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The development of the digital age has made data and information more transparent, enhancing the strategic perspectives of both buyers (strategic waiting) and sellers (price fluctuations) in their decision-making. This research investigates the optimal dynamic pricing strategy of the content product developer in relation to their consideration of consumer fairness concerns to elucidate the impact of consumer fairness concerns on the dynamic pricing strategy of the developer.

This paper assumes that monopolistic content developers implement a dynamic pricing strategy for the content product. Through constructing a two-period dynamic pricing game model, this research investigates the optimal decisions of the content developer, contingent upon their consideration or disregard of consumer fairness concerns. In the extension section, the authors additionally account for the influence of myopic consumers on these optimal decisions.

Our findings reveal that the degree of consumer fairness concerns significantly influences the developer’s optimal dynamic pricing decision. When a developer offers content products with lower depth, there is a propensity for the developer to refrain from incorporating consumer fairness concerns into a dynamic pricing strategy. Conversely, in cases where the developer offers a high-depth content product, consumer fairness concerns benefit the developer. Furthermore, our analysis reveals a consistent benefit for the developer from the inclusion of myopic consumers.

Few studies have delved into the conjoined influence of consumer fairness concerns and strategic behavior on dynamic pricing strategy. Our findings indicate that consumer fairness concerns can enhance the efficiency of the value chain for content products under specific conditions. This paper not only enriches the existing literature on dynamic pricing by incorporating consumer fairness concerns theoretically but also offers practical insights. The outcomes of this research can guide content product developers in devising optimal dynamic pricing strategies.

The study aims to embrace the lingering call for more empirical studies that can theorize the role of digital platforms in digital entrepreneurship. Hence, this study seeks to reveal the liminal space entrepreneurial experience of third-party application developers, by investigating how the platform boundary resources promote third-party entrepreneurial actions, as they transition through the disoriented, uncertain and ambiguous processes of digital entrepreneurship development.

To conduct this investigation, an expert interview qualitative method was used. This approach is a well-established technique in the field of social sciences, which allowed a detailed exploration of the theory of liminality. Liminality refers to the transitional phase that individuals or groups experience when moving from one social or cultural context to another. The expert interview method is appropriate for this study because it involves engaging with knowledgeable individuals who have extensive experience and expertise in the subject area being investigated. Through in-depth and unstructured interviews, the experts were able to provide valuable insights and perspectives about the phenomenon investigated.

The research findings demonstrate that digital platform boundary resources play a significant role in the behaviour of third-party developers’ who engage in the development of digital entrepreneurship in today’s market. The study highlights three ways that show how these resources (software development kit (SDK), API, integrated development environment (IDE), libraries, frameworks) enable third-party developers to create new applications that are used to pursue entrepreneurship in a digital platform, leading to increased user engagement and revenue generation.

The research addresses the critical roles of digital platform boundary resources in digital entrepreneurship development processes. Also, using liminality theory, the research explicated the core experiences of third-party developers as they navigated the challenges and ambiguities experienced in the pursuit of entrepreneurship. Thus, contributing to the existing body of knowledge in literature and practice.

High-density development requires large land parcels, but fragmented land ownership can impede redevelopment. While earlier compact city development in Sydney occurred on large-scale brownfield sites, redeveloping and re-amalgamating older strata-titled properties is now integral to further densification. The purpose of this study is to examine collective sales activity in one Sydney suburb where multiple strata-titled redevelopments and re-amalgamations have been attempted. The authors explore how owners navigate the process of selling collectively, focusing on their experience of legislation introduced to facilitate this process, the Strata Schemes Development Act 2015 [New South Wales (NSW)].

By reviewing sales listings, development applications and media coverage, and interviewing owners, lawyers and estate agents, the authors map out collective sale activity in a case study area in Sydney’s northwest.

Strata collective sales are slow and difficult to complete, even when planning and market drivers align. Owners find the Strata Scheme Development Act 2015 (NSW) difficult to navigate and it has not prevented strategic blocking attempts by competing developers. The long timelines required to organise collective sales can result in failure if the market shifts in the interim. Nonetheless, owners remain interested in selling collectively.

