. However, the users are reluctant to participate in data sharing due to security concerns and lacking incentive mechanisms in the current IoT. In this context, blockchain is introduced into the data sharing of IoT to solve the trust problem of users and provide secure data storage. However, in the exploration of building a secure distributed data sharing system based on the blockchain, how to break the inherent performance bottleneck of blockchain is still a major challenge. For this reason, the efficient blockchain-based data sharing incentive scheme is studied for IoT. In the scheme, an efficient data sharing incentive framework based on blockchain is proposed, named ShareBC. Firstly, ShareBC uses sharding technology to build asynchronous consensus zones that can process data sharing transactions in parallel and deploy efficient consensus mechanisms on the cloud/edge servers and asynchronous consensus zones in sharding, thus improving the processing efficiency of data sharing transactions. Then, a sharing incentive mechanism based on a hierarchical data auction model implemented by a smart contract is presentedto encourage IoT users to participate in data sharing. The proposed mechanism can solve the problem of multi-layer data allocation involved in IoT data sharing and maximize the overall social welfare. Finally, the experimental results show that the proposed scheme is economically efficient, incentive-compatible, and real-time, with scalability, low cost, and good practicability.

Blockchain-Empowered Big Data Sharing for Internet of Things

A recent study predicts that by 2025, up to 75 billion internet of things (IoT) devices will be connected to the internet, in which data sharing is increasingly needed by massive IoT applications as a major driver of the IoT market. However, how to meet the interests of all participants in complex multi-party interactive data sharing while providing secure data control and management is the main challenge in building an IoT data sharing ecosystem. In this article, the authors propose a blockchain-empowered data sharing architecture that supports secure data monitoring and manageability in complex multi-party interactions of IoT systems. First, to build trust among different data sharing parties, the authors apply blockchain technologies to IoT data sharing. In particular, on-chain/off-chain collaboration and sharding consensus process are used to improve the efficiency and scalability of the large-scale blockchain-empowered data sharing systems. In order to encourage IoT parties to actively participate in the construction of shared ecology, the authors use an iterative double auction mechanism in the proposed architecture to maximize the social welfare of all parties as a case-study. Finally, simulation results show that the proposed incentive algorithm can optimize data allocations for each party and maximize the social welfare while protecting the privacy of all parties.

Mapping Out Market Drivers of Improved Variety Seed Use: The Case of Sorghum in Tanzania

Mapping Out Market Drivers of Improved Variety Seed Use: The Case of Sorghum in Tanzania

Tractor services challenges and current demand trends by smallholder farmers in Nigeria

Many smallholder farmers, particularly those in developing countries do not practice mechanisation such as the use of tractor due to some plethora of issues among which include: lack of demand, problematic supply model and failed mechanisation programmes. Based on the problems associated with smallholder mechanisation and the roles lack of demand played in past mechanisation programmes, this research investigated the characteristics and the challenges of tractor service providers, as well as the analyses of the current demand trends by the sampled smallholder farmers for tractor services, while forecasting the future demand in Delta and Benue States of Nigeria. The study employed a snowball sampling technique to collect field survey data from 20 private tractor service providers (SMEs) and 6 government tractor service providers in four local government areas of Delta and Benue States. Descriptive statistics, weighted mean score and trend analyses were used to interpret the result. The study finds that tractor services provide employment for rural dweller, particularly male; use less of household labour and the majority of those involved own only a tractor. Among the different challenges such as the inability of smallholders to pay a high price, long distance to get mechanic, and the high cost of maintenance/high cost of the spare part; too few qualified tractor mechanics is ranked the most severe challenge. While both SMEs and government tractor service providers face some common challenges, poor maintenance and corruption predominates government tractor services. There has been a constant increase in the demand for tractor services by the smallholder farmers from 2013 to 2016 and the demand is forecast to continue. Based on the different challenges associated with the provision of tractor services to the smallholder, it was suggested that the government should support market driver tractor service provision

Using a Stochastic Agent Model to Optimize Performance in Divergent Interest Tacit Coordination Games.

