Shifts In Demand Research Articles

Augmenting last-mile connectivity with multimodal transport: Do choice riders favor integrated bike taxi-bus service in metro cities?

While the pursuit of sustainable measures to arrest private vehicle usage and externalities continues, poor last-mile connectivity remains a major deterrent for choice riders (car owners) considering the use of public transport. The present study proposes an integrated Bike Taxi-Bus Service (BTBS) system as a solution to last-mile connectivity concerns and investigates the perception of choice riders towards BTBS. The research was carried out in two Indian metro cities, tier 1 (Bangalore) and tier 2 (Kochi), where bike taxi and bus services operate independently, albeit without integration. The study investigates the perception of choice riders in two stages. Firstly, to identify key intervention areas for the development of an integrated BTBS system. Secondly, to develop econometric models for the estimation of choice riders’ Willingness to Pay (WTP) for BTBS attributes and their potential shift in demand towards BTBS. Additionally, a sensitivity analysis was conducted to explore the influence of various attributes on demand shift and to establish appropriate attribute levels to attract choice riders. While intervention areas are identified using Revised Importance Satisfaction Analysis (Revised ISA), Mixed Logit (ML) models are developed to estimate demand and WTP values. Unlike previous studies, intervention areas are identified using factor structure and management schemes. The study demonstrates the application of Revised ISA in attribute selection for econometric modelling. The work identified 50% of the listed attributes as intervention areas and notably higher WTP values of choice riders for BTBS attributes. This indicates the promise of BTBS while emphasizing the need for urgent attention.

Automation and disappearing routine occupations in Japan

We examine the implications of automation technology in Japan since 1980, comparing different local labor markets with different degrees of automation exposure. First, we do not find evidence that automation reduces employment rate within demographic groups and automation encourages workers to move from regular to non-regular employment. Second, we show that automation shifts employment from routine occupations in the manufacturing sector to service sectors, while increasing the share of establishments and sales in the manufacturing sector. Finally, we show that this shift in labor demand is attributed to younger generations and non-college-educated workers.

The Impact of COVID-19 Pandemic on the Banking Industry and Its Customers

, including customer demand, digital transformation, risk management methods, and overall financial performance. Focusing on shifts in client demand for loans and financial products, the rise of digital banking, and banks' reactions to increased financial risks, this study examines how the pandemic affected conventional banking operations and customer interactions. The goals of the research are to look at how banks changed their services throughout the pandemic, how they dealt with risk, and how well they did financially. The research relies on secondary data analysis that scours academic journals, government documents, business reports, and bank records. Findings highlight a sharp drop in loan demand in the early phases of the epidemic, followed by a dramatic increase in demand for mortgage refinancing and corporate loans supported by the government. Online and mobile banking saw a meteoric rise in popularity as more and more clients made the switch. Lending restrictions and provisions for non-performing loans were tightened by banks in response to financial instability, which had a detrimental impact on their profitability. Better digital banking infrastructure, more robust risk management frameworks, and more adaptable financial products that can react to crises more effectively are some of the long-term consequences that the study emphasizes for the banking sector. At its end, the paper suggests ways financial institutions might be better ready for future disruptions and urges additional investigation into how fintech will play a role and how regulations will alter in the banking industry after a pandemic.

Optimizing Strategy for Property Development Plan Changes: A Multi-Criteria Decision-Making Approach Using AHP Methods (Case Study: Project Bumi Cipta 3 Jakarta)

This study examines the optimization strategy of property development plan changes for Bumi Cipta 3 Jakarta, a project by PT Cipta Bangun Property, one of the largest property companies in Indonesia. The analysis uses the Analytic Hierarchy Process (AHP), a multi-criteria decision-making (MCDM) method. The project is motivated by significant post-pandemic shifts in office space demand within Jakarta’s Central Business District. The study identifies four alternative development options: continuing with the office tower plan, or pivoting to an apartment, data center, or hospital. By interviewing key decision-makers and using the AHP method, the research evaluates financial and non-financial criteria, such as market attractiveness and site analysis. The findings suggest that the hospital alternative offers the highest potential in both financial returns and market alignment, followed by the data center and apartment projects. Strategic recommendations are provided to prioritize the hospital project while keeping the other alternatives in consideration for phased development, thus ensuring flexibility in response to future market conditions.