This case study is important for understanding the barriers to redevelopment to achieve a more compact city. It highlights lessons for other jurisdictions considering similar legislative changes. It also suggests that legislative change alone is insufficient to resolve the planning challenges created by hyper-fragmentation of land through strata-title development.

This paper aims to explore factors impacting citizen intention toward artificial intelligence (AI) adoption, considering government regulation as a moderating variable. It focuses on the Palestinian Cellular Communications Sector in Gaza Strip, providing insights into the citizen-AI relationship dynamics. The research contributes to enhancing comprehension of AI technology from clients’ perspective.

To test the hypotheses, a questionnaire was used in an empirical study to collect primary data. In total, 347 Palestinian citizens responded to the survey.

The findings of this paper reveal that perceived usefulness, perceived ease of use, perceived risks, social influence, user experience and privacy and security concerns significantly influence citizen intention toward AI adoption. Furthermore, government regulations as a moderating variable strengthen the impact of perceived usefulness, perceived ease of use, perceived risks, social influence, user experience and privacy and security concerns on citizen intention toward AI acceptance and adoption. Thus, further research should explore specific domains and cultural contexts to gain a more comprehensive understanding of the factors shaping acceptance and adoption.

The findings of the study should be understood in the context of their limitations. First, the study ignored cultural or domain-specific subtleties in favor of generic characteristics, which calls for more research in these particular circumstances. Second, relying on self-reported data might result in biases and limitations due to subjectivity in reporting, indicating the necessity for alternate data gathering methods and approaches in future research.

Policymakers, developers and organizations working to promote the acceptability and implementation of AI applications should consider the practical implications of this study’s results. To secure the long-term use of AI technologies in a responsible and user-centric way, policymakers should give priority to public education and awareness, user-centered design and ethical AI development techniques. They should also stimulate partnerships and create monitoring systems.

This paper investigates the originality of factors that influence citizen intention toward AI acceptance and adoption. It uniquely examines the moderating role of government regulations in shaping this intention. By addressing this novel aspect, the paper contributes to advancing our understanding of the complex dynamics surrounding citizen intentions toward AI applications.

This study aims to explore the different effects of inter-community group networks and intra-community group networks on group innovation.

The authors used a pooled panel dataset of 12,111 self-organizing innovation groups in 463 game product creative workshop communities from Steam support to test the hypothesis. The pooled ordinary least squares (OLS) model is used for analyzing the data.

The results show that network constraint is negatively associated with the innovation performance of online groups. The average path length of the inter-community group network negatively moderates the relationship between network constraint and group innovation, while the average path length of the intra-community group network positively moderates the relationship between network constraint and group innovation. In addition, both the network density of inter-community group networks and intra-community group networks can negatively moderate the negative relationship between network constraint and group innovation.

The findings of this study suggest that network structural characteristics of inter-community networks and intra-community networks have different effects on online groups’ product innovation, and therefore, group members should consider their inter- and intra-community connections when choosing other groups to form a collaborative innovation relationship.

High economic policy uncertainty forces firms to accumulate a higher level of cash than during normal business periods. However, it is not evident that economic policy uncertainty has a homogeneous impact across cash-holding distributions. This paper aims to study the impact of economic policy uncertainty, leverage and their interaction on cash-holding distributions.

This study adopted a quantile regression approach to examine the influence of economic policy uncertainty and firm leverage on firm-level cash-holding distributions. To investigate the influence across quantiles, the author estimated 19 quantiles between 0.05 and 0.95.

This study finds that both economic policy uncertainty and firm leverage significantly affect firm-level cash-holding distributions heterogeneously. But, the impact of the interaction of these two variables is significant only for firms placed in the 60th to 85th quantiles of cash holding distribution.