In recent years collaborative robots have become major market drivers in industry 5.0, which aims to incorporate them alongside humans in a wide array of settings ranging from welding to rehabilitation. Improving human–machine collaboration entails using computational algorithms that will save processing as well as communication cost. In this study we have constructed an agent that can choose when to cooperate using an optimal strategy. The agent was designed to operate in the context of divergent interest tacit coordination games in which communication between the players is not possible and the payoff is not symmetric. The agent’s model was based on a behavioral model that can predict the probability of a player converging on prominent solutions with salient features (e.g., focal points) based on the player’s Social Value Orientation (SVO) and the specific game features. The SVO theory pertains to the preferences of decision makers when allocating joint resources between themselves and another player in the context of behavioral game theory. The agent selected stochastically between one of two possible policies, a greedy or a cooperative policy, based on the probability of a player to converge on a focal point. The distribution of the number of points obtained by the autonomous agent incorporating the SVO in the model was better than the results obtained by the human players who played against each other (i.e., the distribution associated with the agent had a higher mean value). Moreover, the distribution of points gained by the agent was better than any of the separate strategies the agent could choose from, namely, always choosing a greedy or a focal point solution. To the best of our knowledge, this is the first attempt to construct an intelligent agent that maximizes its utility by incorporating the belief system of the player in the context of tacit bargaining. This reward-maximizing strategy selection process based on the SVO can also be potentially applied in other human–machine contexts, including multiagent systems.

Extended Market Drivers of Cip Deviations In Korea’s Cross-Currency Swap Markets

This study seeks to explore the peculiar impact of critical indicators: dollar index, oil price, sovereign credit default swap (CDS), stock market returns and Libor-OIS on the Korean won (KRW) cross-currency basis (CCB) at the maturity of 1-and- 5-years by segregating the timeframe from March 2003 to September 2019 into four periods: before the global financial crisis (GFC), during GFC, during eurozone crisis and after the crisis. Hence, for this time series data, simple linear regression with ARMA errors is employed to determine the direction and magnitude of regressors’ impact on KRW as well as to deal with possible heteroskedasticity and serial correlation issues. Vector autoregression (VAR) is used to obtain forecast error variance (FEV) decomposition to ascertain the dynamic relationship and predictive influence of these variables, especially in the advent of shocks. It is affirmed that CDS, stocks and Libor-OIS were most significant during GFC. Also, all regressors were relatively significant for the overall dataset with oil having the least impact.

Current (2020) and Long-Term (2035 and 2050) Sustainable Potentials of Wood Fuel in Switzerland

Wood fuel has become central in environmental policy and decision-making processes in cross-sectoral areas. Proper consideration of different types of woody biomass is fundamental in forming energy transition and decarbonization strategies. We quantified the development of theoretical (TPs) and sustainable (SPs) potentials of wood fuel from forests, trees outside forests, wood residues and waste wood in Switzerland for 2020, 2035 and 2050. Ecological and economic restrictions, timber market situations and drivers of future developments (area size, tree growth, wood characteristics, population growth, exporting/importing (waste wood)) were considered. We estimated a SP of wood fuel between 26.5 and 77.8 PJ/a during the three time points. Results demonstrate that the SP of wood fuel could be significantly increased already in the short term. This, as a moderate stock reduction (MSR) strategy in forests, can lead to large surpluses in SPs compared to the wood fuel already used today (~36 PJ/a), with values higher by 51% (+18.2 PJ) in 2020 and by 59% (+21.3 PJ) in 2035. To implement these surpluses (e.g., with a cascade approach), a more circular economy with sufficient processing capacities of the subsequent timber industries and the energy plants to convert the resources is required.

Drivers of the electric vehicle market: A systematic literature review of empirical studies

This study reviews the extant literature in the field of electric vehicle (EV) adoption that has employed statistical approaches to analyse actual market data. Thereby, the focus of the review is laid on the applied statistical approaches and investigated variables. The analysis reveals that a variety of statistical approaches have been applied. The most common models being in form of panel data to overcome the lack of observations. For the investigated variables it was found that they can be divided into seven categories, whereby a lack of research was identified for psychological factors.

Photonic and terahertz applications as a next driver of gallium arsenide market

Analysis of current GaAs and related device market initiated in a number of earlier works has been continued. Binary semiconductor GaAs compound is a conventional MW electronics material. Until recently GaAs based HF ICs for mobile phones were among the most rapidly growing segments of GaAs market. However the GaAs market development trend is changing. Photonics and TeraHertz engineering are becoming the new world GaAs market drivers. This means that the current emphasize of GaAs single crystal technologies will shift toward vertical directional crystallization of “optoelectronic quality” crystals. In the medium and longer terms the world GaAs wafer and epitaxial structure markets will continue growing. In the shorter term we all will have to take into account COVID epidemic consequences. Still the GaAs market is closely related to Smartphone market novelties. Quite probably after a long growth period the GaAs market will keep on shrinking for the second consecutive year: GaAs production may decline by 11–12 % in 2020. Assuming that the epidemic will be somehow taken under control in 2021 the overall Smartphone production can probably be expected to grow starting from 2021.Currently the Russian market of semiconductor compounds for photonics and electronic components (GaAs etc.) is but moderate and in predictable terms is not expected to achieve a level that is required for the emergence of a competitive domestic manufacturer, even though all importation replacement programs are accomplished. Meanwhile there is understanding that developing an advanced electronic components industry in Russia requires larger production of source materials.

