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Considering the emergence of e-commerce platforms and their integration into marketing channels, this paper aims to investigate how artificial intelligence (AI) resources – both technological and human – possessed by e-commerce platforms can enhance their channel power by acquiring market-based assets (relational and intellectual).

Based on resource-based theory and resource orchestration theory, the authors developed a framework tested using survey data gathered from the sellers, which incorporated six key variables: the e-commerce platform’s AI technology resources and human resources, rational and intellectual market-based assets, intraplatform competition and channel power. The analyses are performed using the regression analysis technique.

The empirical findings indicate that both technological and human AI resources are crucial in building channel power. In addition, market-based assets serve as a mediator in this relationship, while intraplatform competition moderates the effect of intellectual market-based assets on channel power negatively.

This study contributes to the existing literature by exploring how e-commerce platforms’ AI resources affect their channel power. The results offer valuable guidance to managers and researchers on optimizing AI resources to improve channel power.

To create and sustain a resource-based competitive advantage, managers acquire and develop specialized resources as they grow their firms. The authors argue that an important part of committing to a resource-based strategy is a willingness to keep spending on specialized resources during periods when sales and profits are down. The authors seek to validate this conjecture by examining whether such resource-based commitment to a customer-centered strategy results in improved customer satisfaction. The authors use the stickiness of selling, general, and administrative (SG&A) expenses to capture this commitment empirically. The authors first document that future customer satisfaction is positively associated with SG&A cost stickiness, consistent with the premise that the retention of specialized SG&A resources during low demand periods helps firms to build and maintain relationships with customers over time. Next, the authors test whether expected future benefits of customer satisfaction are enhanced when SG&A cost stickiness is higher. The authors find that the positive relation between Tobin’s Q and customer satisfaction is positively moderated by SG&A cost stickiness. Finally, the authors test whether earnings persistence, a quality of earnings associated with sustained performance over time, is positively associated with the interaction between customer satisfaction and SG&A cost stickiness. The authors find that it is. Their evidence supporting these predictions is consistent with the conjecture that resource-based commitment reflected in cost stickiness is an important dimension of creating and sustaining a resource-based competitive advantage.

This paper extends the authors’ previous research work investigating resilience for municipal infrastructure from an asset management perspective. Therefore, this paper aims to formulate a pavement resilience index while incorporating asset management and the associated resilience indicators from the authors’ previous research work.

This paper introduces a set of holistic-based key indicators that reflect municipal infrastructure resiliency. Thenceforth, the indicators were integrated using the weighted sum mean method to form the proposed resilience index. Resilience indicators weights were determined using principal components analysis (PCA) via IBM SPSS®. The developed framework for the PCA was built based on an optimization model output to generate the required weights for the desired resilience index. The output optimization data were adjusted using the standardization method before performing PCA.

This paper offers a mathematical approach to generating a resilience index for municipal infrastructure. The statistical tests conducted throughout the study showed a high significance level. Therefore, using PCA was proper for the resilience indicators data. The proposed framework is beneficial for asset management experts, where introducing the proposed index will provide ease of use to decision-makers regarding pavement network maintenance planning.

The resilience indicators used need to be updated beyond what is mentioned in this paper to include asset redundancy and structural asset capacity. Using clustering as a validation tool is an excellent opportunity for other researchers to examine the resilience index for each pavement corridor individually pertaining to the resulting clusters.

This paper provides a unique example of integrating resilience and asset management concepts and serves as a vital step toward a comprehensive integration approach between the two concepts. The used PCA framework offers dynamic resilience indicators weights and, therefore, a dynamic resilience index. Resiliency is a dynamic feature for infrastructure systems. It differs during their life cycle with the change in maintenance and rehabilitation plans, systems retrofit and the occurring disruptive events throughout their life cycle. Therefore, the PCA technique was the preferred method used where it is data-based oriented and eliminates the subjectivity while driving indicators weights.

The review examines the existing literature on blockchain-based small and medium enterprise (SME) finance and highlights its trend, themes, opportunities and challenges. Based on these factors, the authors create a framework for the existing literature on blockchain-based SME financing and lay down future research paths.

The review follows a systematic approach. It includes 53 articles encompassing multiple dimensions of blockchain-based SME finance, including peer-to-peer lending platforms, supply chain finance (SCF), decentralized lending protocols and tokenization of assets. The review critically evaluates these approaches' theoretical underpinnings, empirical evidence and practical implementations.

The review demonstrates that blockchain-based SME finance holds significant promise in addressing the credit gap by leveraging blockchain technology's decentralized and transparent nature. Benefits identified include reduced information asymmetry, improved access to financing, enhanced credit assessment processes and increased financial inclusion. However, the literature acknowledges several challenges and limitations, such as regulatory uncertainties, scalability issues, operational complexities and potential security risks.

The article contributes to the growing knowledge of blockchain-based SME finance by synthesizing and evaluating the existing literature. It also provides a framework for the existing literature in the area and future research paths. The study offers insights for researchers, policymakers and practitioners seeking to understand the potential of blockchain technology in filling the SME credit gap and fostering economic development through improved access to finance for SMEs.

This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.

This paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.

The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.

The main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.