The study adds to the existing knowledge of determinants of firm-level cash holdings but takes exogenous variables as economic policy uncertainty. The paper builds on a unique sample setting wherein, the cash holdings of all nonfinancial firms have increased many folds, including housing companies in an emerging economy.

This paper aims to explore the relationship between market pricing and design quality within the development industry. Currently, there is a lack of research that examines real estate at the property level. Development quality is widely believed to have diminished over the past decades, while many investors seem uninterested in the design process. The study aims to address these issues through a pricing model that integrates design attributes. It is hoped that empirical findings will invite broader stakeholder interest in the design process.

The research establishes a framework for assessing spatial compliance across residential developments within London. Compliance is assessed across ten boroughs, with technical space guidelines used as a proxy for design quality. Transaction prices and spatial assessments are aligned within a hedonic pricing model. Empirical findings are used to establish whether undermining spatial standards presents a significant development risk.

Findings suggest a relationship between sale time and unit size, with “compliant” units typically transacting earlier than “non-compliant” units. Almost half of the 1,600 apartments surveyed appear to undermine technical guidelines.

It is suggested that an array of design attributes be explored that extend beyond unit size. Additionally, future studies may consider the long-term implications of design quality via secondary transaction prices.

Practical implications include the development of a more scientific approach to design valuation. This may enhance the position of product design management within the development industry and architectural services.

Social implications may include improvement in residential design.

An innovative approach combines a thorough understanding of both design and economic principles.

The COVID-19 pandemic has profoundly impacted the global economy, disrupting supply chains, causing job losses and altering consumer demand. In Malaysia, the real estate sector has been notably affected, with increased property impairments and overhang due to unprecedented uncertainty. Understanding these effects is crucial for policymakers and investors to prevent real estate and banking crises. This study aims to analyse the relationships between macroeconomic factors during the pandemic on property impairments and overhang, providing insights for maintaining macroeconomic stability. The findings will inform strategies for mitigating economic shocks, identifying opportunities, and guiding real estate policies in Malaysia and potentially globally.

This research article uses a time series ARDL regression analysis to examine pivotal macroeconomic factors including income, housing process, interest rates and unemployment on property loan impairments and property supply overhang in Malaysia. ARDL is effective to measure and analyse time series data, especially to understand the lagged impacts of macroeconomic factors. This can be seen by various economists in analysing macroeconomic factors affecting non-performing loans or the real estate finance using regression analyses both in Malaysia and other regions. The observations are gathered before, during and after the COVID-19 pandemic, spanning a five-year period with monthly frequency from 2018 to 2022.

The study emphasizes the critical importance of effectively managing unemployment and implementing policy interventions, such as moratoriums, to stabilize the economy and reduce the risk of loan impairments during crises like the COVID-19 pandemic. Additionally, this study highlights a significant inverse relationship between income per capita and loan impairments, underscoring the necessity for policies that promote economic growth and income equality. Initiatives targeting job creation, education and skills development can elevate income levels, thereby decreasing loan impairments. Lower lending interest rates during the pandemic also help mitigate the risk of loan impairments by facilitating borrowing, stimulating economic activity and enhancing financial well-being. Furthermore, the study suggests that while lower interest rates incentivize property developers and investors, understanding the intricate interaction between housing prices and supply is crucial for policymakers and stakeholders to effectively manage the housing market and ensure adequate housing supply, especially during crises.

This paper provides insight for policymakers, regulators, investors and property consultants into the dynamic effects of key macroeconomic factors amidst a global recession in how they impact the real estate market with regards specifically to all types of property loan impairments and property supply overhang. The observations are limited to the COVID-19 period, spanning five years with monthly data from 2018 to 2022. This understanding can facilitate the development of targeted strategic monetary policies and investment decisions in case of future recessions.

Policymakers should prioritize initiatives such as moratoriums and job creation programs to mitigate economic downturns. Additionally, financial institutions need to adjust lending practices in response to lower interest rates, while stakeholders in the housing market must understand the complex dynamics between housing prices and supply to ensure a balanced market. Overall, addressing underlying economic factors and implementing targeted policies are essential for building resilience and promoting sustainable economic growth amidst challenging circumstances.