Cost-effectiveness of mussel farming as a water quality improvement measure: Agricultural, environmental and market drivers

Abstract This study aims to understand the economic and bio-physical conditions under which mussel farming is a cost-effective mitigation measure to improve water quality related to excess nitrogen in fjords and coastal areas. We set-up a mixed-integer optimization model including every farm in three agricultural catchments surrounding Limfjorden, the largest fjord in Denmark. We include a number of relevant nitrogen abatement measures, including agricultural land-use measures and mussel farming in the sea. The aim is to model the least costly combination of mitigation measures to improve water quality when agricultural, environmental and market conditions vary. We run three scenarios varying environmental conditions for mussel productivity and market opportunities for mussel-based products as organic animal feed. We analyze the resulting marginal abatement costs and draw insights about the potential scale of mussel farming for the different catchments. We show that mussel farming is a cost-effective option for 2 of the 3 catchments, but that decreasing mussel productivity over time may make the measure ineffective for one of the catchments, if a market for feed is not available. The possibility of a market for mussel-based organic feed significantly increases the share of nitrogen reduction done by mussels and decreases overall costs by up to 65%. Ultimately, the results indicate that, for catchments where environmental conditions are adequate, mussel farming can be a cost-effective nutrient reduction measure. Therefore, mussel farming can potentially increase the cost-effectiveness of incentive schemes aimed at reducing eutrophication in fjords and coastal waters.

Cross-country experiences and lessons in nutraceutical health marketing in the United States, China, and India

The distinction between food and drugs has blurred in recent years with a new — and novel — health paradigm representing a hybrid of “nutrition” and “pharmaceuticals.” Nutraceuticals are formulated or processed to satisfy dietary requirements arising from physical or physiological conditions, and to treat certain diseases. This study examines the complex interface between the regulatory framework and nutraceutical marketing by drawing from the experiences of three leading global producers and markets: the United States, China, and India. It identifies opportunities in leveraging positive drivers of health marketing and the risks and challenges to market players, particularly in relation to product safety and effectiveness. Findings indicate that: 1) marketing incentives can stem from the regulatory framework; 2) regulation can yield adverse, albeit unintended, consequences on marketing activity; 3) containment or punishment of undesirable market behavior does not necessarily support the legitimate and desirable ends of health marketing; 4) uncommunicated differences between prescription nutraceutical products and nutraceutical supplements breed public confusion and misinformation, and encourage self-medication; and 5) health marketing may be effectively channeled to act as an agent of compliance and positive change. Practical implications and recommendations drawn from the comparative experiences of the three surveyed countries are addressed by way of conclusion.

Photonic and Terahertz applications as the next gallium arsenide market driver

Analysis of current GaAs and related device market initiated in a number of earlier works has been continued. Binary semiconductor GaAs compound is a conventional MW electronics material. Until recently GaAs based HF ICs for mobile phones were among the most rapidly growing segments of GaAs market. However the GaAs market development trend is changing. Photonics and Terahertz engineering are becoming the new world GaAs market drivers. This means that the current emphasize of GaAs single crystal technologies will shift toward vertical directional crystallization of “optoelectronic quality” crystals. In the medium and longer terms the world GaAs wafer and epitaxial structure markets will continue growing. In the shorter term we all will have to take into account COVID epidemic consequences. Still the GaAs market is closely related to Smartphone market novelties. Quite probably after a long growth period the GaAs market will keep on shrinking for the second consecutive year: GaAs production may decline by 11–12% in 2020. Assuming that the epidemic will be somehow taken under control in 2021 the overall Smartphone production can probably be expected to grow starting from 2021. Currently the Russian market of semiconductor compounds for photonics and electronic components (GaAs etc.) is but moderate and in predictable terms is not expected to achieve a level that is required for the emergence of a competitive domestic manufacturer, even though all importation replacement programs are accomplished. Meanwhile there is understanding that developing an advanced electronic components industry in Russia requires larger production of source materials.

Plastics in the US: toward a material flow characterization of production, markets and end of life

Managing plastics has become a focal issue of the Anthropocene. Developments in plastic materials have made possible many of the technologies and conveniences that define our modern life. Yet, plastics are accumulating in landfills and natural environments, impacting resource utilization and ecosystem function. Solutions to these rising problems will require action and coordination across all stages of plastics value chains. Here, we offer the first contemporary plastics material flow by resin type through the US economy, encompassing 2017 production, sales, use markets and end-of-life management. This roadmap, while sourced from disparate and incomplete data, provides stakeholders with a system-scale context for understanding challenges, opportunities and implications of future interventions. More than three-quarters of the plastics reaching end of life went to landfill, and less than 8% was recycled. Packaging was the largest defined use market for plastics, but two thirds of the plastic put into use in 2017 went into other markets, including consumer products, electronics, buildings and transportation. In nearly all uses, increased coordination between material and product innovation and design and end-of-life recovery and recycling are needed. Alignment of technology, policy and market drivers will be necessary to reduce plastic waste and improve the circularity of plastic materials.