IDENTIFIKASI FAKTOR DAN TANTANGAN DI SEKTOR PERUNGGASAN UNTUK MENINGKATKAN KELINCAHAN BISNIS DI AGROINDUSTRI INDONESIA: TINJAUAN LITERATUR

The poultry industry in Indonesia, especially the poultry sector, plays a crucial role in ensuring national food security by providing essential animal protein sources. Therefore, business agility is highly necessary to address fluctuating demand issues that can change unpredictably. Many industries in this sector have faced setbacks due to unforeseen shifts in demand. However, in implementing business agility, numerous factors need consideration, such as traditional organizational structures, bureaucratic processes that impede change, and a corporate culture resistant to transformation. This research aims to identify various challenges faced by companies in achieving business agility through a literature review using Publish or Perish software and manual searches on Google Scholar, resulting in the selection and analysis of 23 relevant journals. The findings from the literature review indicate that effective supply chain management, innovative management leadership, technological automation, adaptability to unforeseen events, investment in employee development, and a deep understanding of customer needs are key factors influencing business agility in the poultry industry in Indonesia.

Supply Chain Efficiency and Agricultural Product Sales: A Comparative Study of 2022 and 2023 Trends in USA

This comparative study analyzes supply chain efficiency and agricultural product sales trends in the USA between 2022 and 2023. Utilizing a comprehensive dataset of agricultural products—including livestock, fruits, and dairy—the study examines key metrics such as units shipped, units sold, and units on hand across various product categories. By evaluating these variables, the research highlights changes in supply chain performance and market dynamics over the two-year period. The study focuses on identifying shifts in product demand, sales performance, and supply chain efficiency, offering insights into how agricultural producers adapted to market conditions. Results indicate that while some categories, such as fruits, experienced consistent sales growth, others faced fluctuations due to supply chain disruptions and changing consumer preferences. This analysis not only underscores the importance of efficient supply chains but also opens avenues for predictive analysis, providing actionable recommendations for improving supply chain practices in the agricultural sector. The findings are particularly relevant for industry practitioners aiming to enhance supply chain resilience and optimize product sales performance.

Fairer schooling for a better housing environment: Effect of abolishing school zones on the housing market

Access to high-quality public education is often closely linked to residential property ownership and location in many countries, contributing to rising housing costs in areas with better educational resources. This creates challenges in housing affordability and perpetuates inequality in education access. Our study explores the effectiveness of two policies aimed at reducing the association between property ownership and access to premium public education: the "six-year-one-position" policy, which limits school enrolment to one student per property every six years, and the "multi-dicing" policy, which uses a random allocation system for student placement. While this research focuses on Beijing, China, the implications of such policies are of broader relevance to cities worldwide facing similar issues. Using a spatial difference-in-difference approach, we find that eliminating school zoning can decrease housing prices by 2.5% in areas where these policies are implemented, with reductions of 4.4%–7.9% in districts previously associated with high-ranking schools. However, the announcement of these policies triggers short-term price spikes, with increases of 2.7%–4.7%, reflecting a panic-buying effect. Moreover, we observe a spillover effect, where demand shifts from regions that abolished school zones earlier to those that implemented the policy later, as revealed by a staggered difference-in-difference analysis. These findings offer insights for policymakers in other urban contexts where educational access is intertwined with housing markets, highlighting the need for balanced strategies to address both housing affordability and equitable education access.

Economic and environmental impacts of the shifts to electromobility in Spain: A multiregional input–output framework

AbstractThe decarbonization of transport is a key goal facing climate change. The electrification of the powertrain for passenger cars is part of this goal to reduce carbon emissions. This involves a big change in the global supply chain, specifically in countries with a high weight of the traditional automotive sector, such as Spain, where above 10% of the GDP comes from this industry. There is a forecasted shift from the sector of traditional automotive parts to the electric sector, where batteries and electric components will be the major part of the powertrain. This work evaluates socioeconomic and environmental impacts of the changes in the car industry from the ramp‐up of the electric vehicles market in Spain, and also in the European Union and the rest of the world. To do it, we use an environmentally extended multiregional and multi‐sectoral input–output model. Our simulations include the technological change and demand shifts estimated to achieve the penetration of electric vehicles up to 2030 and 2050. The results show significant impacts on employment and economic indicators by 2050, when the share of electric vehicles is expected to increase up to a relevant level.

