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Retail areas significantly influence the social, economic, and environmental dynamics of cities. The systematic review of 84 articles demonstrates how urban form shapes the spatiality of retailing across social, spatial, ecological and political dimensions. By synthesizing research scopes, theories, methods and scales, the findings show how diverse dimensions of retailing are increasingly integrated through shared analytical perspective and overlapping agendas. The study argues that urban form acts not only as a structural basis but also as a connective thread, linking spatial characteristics of retail landscape to governance strategies, consumer behavior, cultural diversity, resilience, environmental outcomes, and social inclusion.

Extensive multi-criterion sorting (MCS) methods have been developed to address problems encountered in real-world scenarios. Nevertheless, these methods did not take into account the reliability of the information given by experts, and neglected the preferences of decision-makers when calculating the comprehensive performances of alternatives. To overcome these limitations, this paper proposes a double normalization-based multiple aggregation sorting method with Z-numbers (Z-DNMASort), which can depict both the reliability and uncertainty of any available information. The method considers both quantitative and qualitative criteria in forms of benefit, cost or target types in the sorting process, and also considers the reliability of information given by experts. Furthermore, this method takes into account three aggregation techniques to reflect the preferences of decision-makers when calculating the comprehensive performances of alternatives. A case study regarding battery electric vehicle (BEV) evaluation is given to validate the proposed method. The paper concludes by providing a comprehensive analysis and discourse on the proposed method.

The development of technology and globalisation have opened wide opportunities even for investors without relevant knowledge or experience to use investment instruments. Although cryptocurrencies have unique features that attract investors, the market is also subject to risks: price fluctuations, operational risks, and the issue of trust. In this context, looking for effective solutions for investment portfolios and being able to understand better and evaluate the cryptocurrency market becomes especially important. Previous studies have highlighted the benefits of cryptocurrencies, such as diversification, hedging, and safe havens, but there is little research that reveals the performance of a cryptocurrency portfolio. Investors seek the highest possible return but generally avoid or underestimate risk. Applying portfolio optimisation in the study aims to identify cryptocurrencies, the inclusion of which in a traditional asset investment portfolio would help to achieve the most effective combination of desired return and risk. The primary purpose of this study is to evaluate the impact of different cryptocurrencies on the efficiency of the investment portfolio. Ten cryptocurrencies included in the B10 BITA CRYPTO 10 INDEX composition are studied. Due to the instability of cryptocurrencies, the composition of this index is constantly changing. The cryptocurrencies analysed in the study, included in the composition of this index, were fixed in August 2023. Traditional assets in the study are represented by stock indices: S&P 500, Euro Stoxx 50, DAX and CAC 40. After identifying efficient portfolios in the study, it can be concluded that the inclusion of different cryptocurrencies in an investment portfolio consisting of stock indices does not always allow efficient portfolio results to be achieved.

Research background: Stochastic information in finance and economics results from various unexpected and uncertain processes and fluctuations of financial and economic activity. It is necessary to make investment decisions in capital and exchange markets adequately. Thus stochastic models in investment are widely used. Investment decision-making problems are successfully being solved by applying artificial intelligence or similar methods. However, artificial intelligence can solve significantly broader groups of problems and tasks. To solve such kinds of problems, the focusing of artificial neurons into the effectively functioning stochastic pseudo-neural network is oriented in the proposed research. Purpose of the article: The research objective is to form an efficient investment portfolio using the historical data of financial markets and assess its results. Methods: To reach the aim of the research, the stochastic investment system based on artificial intelligence and methodology of stochastic expertise is developed. It is capable of solving the investment problems in financial markets. The system is backed up by the authors’ previously developed adequate portfolio model, which solves investment problems concerning the stochastic nature of investment return and analyses three investment parameters: return, reliability, and risk. Findings & value added: The paper presents investment results in the USA stock market during the 2019-2020 time period. Based on the historical data, the investment portfolio is proposed, highlighting its generated return and reliability. The possibilities to invest in portfolios with different risk levels are disclosed, attention to the investor’s risk resistance level is paid. Also, an experiment in the exchange market has been conducted. After performing experiments in capital and exchange markets, the traditional technique of investment portfolio optimal solution search has been diversified and broadened. The results of the research are useful for researchers in the field of investment, as well as for individual and institutional investors.

Disasters such as the COVID-19 pandemic outbreak can bring challenges to trades all over the world. How to enhance the supply chain resilience and strengthen the competitiveness of enterprises in emergency situations has become a significant topic. To improve the emergency response capability of supply chains, this study aims at establishing a novel decision-making method to deal with the supplier selection problem during the COVID-19 pandemic outbreak based on a novel preference information represent tool, the hesitant fuzzy linguistic preference relation (HFLPR). To achieve this goal, we first introduce the alpha-cut of hesitant fuzzy linguistic elements and transform each hesitant fuzzy linguistic element in an HFLPR into the corresponding semantic considering the degree of confidence of the decision-makers regarding the evaluation values. Then, the utility matrix of an HFLPR is proposed. Afterwards, we construct two dual linear programming models to obtain interval weights from an HFLPR based on the concept of the least upper and the greatest lower bounds. An approach to ranking the obtained interval weights is introduced based on the concept of possibility degree between intervals. The procedure is summarised for the facility of implementation and we apply it to a case study about the resilient supplier selection during the COVID-19 pandemic outbreak. Some comparative analyses are presented to demonstrate the advantages of our method.