Initiatives aimed at fostering income equality, creating employment opportunities and ensuring housing accessibility contribute to greater social cohesion and well-being. By promoting financial inclusion and building resilience to crises, societies can mitigate the adverse social impacts of economic challenges such as unemployment and housing affordability. Overall, addressing socioeconomic disparities and promoting inclusive growth are essential for fostering a more equitable and resilient society.

The originality and uniqueness of this study lie in its comprehensive analysis of the impact of COVID-19 on loan impairments and housing supply. While previous studies have focused on the pandemic’s effects on specific segments of the real estate market or property prices, this study provides a broad overview of its impact on property loan impairments and housing supply overhang. Finally, this study highlights the social and practical implications. Overall, this study offers a distinctive analysis of COVID-19’s impact on the real estate market and its implications for policymakers, real estate professionals and investors.

The Covid-19 pandemic appears to have engendered heterogeneous effects on individuals’ labour market prospects. This paper focuses on two possible sources of a heterogeneous exposition to labour market risks associated with the pandemic outbreak: the routine task content of the job and the teleworkability. To evaluate whether these dimensions played a crucial role in amplifying employment and wage gaps among workers, we focus on the case of Italy, the first EU country hit by Covid-19.

Investigating the actual effect of the pandemic on workers employed in jobs with a different degree of teleworkability and routinization, using real microdata, is currently unfeasible. This is because longitudinal datasets collecting annual earnings and the detailed information about occupations needed to capture a job’s routine task content and teleworkability are not presently available. To simulate changes in the wage distribution for the year 2020, we have employed a static microsimulation model. This model is built on data from the Statistics on Income and Living Conditions (IT-SILC) survey, which has been enriched with administrative data and aligned with monthly observed labour market dynamics by industries and regions.

We measure the degree of job teleworkability and routinization with the teleworkability index (TWA) built by Sostero et al. (2020) and the routine-task-intensity index (RTI) developed by Cirillo et al. (2021), respectively. We find that RTI and TWA are negatively and positively associated with wages, respectively, and they are correlated with higher (respectively lower) risks of a large labour income drop due to the pandemic. Our evidence suggests that labour market risks related to the pandemic – and the associated new types of earnings inequality that may derive – are shaped by various factors (including TWA and RTI) instead of by a single dimension. However, differences in income drop risks for workers in jobs with varying degrees of teleworkability and routinization largely reduce when income support measures are considered, thus suggesting that the redistributive effect of the emergency measures implemented by the Italian government was rather effective.

No studies have so far investigated the effect of the pandemic on workers employed in jobs with a different degree of routinization and teleworkability in Italy. We thus investigate whether income drop risks in Italy in 2020 – before and after income support measures – differed among workers whose jobs are characterized by a different degree of RTI and TWA.

In 2016, the Chinese central government decentralized the responsibilities of housing market regulation to the municipal level. This paper aims to assess whether the decentralized market regulation is effective.

This study first investigates the fundamental drivers of urban housing prices in China. Taking into consideration the factors driving housing prices, the authors further investigate the effectiveness of decentralized housing market regulation by a pre- and post-policy comparison test using a panel data set of 35 major cities for the years from 2014 to 2019.

The results reveal heterogenous policy effects on housing price growth among cities with a one-year lag in effectiveness. With the decentralized housing market regulation, cities with fast price growth are incentivized to implement tightening measures, while cities with relatively low housing prices and slow price growth are more likely to do nothing or deregulate the markets. The findings indicate that the shift from a centralized housing market regulation to a decentralized one is more appropriate and effective for the individual cities.

Few policy evaluation studies have been done to examine the effects of decentralized housing market regulation on the performance of urban housing markets in China. The authors devise a methodology to conduct a policy evaluation that is important to inform public policy and decisions. This study helps enhance the understanding of the fundamental factors in China’s urban housing markets and the effectiveness of municipal government interventions.