Energy Demand Estimation in Turkey According to Road and Rail Transportation: Walrus Optimizer and White Shark Optimizer Algorithm-Based Model Development and Application

Transport energy demand (TED) forecasting is a crucial issue for countries like Turkey that are dependent on external resources. The accuracy and effectiveness of these forecasts are extremely important, especially for the strategies and plans to be developed. With this in mind, different forms of forecasting models were developed in the present study using the Walrus Optimizer (WO) and White Shark Optimizer (WSO) algorithms to estimate Turkey’s energy consumption related to road and railway transportation modes. Additionally, another objective of this study was to examine the impacts of different transport modes on energy demand. To investigate the effect of demand distribution among transport modes on energy consumption, model parameters such as passenger-kilometers (P-km), freight-kilometers (F-km), carbon dioxide emissions (CO2), gross domestic product (GDP), and population (POP) were utilized in the development of the models. It was found that the WO algorithm outperformed the WSO algorithm and was the most suitable method for energy demand forecasting. All the developed models demonstrated a better performance level than those reported in previous studies, with the best performance achieved by the semi-quadratic model developed with the WO, showing a 0.95% MAPE value. Projections for energy demand up to the year 2035 were established based on two different scenarios: the current demand distribution among transport modes, and a demand shift from road to rail transportation. It is anticipated that the proposed energy demand models will serve as an important guide for effective planning and strategy development. Moreover, the findings suggest that a balanced distribution among transport modes will have a positive impact on transport energy and will result in lower energy requirements.

Agile Supply Chain Management In FashionIndustry

The fashion industry is one of the most globalized sectors, featuring a complex and widespread supply chain. Often, clothing is designed in one country, produced in another, and then distributed and sold across multiple international markets. This global scope and complexity necessitate an agile approach to supply chain management. Agile supply chains are engineered to be flexible, responsive, and adaptable, enabling companies to swiftly respond to shifts in consumer demands and market conditions. By adopting agile principles, fashion companies can more effectively navigate the intricacies of global operations, ensuring that products reach the appropriate markets at the right time, thereby sustaining competitiveness and meeting consumer expectations in a fast-paced industry

Из истории развития табаководства как важнейшей отрасли торгового земледелия в губер-ниях Южного Кавказа во второй половине XIX – начале XX века

The article focuses on the evolution of tobacco cultivation in the South Caucasus over the research period. The author investigates the spread of this technological culture throughout all provinces in the specified region. Information is provided on the quantity of land occupied for tobacco cultiva-tion, tobacco types, industrial firms and their owners, tobacco products produced, and tobacco sales locations. The author examines the many causes of the shift in demand for tobacco products over this historical time, as well as the unequal level of tobacco production. The essay discusses the excellent conditions for the growth of tobacco production in the South Caucasus, which eventually became commodities. The report also underlined the Russian government’s interest in spreading this crop, which was the primary raw material for the tobacco sector. The government, which contributed to the region’s colonial exploitation by the Russian and foreign bourgeoisie, benefited financially from the expansion of the cultivation of technologi-cal and precious crops. The author finds that the tobacco business in the South Caucasus evolved, improved, and gradually adopted the characteristics of commercial agriculture, which included commodity rela-tions. It is emphasized that the quick expansion of commercial agriculture is closely tied to the emergence of capitalist relationships in agriculture.

Investigating Changes in Natural Gas Demand across Great Britain for Domestic Heating Using Daily Data: 2018 to 2024

This study analyses data from natural gas combination boilers across a 6-year timeframe, exploring how demand for space heating and hot water have both changed over time, and highlights the impact of factors such as external temperature and the UK energy price cap. The results show that there has been a significant decrease in annual space heating and hot water demand since 2021. Space heating typically contributes 88% of the total annual gas demand of the boilers, with hot water contributing the other 12%. For the same mean temperature across the fourth quarter (8.5 °C), 2018 had a daily mean energy demand of 50.4 kWh, whereas the 2022 value was 41.4 kWh. This 9.0 kWh (18%) difference of the daily mean for Q4 suggests a shift in consumer demand influenced by other factors such as the energy price cap. This analysis provides additional understanding of how consumer energy demand for heating continues to evolve and invites further studies to be completed on future trends of energy demand for both space heating and hot water. Here, we also highlight the benefit of considering space heating and hot water as separate demands, as this provides additional insights and is something the paper helps to advocate for.