National cultures significantly explain cross-country differences in the relation between asset growth and stock returns. Motivated by the notion that managers in individualistic and low uncertainty-avoiding cultures have a higher tendency to overinvest, this study aims to show that the negative relation between asset growth and stock returns is stronger in countries with such cultural features. Once the researchers control for cultural dimensions, proxies associated with the q-theory, limits-to-arbitrage, corporate governance, investor protection and accounting quality provide no incremental power for the relation between asset growth and stock returns across countries. Evidence of this study highlights the importance of the overinvestment hypothesis in explaining the asset growth anomaly around the world.

Financial asset return series usually exhibit nonnormal characteristics such as high peaks, heavy tails and asymmetry. Traditional risk measures like standard deviation or variance are inadequate for nonnormal distributions. Value at Risk (VaR) is consistent with people's psychological perception of risk. The asymmetric Laplace distribution (ALD) captures the heavy-tailed and biased features of the distribution. VaR is therefore used as a risk measure to explore the problem of VaR-based asset pricing. Assuming returns obey ALD, the study explores the impact of high peaks, heavy tails and asymmetric features of financial asset return data on asset pricing.

A VaR-based capital asset pricing model (CAPM) was constructed under the ALD that follows the logic of the classical CAPM and derive the corresponding VaR-β coefficients under ALD.

ALD-based VaR exhibits a minor tail risk than VaR under normal distribution as the mean increases. The theoretical derivation yields a more complex capital asset pricing formula involving β coefficients compared to the traditional CAPM.The empirical analysis shows that the CAPM under ALD can reflect the β-return relationship, and the results are robust. Finally, comparing the two CAPMs reveals that the β coefficients derived in this paper are smaller than those in the traditional CAPM in 69–80% of cases.

The paper uses VaR as a risk measure for financial time series data following ALD to explore asset pricing problems. The findings complement existing literature on the effects of high peaks, heavy tails and asymmetry on asset pricing, providing valuable insights for investors, policymakers and regulators.

One important study in the portfolio investment is the study of the optimal asset allocations. Markowitz is the pioneer of modern portfolio theory that analyses the performance of portfolio based on the mean (reward) and variance (risk). Motivated by the Markowitz's mean variance model, the purpose of this paper is to propose a new portfolio optimization model that takes into consideration both processes of purification and screening, which are key to constructing a Shariah-compliant portfolio. In practice, this paper introduces a stochastic purification variable and a probabilistic screening constraint into a portfolio model.

First, the authors study the stochastic nature of purification variable and apply it to both investment and dividend purification. Second, recognizing that the importance of on-going screening could adversely affect the portfolio strategy, the authors impose probabilistic constraints to control the risk of compliance change. They evaluate the proposed model by formulating the screening constraints at both asset and portfolio levels, together with three different financial screening divisors that are broadly used by the international Shariah boards. The authors also conduct an extensive empirical study using a sample of Shariah-compliant public companies listed on the Indonesia Stock Exchange.

Based on the empirical example presented in this paper, the authors found that the purification variable in the proposed model is closer to the practice in the Sharia capital market in terms of the nature of the non-constant data, and this variable reduces the total income of portfolio which has not been captured in the previous literature. The authors also have successfully derived the portfolio screening constraint to mitigate the risk of the asset change to be non-compliant in the future.

Based on the authors’ knowledge, this is the first paper that proposed the stochastic purification and the dynamic of screening processes into the Shariah portfolio model. This paper also examines the impact of non-short-selling, purification and screening policies to the performance of Shariah portfolio.

With the emergence of digital twins, the construction industry is looking toward improving the inspection and maintenance of all kinds of assets, such as bridges, roads and utilities. The purpose of this paper is to provide insights into how the development of an interactive digital twin creates a variety of interactions between users of this technology and assets to be monitored.

The development of a digital twin inspection model, focusing on the specific case of a sewage pumping station, is chosen as the subject of a case study. Through the development of this model, this study explores the various user–technology interactions that can be designed in a digital twin context.

Users interact with digital twins by following virtual instructions in a certain way, which creates a “quasi-other” relationship. A digital twin based on virtual reality (VR) also make users feel as if they are within the created VR of an inspection site, thereby immersing them in the VR environment. The design of a VR-based digital twin, which is determined by decisions made during the development process, shapes the context in which users interact with the technology and assets.

This study shows that a digital twin in construction practice may play different “actant” roles having different types of influences. Analyzing these actant roles and influences in terms of force and visibility adds a new perspective on the interaction between users and digital twins in construction and asset monitoring practice.

The purpose of this paper is to present alternative ways of addressing inequality in age-friendly work by drawing attention to the limitations of place-based approaches in meeting the needs of dispersed communities.

A case study of the age-friendly programme Ambition for Ageing (AfA) is used to explore three examples of working with minority communities.

Place-based age-friendly development risks further marginalising older people belonging to dispersed communities of identity or experience; therefore, we need to adopt adopt an intersectional approach to inequality in later life.

Three ways that age-friendly programmes may become more inclusive of minority groups who are geographically dispersed are identified: bringing community members together; co-production; and supporting visibility in mainstream settings.

This paper brings together insights from the AfA programme, critically assessing place-based approaches in relation to working with dispersed communities of identity. It offers some ways to mitigate limitations through adopting tailored equality approaches.