Search Results (1,527)

This research paper aims to investigate the impact of ownership concentration, insider ownership, and board size on employee productivity for 136 Jordanian public shareholding firms listed on the Amman Stock Exchange (ASE) from 2012 to 2021. Ownership concentration has been measured by Herfindahl–Hirschman Index (HHI), whereas insider ownership and board size have been represented as the proportion of shares held by insiders and by the number of board members, respectively. Lastly, employee productivity has been measured using a data envelopment analysis (DEA) tool. We employed ordinary least squares regression (OLS) including firm-year-fixed effects. Our empirical results indicate a non-linear relation between ownership concentration and employee productivity, whereby the productivity of employees increases in firms with a proportion of ownership concentration less than 60%. In addition, we found a non-linear relation between insider ownership and employee productivity, whereby the productivity of employees increases in firms with proportion of insider ownership less than 50%. Moreover, we found a non-linear relation between board size and employee productivity, whereby the productivity of employees increases in firms that have less than 11 board members. Our outcome contributed to the knowledge found in the previous literature, as it is the first to highlight the productivity of employees in emerging economies, such as the economy in Jordan. Furthermore, our findings could be useful for the Jordan Securities Commission (JSC) and the ASE on their continuous process to improve and develop corporate governance instructions. Full article

Gansu Province in China has the characteristics of an underdeveloped economy, low forest carbon sink, and rich non-fossil energy, making it a typical area for research to achieve the “double carbon” target. In this paper, the primary energy consumption and carbon emissions and their development trends in Gansu Province during the “double carbon” target period were predicted by the fixed-base energy consumption elasticity coefficient method, and the possibility of achieving the “double carbon” target in Gansu Province was explored. In the three hypothetical scenarios, it was estimated that the total primary energy consumption of Gansu Province will be 91.9–94.81 million tons of standard coal by 2030 and 99.35–110.76 million tons of standard coal by 2060. According to the predicted share of different energy consumption in Gansu Province, the CO₂ emissions of Gansu Province in the three scenarios were calculated and predicted to be between 148.60 and 153.31 million tons in 2030 and 42.10 and 46.93 million tons in 2060. The study suggests that Gansu Province can reach the carbon peak before 2030 in the hypothetical scenarios. However, to achieve the goal of carbon neutrality by 2060, it was proposed that, in addition to increasing carbon sinks by afforestation, it is also necessary to increase the share of non-fossil energy. As long as the share is increased by 0.3% on the basis of 2030, the goal of carbon neutrality by 2060 in Gansu Province can be achieved. The results show that the increase in the share of non-fossil energy consumption is the most important way to achieve the goal of carbon neutrality in Gansu Province, and it also needs to be combined with the optimization of industrial structure and improvement of technological progress. Based on the research results, some countermeasures and suggestions are put forward to achieve the goal of carbon neutrality in Gansu Province. Full article

Under the background of the Energy Internet and the shared economy, it is of great significance to explore the collaborative planning strategies of multi-energy microgrids (MEMGs) and a shared energy storage operator (SESO) supported by shared energy storage resources. In this context, a joint planning method of SESO and MEMG alliances based on a dynamic game with perfect information is proposed in this paper. First, an upper-level model for energy storage capacity configuration and pricing strategy planning of SESO is proposed to maximize the total planning and operational income of SESO. Then, a lower-level model for the optimal configuration of MEMGs’ alliance considering SES is proposed to minimize the total planning and operational costs of the MEMG alliance. On this basis, a solving algorithm based on the dynamic game theory with perfect information and the backward induction method is proposed to obtain the Nash equilibrium solution of the proposed bi-level optimization models. Finally, a case study with one SESO and an alliance consisting of five MEMGs is conducted, and the simulation results show that the proposed bi-level optimization method can increase SESO’s net income by 1.47%, reduce the average planning costs for each MEMG at least by 1.7%, and reduce model solving time by 62.9% compared with other counterpart planning methods. Full article

Background: The on-demand warehousing (ODW) model is emerging as a platform-enabled logistics solution based on resource sharing for businesses needing storage space. It is based on a business-to-business marketplace where companies can provide (or request) storage services with an elevated level of flexibility. The literature focusing on this topic is still scarce, and while the potential advantages of such a model seem quite clear, challenges and criticalities need to be further explored. Methods: Starting from a state-of-the-art analysis of ODW, a two-step methodology was adopted: first, a SWOT analysis was conducted to help summarize the challenges related to this emerging model. Then, an exploratory analysis of multiple case studies was employed to provide a first discussion on the advantages and criticalities of this model, highlighting its latest evolution. Results: The ODW model is still evolving, as several former pure ODW platforms have been changing their business model to become on-demand 4PLs (defined as “mixed ODW-4PLs”), adapting their core activities to manage the criticalities of on-demand services. Conclusions: This study represents the first attempt to investigate benefits and criticalities of ODW models, outlining the latest trend of ODW and identifying two distinct types of ODW model currently present on the market. Full article