A Radical Idea of Temple Tourism Based on the Theory of Time-Space-Connection

Temple tourism, after the timely and effective building of the offline experience and public opinions, also with the help of marketing platforms and social media to enhance consumer perceived value, thereby establishing the mind assets of temple tourism on the internet. Besides, the research use the concept of time-space-connections as a backdrop to explores the deep reasons why temples can attract the younger generation successfully. Guided by the theory of time-space-connections, we found that the deep reasons are the introduction of suitable products (temple tourism and additional products) at the right time (two periods: post-pandemic and after customer demand shifts) and in the right space (two types of spaces: temple space and the choice between online and offline spaces). Nowadays, with the booming of temple tourism, the author discusses the deep-seated reasons behind the popularity and how to combine offline experiences and public opinions to ignite temple tourism. This offers other industries valuable marketing suggestions and endorsements.

Do Consumers Respond to Social Movements? Evidence from Gender-Stereotypical Purchases After #MeToo

This paper explores whether and how consumers respond to global social movements challenging systemic discrimination and stereotypes. We examine the impact of the #MeToo movement on the market for products with stereotypical markers of femininity. Our analysis of high-frequency stockout and price data from a leading global fashion retailer spans from January 2017 to December 2018 and covers 32 countries, or 89% of the Organization for Economic Cooperation and Development (OECD) population. Using a triple-difference approach across time, countries, and products, we identify changes in product-level stockouts, corresponding to a drop in the purchase of stereotypically “feminine” items. Our findings suggest a sudden shift in demand, with no immediate adjustments in product assortments or pricing. We discuss potential driving mechanisms, as well as implications for product management and marketing in marketplaces where segmentation is gender based. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02352 .

Data‐driven control and a prey–predator model for sourcing decisions in the low‐carbon intertwined supply chain

AbstractThis paper addresses the challenges of low‐carbon sourcing in intertwined supply chains by proposing a data‐driven control framework and a prey–predator model for sourcing decisions. The objective is to optimize low‐carbon objectives and reduce environmental impact. Existing static models fail to capture the dynamic nature of supply chain systems and overlook the ripple effects when sourcing decisions propagate throughout the interconnected network. To bridge this gap, our study develops a dynamic model that explicitly captures the bullwhip effect and leverages real‐time and historical data. This model conceptualizes suppliers as prey and manufacturers and consumers as predators, employing an ecological analogy to decipher the intricate interactions and dependencies within the supply chain. Through this approach, we identify strategies to promote sustainable practices and motivate suppliers to adopt low‐carbon measures. We assess two data‐driven algorithms, the nonlinear auto‐regressive exogenous (NARX) network and sparse identification of nonlinear dynamic systems with input variables (SINDYc). The results reveal that SINDYc outperforms prediction accuracy and control, offering significant advantages for rapid decision‐making. The study highlights how shifts in market demands and regulatory pressures critically influence the strategies of chemical firms and fertilizer markets. Moreover, it discusses the economic challenges in transitioning from high carbon footprint suppliers (HCFSs) to low carbon footprint suppliers (LCFSs), exacerbated by a notable cost disparity where HCFSs are approximately 30% cheaper. By advancing beyond conventional static models, this research provides a deeper understanding of the environmental impacts and operational dynamics within supply chains, emphasizing the significant “ripple effect” where decisions at one node profoundly affect others within the chain.

Application of information technology in transportation operation: A benchmarking approach by ISM MICMAC analysis

This study compares the obstacles to the efficient integration of Information Technology into Transportation services. In order to comprehend the significance and connections between the identifying criteria, a hybrid Interpretive Structural Modelling (ISM) and Matrix Impact Cross Multiplication Applied to Classification (MICMAC) analysis was performed. Based on the experts’ opinion, Government factors, financial restrictions, sector restrictions, firm-related restrictions, and a shift in demand are the five factors that emerged. According to the ISM-based study model, the Information Technology - enabled applications in Transportation services operations is driven by governmental restrictions, then financial constraints, construction industry constraints, and firm-related constraints.