This study links three important global governance issues: The 2030 Agenda for Sustainable Development, China-Lithuania (EU) relations and The Belt and Road Initiative (BRI). Our orientation to sustainability as it relates to China and Lithuania is both methodological and analytical. We view the impact of the Brundtland Report as an approach to temporal planning. It is also a solution to the problem of political legitimation for China and Lithuania, as time horizons differ according to differing sectors of the social system. There are layerings of horizons along local, national, and global development. Within the context of sustainability, the focus on development centres economic objectives and priorities of which government, business, and policy organizations must be mindful in their decision-making. All decisions create time horizon constraints. In the case of China and Lithuania, sustainability developments emanate from recent totalitarian regimes under a command organizational structure. Both now embark upon market economies, creating challenges for each in their efforts to maintain stability in transition. A typology of time horizons and an example of their possible impacts are discussed.

The development of municipalities as open socio-economic systems is accompanied not only by positive, but also by negative variations that integrate a number of social and economic problems (corruption, poverty, emigration, crime, etc.). The major outcomes of the above-mentioned problems are emergence of the shadow economy (SE) and a growing unemployment rate. Generalising, their combined impact can be termed as N-development. Quantification of the scale of N-development requires normalisation and aggregation of the shadow economy and unemployment rate values. It is relevant to normalize the values by employing a functional (non-linear) rather than linear approach because appropriate functions allow quantifying both value fluctuation rates and value significance on a selected evaluation scale. This way, indicator values are transformed into dimensionless and made comparable, which allows aggregating them into a single index. Scientific novelty of the article lies in two following aspects. First, the negative processes that inevitably accompany the development of socio-economic systems have been identified and aggregated under the category of N-development; second, the scale of N-development has been quantified by adjusting non-parametric statistical methods that allow a transition from linear to non-linear normalisation of the indicator values based on Harrington functions. The method proposed makes it possible not only to make the indicator values dimensionless, but also to evaluate their significance and thus expand the potential of multi-criteria evaluation.

Randomness, volatility, and nonlinearity displayed by the stock market lead to the uncertainty of the stock market index and stock prices. The purpose of the study is to find a straightforward method for portfolio decision applicable to strong-form and weak-form efficient markets. Thus, a methodology for porfololio decision base on the Nonlinear Autoregressive Exogenous Model (NARX) and multi-objective optimization (MO) was proposed. First, two of eight quarters from 2018 to 2019 were chosen to buy S&P 500 stocks on the basis of the predicted stock market trend using the NARX with a single exogenous variable. The variable was selected from 67 macroeconomic factors by Shannon entropy or relevance. Then, the stocks were selected for a portfolio on the basis of the predicted stock returns from the NARX with a mean relative error as the criteria. Next, a reverse conditional probability indicator was imported as a risk indicator for the objective function of MO, and the stock weights of the portfolio were allocated by MO following the principle of maximizing predicted portfolio return and minimizing portfolio risk. The final findings demonstrate that the portfolio return is 8%–14% below the S&P 500 return and is increased to approximately 5% above the S&P 500 return after the stock weights were allocated by MO. The final investment return for eight quarters is 60% above the S&P 500 return if the proposed investment strategy was adopted. Therefore, the proposed method in the study combining the NARX and MO with certain criteria can guide investors to make a rational portoloio decision and give a reference for scholars to establish effective method for the prediction of stock prices and assets allocation.

The aspiration of the undertaken article is to explore the effect of relative price variability (RPV) on the inflation rate by estimating the model of fixed effects regression by employing the panel data of prices of 35 cities of Pakistan. We have followed the Ball - Mankiw (1995) approach, which has considered the dissemination of changes in relative price as the supply shocks and reflects on a theory of inflation on the supply side. Our results show that the inflation rate, both food, and non-food inflation, is significantly and positively affected by relative price variability. The results imply that supply-side factors, as exhibited in the dispersion of relative price changes, are robust determinants of the inflation rate.

Practical multi-criterion decision-making problems about partner selection are usually composed of both quantitative and qualitative criteria. To solve such problems, this paper develops a ranking method, named the q-ROF-DNMA method, based on the double normalization-based multiaggregation (DNMA) method and a distance measure of q-rung orthopair fuzzy numbers (q-ROFNs). The values of quantitative criteria are expressed as numerical numbers, while the information on qualitative criteria is described by the q-rung orthopair fuzzy set (q-ROFS) which can describe inaccurate and uncertain information effectively. A distance measure is proposed to process the q-ROF information such that both quantitative and qualitative data can be integrated in a collective decision matrix. In addition, we combine three aggregation techniques with two target-based normalization methods to develop three subordinate utility functions under the q-rung orthopair environment. Next, an integration method is introduced to aggregate the utility values and subordinate ranks of alternatives to measure their comprehensive performances. Finally, the superiority of the proposed method is illustrated by comparative analysis based on a case study concerning the partner selection in an automobile manufacturing enterprise.