This study examines the interplay between Airbnb and gentrification in Thessaloniki and Greece, focusing on their economic and social impacts on urban neighborhoods. Utilizing data from 110 online publications and qualitative insights from ten semi-structured interviews with real estate agents, Airbnb stakeholders, residents, and experts, the research provides a nuanced view of these dynamics. The findings suggest that Airbnb influences housing markets by driving up rental and home prices, potentially exacerbating housing scarcity and displacing vulnerable populations in gentrifying areas. While this aligns with the existing literature, the results remain tentative due to the complexities involved. The trend toward corporate-hosted short-term rentals appears to shift Airbnb away from its original community-focused model, though this shift is still evolving. The COVID-19 pandemic introduced changes, such as a move from short-term to long-term rentals and the conversion of commercial spaces to residential use, impacting neighborhood dynamics. However, these effects may be temporary and do not fully address broader housing issues. While an oversupply of Airbnb accommodations might stabilize rental prices to some extent, its impact on the overall housing crisis remains uncertain. Future research should investigate the long-term effects on housing affordability and social equity, considering the limitations of current findings. Full article

The vigorous development of the digital economy, alongside the collaborative promotion of enterprise digital transformation and low-carbon supply chains, has emerged as a critical pathway for achieving green and high-quality development in enterprises. In this paper, we utilize a mathematical model framework to empirically investigate the mechanisms and impacts of enterprise digital transformation on the low-carbon effect of supply chains, employing data from A-share-listed companies spanning 2011 to 2021. The findings indicate that (1) enhancing the degree of enterprise digital transformation can significantly decrease the carbon emission intensity of upstream suppliers, thereby promoting low-carbon supply chains. (2) “Innovation-driven” and “structural transformation” mechanisms are vital channels by which enterprise digital transformation promotes carbon reduction in supply chains. (3) The diffusion mechanism effect and demonstration effect exhibit heterogeneity in the process of enterprise digital transformation, driving low-carbon emission reductions in supply chains. Full article

Energy trade balance has been the main factor behind current account imbalances in many developed and developing countries. This study investigates whether or not renewable energy conveys information to the current account deficit of selected OECD countries. Utilizing a dataset spanning from 1990 to 2021, we apply a Panel Autoregressive Distributed Lag (ARDL) estimator to determine the interrelation of current account deficit (CAB) as a percentage of GDP with selected indicators, namely, net energy import in total final energy consumption (NEI), the share of renewable energy in total electricity production (REN_TEO), and fiscal deficit as a percentage of GDP (FAB). The results of long-term estimations reveal that as net energy import increases, the current account deficit deteriorates. On the other hand, in the case that countries utilize more of renewable energy in their total electricity generation, their current account deficits improve. Thus, we conclude that energy policy matters for the current account balances and subsequently for the well-being of OECD economies. Finally, we find strong evidence for the twin deficit hypothesis, as fiscal deficit is negatively interrelated with current account deficit both in the short-run and long run. In other words, an increase in the level of budget deficit is associated with an upsurge in the current account deficit problem. Furthermore, the Dumitrescu-Hurlin causality test reveals that there is bidirectional heterogeneous causality between current account deficit and budget deficit. Additionally, when the countries in the sample are grouped by their per capita GDP levels, estimations reveal that the direction of interaction between CAB and energy-related indicators (NEI and REN_TEO) does not differ between Group 2 (the ones whose per capita incomes are over USD 25,000 but below USD 50,000) and Group 3 (the ones having more than USD 50,000 per capita income) countries. However, the coefficients of energy-related indicators for Group 2 countries are higher than those of Group 3 ones, suggesting that energy policy matters more for Group 2 countries’ current account imbalances in the long-term. Full article

Due to the continuous growth of global energy demand and the urgent pursuit of sustainable development goals, renewable energy development has become a vital strategy to deal with energy challenges and environmental issues. Renewable energy generation efficiency (REGE) around the world has begun to be examined, and ambitious goals with a sense of mission within a predetermined timeline have been set. The goal of this paper is to use the dynamic slacks-based measure (DSBM) data envelopment analysis (DEA) method to obtain the REGE for 44 Asian economies from 2010 to 2021. This paper also uses Tobit regression analysis to explore the factors that may affect the REGE. The empirical results indicate that the REGE in 17 economies reached the efficiency target during this period. When classified by income level, differences in average REGE are observed among high-income, upper-middle-income, lower-middle-income, and low-income economies. Additionally, differences in average REGE exist between tropical and temperate economies when classified by geographic latitude. Furthermore, through the Tobit regression model, we determine that information digitalization, financial openness, technological innovation ability, and renewable energy device capacity share all have significant positive effects on REGE, but life quality and democracy degree have significant negative impacts on REGE. Moreover, it has been found that the REGE scores of Asian economies exhibit a status similar to the middle-income trap. The outcome of the research provides Asian governments and those middle-income economies with ways to enhance REGE. Due to data limitations, this study cannot estimate the convergent solution based on the data of the research sample, and a new advanced Panel Tobit model is required. Full article