Intelligent RAN Slicing for Broadband Access in the 5G and Big Data Era

As the 5G pace is quickening, enabled by rapid coverage rollout and availability of devices, the fixed broadband market is poised to become more competitive. We envision that 5G NS can emerge as a game changer that accelerates the development of FMC while counterintuitively opening up opportunities for fixed operators and the like in need of higher access network capability by leveraging NS-assisted FMC. In this article, we present an architectural framework to foster cross-industry infrastructure collaboration from a RAN slicing perspective with the aim of reducing network costs and improving rollout efficiency in the broadband ecosystem. Under this framework, RAN slicing can turn wireless access into an anchor service offering that can be exploited by not only existing mobile operators with licensed spectrum, but by a host of slice tenants including fixed wireless access startups, digital subscriber line operators, and converged operators. In the latter instances, independent RAN slices can be instantiated and granularly scaled to support these tenants who may not own mobile infrastructure or spectrum, while maximizing reuse of their live networks. Following a discussion on the market drivers and rationales for RAN slicing, we outline some RAN slicing options for different operators to realize resource isolation and allocation. Furthermore, a big data-driven, artificial-intelligence- powered RAN slicing and resource management framework is introduced for enabling RAN automation. Finally, several future challenges are identified.

Stock market drivers of retail investors’ sentiment – facets and new evidences from India

Purpose The determinants of investors’ sentiment based on secondary stock market proxies in many empirical studies are reported. However, to the best of our knowledge, no study undertakes investor sentiment drivers developed from primary survey measures by constructing an investor sentiment index (ISI) in relation to market drivers to date. This study aims to fill this research gap by first developing the ISI for the Indian retail investors and then examining which of the stock market drivers impacts such sentiment. Design/methodology/approach The ISI is constructed using the mean scores of eight statements as formulated based on popular direct investor sentiment surveys undertaken across the world. Then, we use the multiple regression approach overall and for top 33.33% (high-sentiment) and bottom 33.33% (low-sentiment) investors based on the responses of 576 respondents on 18 statements (proxying eight study hypotheses) collected in 2016. Moreover, the demography-based classification based investors’ sentiment is examined to make our results more robust and in-depth. Findings On an overall basis, the IPO activities/issues and information certainty, trading volume and momentum and institutional investors’ investment activities market drivers significantly and positively impact retail investors is examined. However, only IPO activities/issues and information certainty influences both high- and low-sentiment investors. It is intriguing to report that nature of the stock markets show conflicting results for high- (negative significant) and low- (positive significant) sentiment investors. Originality/value The construction of the ISI from primary survey measure is for the first time in Indian context in relation to investigating the stock market drivers influential to retail investors’ sentiment. In addition, hypothesized market drivers are also unique, each representing different fundamental and technical characteristics associated with the Indian market.

Analysis of print packaging market drivers

Analysis of print packaging market drivers

Modelling the drivers of the UK multi-let industrial property market: an exploratory study

PurposeMulti-let industrial (MLI) estates are an emerging £15 billion UK real estate asset class that can offer attractive returns, a diversified income base, constrained supply and extensive management opportunities to add value within an operational platform. This investment appeal is supported by the evolving MLI occupier market with the growth of small to medium enterprises (SME) requiring modern urban business space driven in part by technology advances offering new streams of supply chain connectivity between businesses and potential clients at a local level.Design/methodology/approachTo understand more about MLI properties, this study utilises a hedonic pricing model to quantify property values as a function of defined variables. The dataset used for this research is a sample portfolio of 26 multi-let industrial properties. The dataset was analysed alongside eleven physical, financial and locational factors. Interestingly, the hedonic pricing model results showed that only four characteristics are value-affecting across the selected properties: namely (1) Granularity of the property income, (2) Distance from the nearest motorway, (3) Distance to the nearest town centre and (4) Gross internal floor area. A chi–test confirmed that there was no significant difference between the modelled values and the supplied property valuations.FindingsThis preliminary study offers valuable insight into MLI property market drivers and could easily form a simple decision-making tool to examine potential MLI opportunities in this developing real estate asset class.Originality/valueIn detailing these key MLI property features, current research is limited and focused primarily on market commentary. New knowledge on the MLI property market can provide a platform creating interesting opportunities for fund managers with an intensive management engagement strategy.