Prior research has failed to document an integrated model from UTAUT2 and diffusion innovation theory to explain the intention of fintech adoption among culinary micro, small, and medium enterprises (MSMEs) post-COVID-19. This study investigates the factors affecting the fintech adoption intentions of MSMEs and determines the role of fintech knowledge as a moderating variable. Two hundred ninety one (291) culinary MSMEs from four locations in West Sumatra, Indonesia, participated in this survey. In addition, the SEM-PLS is employed to analyse the significant factors influencing fintech adoption among culinary MSMEs. Model evaluation is conducted to achieve a good-fitting model. The results show five direct relationships between factors and fintech adoption intentions: effort expectancy, social influence, facility condition, price value, and fintech knowledge. In addition, fintech knowledge moderates the relationship between performance expectancy and fintech adoption intentions. In this case, fintech knowledge strengthens the association between performance expectancy and fintech adoption intentions. Thus, fintech knowledge also plays as a moderating variable between effort expectancy and fintech adoption intentions. The finding is relevant to the government program aimed at increasing the fintech knowledge of culinary MSMEs. Fintech companies can improve the expectancy effort, facility condition, social influence, and price value to boost the intention to adopt Fintech. Theoretically, this study contributes to the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and diffusion of innovation theory. Acknowledgements Our paper is funded by the Ministry of Education, Culture, Research and Technology, Republic of Indonesia (no. 186/E5/PG.02.00.PT/2023). We acknowledge the Directorate of Higher Education, the Ministry of Education, Culture, Research and Technology, Republic of Indonesia, for research funding. We would also like to extend our gratitude to the Rector of Universitas Ekasakti and Universitas Bung Hatta, as well as the anonymous reviewers of this article, for their constructive suggestions.

In the face of growing competition in the transportation market, companies are looking for new ways to improve operational efficiency and reduce fleet maintenance costs. This article presents an innovative vehicle technical condition management model that describes a mechanism for assessing the condition of vehicles using distributed ledger technology (DLT) and smart contracts. An information system for automating maintenance is proposed that can perform monitoring functions and initiate vehicle maintenance without human intervention by automatically registering operation and maintenance events, as well as using smart contracts to launch predefined actions. This level of automation allows timely prevention of unplanned breakdowns, which directly contributes to an increase in the service life of vehicles. The proposed solution allows transport companies to automate decision-making processes on maintenance, reduce transport downtime and optimize operating costs. The model ensures transparency of vehicle operation data, increases trust in information and shortens the decision-making chain. The solution is of particular value for public transport companies, where uninterrupted transportation and passenger safety are critically important.

The aim of this study was to examine the determinants of the continuance intention with respect to use of the Audit Tools and Linked Archives System (ATLAS) by employing survey methods. These determinants are developed from an Expectation Confirmation Model (ECM). The sample of this study is auditors who use ATLAS in public accounting firms in Indonesia. As many as 356 data points can be processed using smartPLS. This study revealed that perceived usefulness, confirmation, information quality, top management commitment and satisfaction affected the auditor’s intentions when using ATLAS. The implications of this study are (1) Public accounting firms must provide full support to auditors in using ATLAS and equip auditors through training so auditors understand that using ATLAS is very useful; (2) IAPI must pay attention to outputs that are complete, good and appropriate so that the auditor is satisfied when using ATLAS. The auditor has a tendency to continue using ATLAS if he is satisfied.

Real estate market price forecasting is always in the focus of interests of scientists-economists, market analysts, market participants (sellers and buyers), marketing services of building complex enterprises, analysts working for banks and insurance companies and investors. Under present day conditions, the price behavior of properties on real estate markets takes especially important meaning subject to the influence of such factors as changes in the structure of household incomes, changes in mortgage rates and their availability, dynamic changes in the macroeconomic and other external socio-economic and political type factors. However, unlike the financial and securities markets, the real estate market is always characterized by a delayed reaction to external perturbations, often up to half a year, which allows us to hope for an appropriate construction of forecasts, at least in time for the delayed reaction. Traditional autoregressive forecasting methods are characterized by rapidly increasing forecast variance, because they assume a factor of stochastic volatility. This paper proposes a model and method of forecast construction based on stochastic processes of the “Poisson random index” having a short time for reaching a stationary stable variance. The model is based on the “principle of replacements” of current prices with new ones. We analyze in detail an example of the application of the “principle of replacements” for construction of price forecasts on secondary residential real estate in St. Petersburg which is based on data of four-year observations of offer prices.

Системы онлайн-рекрутмента за последние годы накопили огромное количество данных о реальном рынке труда. Особый интерес для исследования представляют данные о реальных требованиях рынка труда, содержащихся в текстах онлайн-вакансий, а также процесс их извлечения и структурирования для дальнейшего анализа и использования. Этап составления актуального списка требований для профиля должности в процессе подбора персонала является очень трудоемким и требует от HR-специалиста значительных усилий, связанных с мониторингом изменений целых отраслей и профессий, а также анализом востребованности и актуальности существующих на рынке требований. В данной статье автором предлагается концептуальная модель рекомендательной системы, позволяющая снизить нагрузку на HR-специалиста на этапе формирования актуального списка требований профиля должности в процессе подбора персонала. В основе модели предлагается использовать комбинацию графовой модели требований рынка труда на основе таксономии ESCO, адаптированной для русского языка, и интеллектуального метода формирования рекомендаций для составления актуального списка требований в процессе подбора персонала на основе нейросетевых моделей языка на архитектуре трансформеров, таксономии навыков ESCO и корпуса онлайн-вакансий российского рынка труда. Также в статье приводится концептуальный алгоритм работы рекомендательной системы и возможные варианты рекомендаций по актуализации списка требований профиля должности в процессе подбора персонала на основе анализа потребностей реального рынка труда.