We propose a composite index to measure and benchmark community participation in cross-border tourism development processes. The index synthesizes information regarding three dimensions of this construct, deemed as very important by the extant literature: residents’ engagement in the planning process and willingness to proactively welcome tourists and provide tourist services directly through sharing-economy platforms. The latter aspect is crucial to develop a local tourist supply able to combine environmental sustainability and financial feasibility in marginal areas, where public funding is scarce and private investments are unprofitable. This study offers a methodological innovation using response rates to open-ended questions to measure residents’ engagement in tourism planning. By applying the ELECTRE III algorithm, which is non-compensatory and ensures reliability in the presence of a high degree of uncertainty, survey information is aggregated in a single figure, which can be easily interpreted by destination managers and policymakers. After COVID-19, in readying for the next pandemic, decision makers should find our index as a very relevant and useful tool for tourism recovery and innovation planning, including compliance with measures to prevent the spread of future infections. We apply the proposed index to ten Croatian and Italian lands involved in a European development project. Data were collected through face-to-face interviews with residents, according to an availability sampling design. We obtained 879 valid questionnaires. The robustness of the resulting index is tested through an uncertainty and a sensitivity analysis. Full article

The textile industry, a substantial component of the global economy, holds significant importance due to its environmental impacts. Particularly, the use of water and chemicals during dyeing processes raises concerns in the context of climate change and environmental sustainability. Hence, it is crucial from both environmental and economic standpoints for textile factories to adopt green industry standards, particularly in their dyeing operations. Adapting to the green industry aims to reduce water and energy consumption in textile dyeing processes, minimize waste, and decrease the carbon footprint. This approach has become crucial in achieving sustainability in textiles following the signing of the Paris Climate Agreement. Important elements of this transformation include the reuse of washing waters used in the dyeing process, the recycling of wastewater, and the enhancement of energy efficiency through necessary methodological and equipment changes. This study analyzes the energy, labor, production, and consumption data since 2011 for a textile factories with four branches located in the Adana Organized Industrial Zone. Among these factories, the one designated as UT1, which has the highest average energy and water consumption compared to the other three branches, is selected. In recent years, the use of artificial intelligence and machine learning technologies in predicting industrial processes has been increasingly observed. The data are analyzed using LSTM (Long Short-Term Memory) and ANN (Artificial Neural Networks) forecasting methods. Particularly, the LSTM algorithms, which provided the most accurate results, have enabled advanced forecasting of electricity consumption in dyeing processes for future years. In 2020, electricity consumption was recorded as 3,717,224 kWh and this consumption was reflected in the total energy cost as TRY 1,916,032. Electricity consumption accounts for 22.34% of total energy consumption, while the share of this energy type in the cost is 43.25%. In the light of these data, the MAPE value for energy consumption forecasts using the LSTM model was 0.45%, which shows that the model is able to forecast with high accuracy. As a result, a solar power plant was installed to optimize energy consumption, and in 2023 60% energy savings were achieved in summer and 25% in winter. The electricity consumption forecasting results have been an essential guide in planning strategic initiatives to enhance factory efficiency. Following improvement efforts aimed at reducing energy consumption and lowering the carbon footprint, significant optimizations in processes and layouts have been made at specific bottleneck points within the facility. These improvements have led to savings in labor, time, and space, and have reduced unit production costs. Full article

In the competitive landscape of Airbnb hosting, optimizing pricing strategies for properties is a complex challenge that requires revenue maximization with high occupancy rates. This research aimed to introduce a solution that leverages big data and machine learning techniques to help hosts improve their property’s market performance. Our primary goal was to introduce a solution that can augment property owners’ understanding of their property’s market value within their urban context, thereby optimizing both the utilization and profitability of their listings. We employed a multi-faceted approach with diverse models, including support vector regression, XGBoost, and neural networks, to analyze the influence of factors such as location, host attributes, and guest reviews on a listing’s financial performance. To further refine our predictive models, we integrated natural language processing techniques for in-depth listing review analysis, focusing on term frequency-inverse document frequency (TF-IDF), bag-of-words, and aspect-based sentiment analysis. Integrating such techniques allowed for in-depth listing review analysis, providing nuanced insights into guest preferences and satisfaction. Our findings demonstrated that AirBnB hosts can effectively utilize both state-of-the-art and traditional machine learning algorithms to better understand customer needs and preferences, more accurately assess their listings’ market value, and focus on the importance of dynamic pricing strategies. By adopting this data-driven approach, hosts can achieve a balance between maintaining competitive pricing and ensuring high occupancy rates. This method not only enhances revenue potential but also contributes to improved guest satisfaction and the growing field of data-driven decisions in the sharing economy, specially tailored to the challenges of short-term rentals. Full article