Assessing the fiscal implications of changes in critical minerals’ demand in the low-carbon energy transition

This study introduces the Fiscal Revenue Risk Index (FRRI), a novel metric designed to assess the impact of market fluctuations in critical minerals due to the materialization of climate-related transition risks on countries highly dependent on these resources. Focusing on minerals essential for renewable energy technologies, such as lithium, cobalt, nickel, and rare earth elements, our analysis evaluates how shifts in global demand might influence government revenues, providing crucial insights for policymakers. The FRRI methodology involves several key steps, including assessing fiscal revenue dependency, estimating price volatility, computing minerals’ production changes for future transition scenarios, and integrating these factors into a weighted risk model. This approach allows the computation of aggregate risk scores across various minerals and scenarios, reflecting a country’s exposure to fiscal risks. The study findings indicate that the demand for critical minerals could grow up to sevenfold over the coming decades, significantly impacting fiscal revenues in emerging markets and developing economies (EMDEs). While these countries stand to benefit from increased exports, they also face challenges related to supply reliability and price volatility. Overall, the FRRI is essential for identifying economic risks and opportunities related to the low-carbon transition in EMDEs.

From Data to Insight: Transforming Online Job Postings into Labor-Market Intelligence

In the continuously changing labor market, understanding the dynamics of online job postings is crucial for economic and workforce development. With the increasing reliance on Online Job Portals, analyzing online job postings has become an essential tool for capturing real-time labor-market trends. This paper presents a comprehensive methodology for processing online job postings to generate labor-market intelligence. The proposed methodology encompasses data source selection, data extraction, cleansing, normalization, and deduplication procedures. The final step involves information extraction based on employer industry, occupation, workplace, skills, and required experience. We address the key challenges that emerge at each step and discuss how they can be resolved. Our methodology is applied to two use cases: the first focuses on the analysis of the Greek labor market in the tourism industry during the COVID-19 pandemic, revealing shifts in job demands, skill requirements, and employment types. In the second use case, a data-driven ontology is employed to extract skills from job postings using machine learning. The findings highlight that the proposed methodology, utilizing NLP and machine-learning techniques instead of LLMs, can be applied to different labor market-analysis use cases and offer valuable insights for businesses, job seekers, and policymakers.

Individual corticosterone response to intermittent swim stress predicts a shift in economic demand for ethanol from pre-stress to post-stress in male rats.

This study investigated the relationship between stress exposure and subsequent ethanol use, focusing on individual differences among male rats. We combined operant self-administration with behavioral economics to assess how intermittent swim stress affects ethanol consumption. This approach allowed for a nuanced analysis of the transition from regular ethanol intake to stress-induced escalation in economic demand. Results showed a consistent rise in ethanol demand post-stress among subjects, irrespective of exposure to actual swim stress or a sham procedure. This increase may result from a two-week abstinence or an inherent rise in demand over time. Significantly, we identified a direct link between post-stress corticosterone levels and the demand for ethanol, considering baseline levels. This correlation was particularly pronounced when examining the shifts in both corticosterone levels and demand for ethanol post-stress. However, neither post-stress corticosterone levels nor their change over time correlated significantly with changes in ethanol demand following a forced swim test that was administered 24 h after the intermittent swim stress test. This suggests potential context-specific or stressor-specific effects. Importantly, pre-stress ethanol demand did not significantly predict the corticosterone response to stress, indicating that high ethanol-demand rats do not inherently exhibit heightened stress sensitivity. Our research brings to light the complex interplay between stress and ethanol consumption, highlighting the critical role of individual differences in this relationship. This research introduces a nuanced perspective, underscoring the need for future studies in the realm of stress and substance use to give greater consideration to individual variability.

Transformative changes are needed to support socio-bioeconomies for people and ecosystems in the Amazon.

Current social-technical and political conditions threaten the integrity of the Amazon biome. Overcoming these lock-ins requires structural transformations away from conventional economies towards 'socio-bioeconomies' (SBEs). SBEs are economies based on the sustainable use and restoration of Amazonian ecosystems, as well as Indigenous and rural livelihood systems in the region. They include sustainable eco-tourism as well as diversified production and innovative processing of fruits, nuts, oils, medicines, fish and other products deriving from socio-biodiversity. Using a sustainability transitions perspective, we argue for multi-scalar policy changes to sustain, enhance and scale-out and scale-up SBE initiatives. To nurture niche SBE acitivities, we advocate for improvements in infrastructure, value chains and social organizations. To dismantle structural barriers, we call for an end to harmful subsidies, greater representation of marginalized communities in territorial planning, enhanced rural-urban and intersectoral linkages, international collaboration, shifts in demand, and changes in conservation and production narratives. Policies for SBEs must also use clear definitions, participatory processes and a multi-biome approach to avoid perverse outcomes.