It is common knowledge that information and communication technologies (ICTs) have made continuous inroads in the knowledge management field; thus, this study is modeled to examine the impact of ICTs on inter-organizational knowledge sharing (IOKS) and its subsequent effect on the growth of small and medium-sized enterprises (SMEs). The study adopts a descriptive survey design, using the quantitative research approach. Using the simple random sampling technique, a web-based questionnaire was used to collect data from 187 respondents. Results showed that IOKS among SMEs is not carried out to a great extent, which means that it is not a common practice among SMEs. Findings showed that less than half of the SMEs used training programs, internship programs, research collaboration and workshops for IOKS. It further showed that IOKS enhances sales, productivity, profit, organizational assets and equity. This study provides evidence of how ICT systems/tools have been used in IOKS and their impact on the growth of SMEs.

Удержание клиентов является одной из главных задача бизнеса, при этом крайне важно распределить ресурсы на удержание в соответствии с потенциальной прибыльностью потребителя. Чаще всего задача прогнозирования оттока клиентов решается на основе RFM (Recency, Frequency, Monetary) модели. В работе предлагается способ расширения RFM модели с помощью оценок вероятности изменения поведения клиента. На основе анализа данных о 33918 покупателях крупной российской торговой сети за 2019–2020 гг. показано, что существуют повторяющиеся паттерны изменения их поведения длительностью в один год. Информация об этих паттернах используется для вычисления необходимых оценок вероятности. Включение этих данных в предиктивную модель на основе логистической регрессии увеличивает точность прогнозирования более чем на 10% по метрикам AUC и геометрическое среднее. Показано также, что данный подход имеет ограничения, связанные с нарушением паттернов поведения в случае внешних шоков, таких как локдаун из-за пандемии COVID-19 в апреле 2020 г. В работе также предложен способ идентификации этих шоков, позволяющий спрогнозировать снижение предиктивной способности модели.

This article presents an approach to simulation of the rates of road traffic accidents involving unmanned ground vehicles within a multi-agent intelligent transportation system for the ‘smart city.’ A new simulation model of an intelligent transport system has been developed which makes it possible to significantly reduce the number of potential road traffic accidents (TAs) and implements the concept of socially-centered management of the urban economy. The software implementation of such a large-scale agent-based model was carried out using the FLAME GPU framework, which allows us to effectively parallelize the agents’ behaviour logic and consider their individual decision-making systems when modelling the spacial dynamics of an ensemble of unmanned ground vehicles (UGVs) interacting with other road users: the usual manned ground vehicles (MGVs), unexpected obstacles (e.g., pedestrians, etc.). Various scenarios of such agents’ behaviour in an intelligent transportation system are studied, including the occurrence of an accident under certain conditions (e.g., under the high speed and traffic intensity of UGVs, etc.) and various configurations of the digital road network (DRN). We determine the parameter values that provide for the individual decision-making system of UGVs remaining stable with respect to the characteristics of the external environment (including in extreme situations), ensuring the safety of other road users on the scale of the ‘smart city.’

This article is devoted to the development of a high-level simulation model of tourism industry dynamics. The purpose of this study is to form recommendations for the recovery of the tourism industry from the effects of the pandemic. The resulting model considers domestic tourism from the point of view of the interdependence of the economic condition of the state, the contribution of the tourism industry to gross domestic product, the size of the tourist flow and the average income per tourist. In addition to describing the functional dependencies of the model elements, several experiments are proposed to test the logic of the elements’ relationships. System dynamics tools are used to develop the model. The study also examines the class of computable general equilibrium models as a tool for analyzing supply and demand in the market of tourist products.

The modern global debt market features historically low average interest rates, convergence of yields on bonds with different maturities, an increase of yield curve inversion emergence frequency and a large-scale trend to automate financial decision making. The researchers’ attention in these fields is mainly focused on designing models that describe the state of the debt market as whole or its individual instruments in particular, as well as on risk management methods. At the same time, the specialized literature offers very few works concerning the topic of computer algorithms for bond portfolio selection based on traditional or advanced investment strategies. The aim of the present research is to create a modification of the existing algorithm of riding the yield curve strategy application, employing, first, average bond yield over the holding period instead of traditional bond yield to maturity; second, a developed algorithm for calculating the market spread on bonds; and, third, alternative risk evaluation indicators (compensation coefficients), which allow us to measure objectively price risk, liquidity risk, transaction costs risk and a general risk. The modification and the development of the algorithm for calculating the market spread were carried out using the direct measurement of the result technique, which entails application of the strategy to the data on bond issues received through the Moscow Exchange API. The selection of financial instruments was conducted in all sectors of the Russian debt market: public bonds, sub-federal and municipal bonds, corporate bonds. The modified algorithm enabled us to get extra yield for each selected bond issue, thereby proving the high effectiveness of the technique compared to the traditional strategy. Software implementation of the algorithm can be integrated into any robotized or semi-robotized stock exchange trading application.