As the organizational environment becomes more volatile, uncertain, complex, and ambiguous, and the economy becomes increasingly knowledge-based, organizational knowledge management is key for companies’ success. This is especially important as organizational ties are weaker and job-hopping becomes a more prevalent phenomenon. As human resource mobility increases, companies must ensure that knowledge remains within the company despite employee exit. In this context, the current study sought to understand how leaders’ actions can facilitate employee knowledge sharing, focusing on paradoxical leadership. Besides examining the impact of paradoxical leadership on employees’ propensity to adopt knowledge-sharing behaviors, this study also explored the effects of one potential intervening variable (i.e., promotive voice behavior) and one potential boundary condition (i.e., trust in the leader) on this relationship. A two-wave time-lagged correlational study was conducted with a sample of 154 workers from various sectors. The results of moderated mediation analysis suggest that paradoxical leaders indirectly promote greater knowledge-sharing among subordinates by fostering their promotive-voice behaviors, but only for those with high levels of trust in the leader. The implications of these findings for current organizational challenges regarding knowledge management are discussed. Full article

There are various types of sharing economy services, such as ride-sharing and shared-taxi rides. Motivated by these services, we consider a single-server queue in which customers probabilistically select the type of service, that is, the single service or batch service, or other services (e.g., train). In the proposed model, which is denoted by the M+M(K)/M/1 queue, we assume that the arrival process of all the customers follows a Poisson distribution, the batch size is constant, and the common service time (for the single- and batch-service customers) follows an exponential distribution. In this model, the derivation of the sojourn time distribution is challenging because the sojourn time of a batch-service customer is not determined upon arrival but depends on single customers who arrive later. This results in a two-dimensional recursion, which is not generally solvable, but we made it possible by utilizing a special structure of our model. We present an analysis using a quasi-birth-and-death process, deriving the exact and approximated sojourn time distributions (for the single-service customers, batch-service customers, and all the customers). Through numerical experiments, we demonstrate that the approximated sojourn time distribution is sufficiently accurate compared to the exact sojourn time distributions. We also present a reasonable approximation for the distribution of the total number of customers in the system, which would be challenging with a direct-conventional method. Furthermore, we presented an accurate approximation method for a more general model where the service time of single-service customers and that of batch-service customers follow two distinct distributions, based on our original model. Full article

One new factor driving the banking industry towards long-term, high-quality growth is digital marketing, which has arisen within the framework of the digital economy. The purpose of this research is to examine the effect of digital marketing on the financial results of MENA banks from 2010 to 2023. The research examines the impact of digital marketing techniques on the effectiveness of financial institutions through Tobit regression analysis, taking into account and controlling for sustainable practices (ESG), bank-specific characteristics (capital adequacy, bank size, liquidity, and cost efficiency), and macroeconomic variables (GDP and inflation). This empirical paper managed to collect the data for eleven countries in the MENA from the Refinitiv Eikon platform, world bank database, and the annual reports of relevant banks in the different stock markets. The final sample included 78 banks out of 120 listed banks. The results show that there is a clear association between the presence of digital marketing campaigns and improved profitability and market share growth for banks. Aligning digital initiatives with ESG principles is crucial for long-term value development, and sustainable practices increase these beneficial benefits even more. The research also shows that macroeconomic factors and bank-specific characteristics affect how effective digital marketing campaigns are. The significance of digital transformation and ESG integration in promoting competitive advantages and long-term growth in the MENA banking sector is highlighted by these findings, which have important implications for policy, investors, and bank executives. Full article

The sharing economy is beneficial for sustainable development. It effectively utilizes underused resources and reduces unnecessary production, consumption, and waste through resource sharing. This study investigates the factors that can increase consumers’ perceived sustainability of a sharing economy service and examines the impact of perceived sustainability on their intentions to continue using the service. Furthermore, the study considers the moderating effect of perceived green transparency. Internet surveys are conducted to collect responses from users of a transportation service (Uber) and an on-demand logistics service (Uber Eats). This study provides suggestions for service providers in the sharing economy to develop sustainability strategies. Full article