Saeed Nosratabadi, Amir Mosavi, S. Shamshirband
et al.
During the past two decades of e-commerce growth, the concept of a business model has become increasingly popular. More recently, the research on this realm has grown rapidly, with diverse research activity covering a wide range of application areas. Considering the sustainable development goals, the innovative business models have brought a competitive advantage to improve the sustainability performance of organizations. The concept of the sustainable business model describes the rationale of how an organization creates, delivers, and captures value, in economic, social, cultural, or other contexts, in a sustainable way. The process of sustainable business model construction forms an innovative part of a business strategy. Different industries and businesses have utilized sustainable business models’ concept to satisfy their economic, environmental, and social goals simultaneously. However, the success, popularity, and progress of sustainable business models in different application domains are not clear. To explore this issue, this research provides a comprehensive review of sustainable business models literature in various application areas. Notable sustainable business models are identified and further classified in fourteen unique categories, and in every category, the progress -either failure or success- has been reviewed, and the research gaps are discussed. Taxonomy of the applications includes innovation, management and marketing, entrepreneurship, energy, fashion, healthcare, agri-food, supply chain management, circular economy, developing countries, engineering, construction and real estate, mobility and transportation, and hospitality. The key contribution of this study is that it provides an insight into the state of the art of sustainable business models in various application areas and future research directions. This paper concludes that popularity and the success rate of sustainable business models in all application domains have been increased along with the increasing use of advanced technologies.
S. M. H. Bamakan, Nasim Nezhadsistani, Omid Bodaghi
et al.
With the explosive development of decentralized finance, we witness a phenomenal growth in tokenization of all kinds of assets, including equity, funds, debt, and real estate. By taking advantage of blockchain technology, digital assets are broadly grouped into fungible and non-fungible tokens (NFT). Here non-fungible tokens refer to those with unique and non-substitutable properties. NFT has widely attracted attention, and its protocols, standards, and applications are developing exponentially. It has been successfully applied to digital fantasy artwork, games, collectibles, etc. However, there is a lack of research in utilizing NFT in issues such as Intellectual Property. Applying for a patent and trademark is not only a time-consuming and lengthy process but also costly. NFT has considerable potential in the intellectual property domain. It can promote transparency and liquidity and open the market to innovators who aim to commercialize their inventions efficiently. The main objective of this paper is to examine the requirements of presenting intellectual property assets, specifically patents, as NFTs. Hence, we offer a layered conceptual NFT-based patent framework. Furthermore, a series of open challenges about NFT-based patents and the possible future directions are highlighted. The proposed framework provides fundamental elements and guidance for businesses in taking advantage of NFTs in real-world problems such as grant patents, funding, biotechnology, and so forth.
Language agents, built on top of language models (LMs), are systems that can interact with complex environments, such as the open web. In this work, we examine whether such agents can perform realistic and time-consuming tasks on the web, e.g., monitoring real-estate markets or locating relevant nearby businesses. We introduce AssistantBench, a challenging new benchmark consisting of 214 realistic tasks that can be automatically evaluated, covering different scenarios and domains. We find that AssistantBench exposes the limitations of current systems, including language models and retrieval-augmented language models, as no model reaches an accuracy of more than 25 points. While closed-book LMs perform well in terms of accuracy, they exhibit low precision and tend to hallucinate facts. State-of-the-art web agents reach a score of near zero. Additionally, we introduce SeePlanAct (SPA), a new web agent that significantly outperforms previous agents, and an ensemble of SPA and closed-book models reaches the best overall performance. Moreover, we analyze failures of current systems and highlight that open web navigation remains a major challenge.
The OECD Science, Technology and Industry Scoreboard 2017 draws on the latest international comparative data to uncover the strength of the OECD and other large economies, and shows how the digital transformation is affecting science, innovation, the economy and the way people work and live. Mobility, cloud computing, the Internet of Things (IoT), artificial intelligence (AI) and big data analytics are among the most important technologies in the digital economy today, empowering businesses, consumers, and society as a whole. However, their development and use are distributed very unevenly. The headquarters of the top 2,000 R&D corporations worldwide are concentrated in just a few economies – notably the United States, Japan, and China – and about 70% of their total R&D spending is concentrated in the top 200 firms. Although the digital transformation is affecting all sectors of the economy, Telecommunications and IT services are consistently ahead in terms of digital intensity, while Agriculture, Mining, and Real estate are consistently ranked at the bottom. Significant differences remain in a majority of OECD countries, including between younger and older generations, between women and men, by educational background, urban and local locations, and firms of different size. This publication aims to help governments design more effective science, innovation, and industry policies in the digital era.
Alin-Gabriel Vaduva, Simona-Vasilica Oprea, Adela Bara
Explainable Artificial Intelligence (XAI) methods (SHAP, LIME) are increasingly adopted to interpret models in high-stakes businesses. However, the credibility of these explanations, their stability under realistic data perturbations, remains unquantified. This paper introduces the Credibility Index via Explanation Stability (CIES), a mathematically grounded metric that measures how robust a model's explanations are when subject to realistic business noise. CIES captures whether the reasons behind a prediction remain consistent, not just the prediction itself. The metric employs a rank-weighted distance function that penalizes instability in the most important features disproportionately, reflecting business semantics where changes in top decision drivers are more consequential than changes in marginal features. We evaluate CIES across three datasets (customer churn, credit risk, employee attrition), four tree-based classification models and two data balancing conditions. Results demonstrate that model complexity impacts explanation credibility, class imbalance treatment via SMOTE affects not only predictive performance but also explanation stability, and CIES provides statistically superior discriminative power compared to a uniform baseline metric (p < 0.01 in all 24 configurations). A sensitivity analysis across four noise levels confirms the robustness of the metric itself. These findings offer business practitioners a deployable "credibility warning system" for AI-driven decision support.
Julius Sechang Mboli, John G. O. Marko, Rose Anazin Yemson
The explorations and applications of Artificial Intelligence (AI) in various domains becomes increasingly vital as it continues to evolve. While much attention has been focused on Large Language Models (LLMs) such as ChatGPT, this research examines the readiness of other LLMs such as Google Gemini (previously Google BARD), a conversational AI chatbot, for potential business applications. Gemini is an example of a Generative AI (Gen AI) that demonstrates capabilities encompassing content generation, language translation, and information retrieval. This study aims to assess its efficacy for text simplification in catering to the demands of modern businesses. A dataset of 42,654 reviews from distinct Disneyland branches was employed. The chatbot's API was utilised with a uniform prompt to generate simplified re-views. Results presented a spectrum of responses, including 75% successful simplifications, 25% errors, and instances of model self-reference. Quantitative analysis encompassing response categorisation, error prevalence, and response length distribution was conducted. Furthermore, Natural Language Processing (NLP) metrics were applied to gauge the quality of the generated content with the original reviews. The findings offer insights into Gen AI models performance, highlighting proficiency in simplifying re-views while unveiling certain limitations in coherence and consistency since only about 7.79% of the datasets was simplified. This research contributes to the ongoing discourse on AI adoption in business contexts. The study's out-comes provide implications for future development and implementation of AI-driven tools in businesses seeking to enhance content creation and communication processes. As AI continues to transform industries, an understanding of the readiness and limitations of AI models is essential for informed decision-making, automations and effective integration.
Entrepreneurship education equips students to transform innovative ideas into actionable entrepreneurship plans, yet traditional approaches often struggle to provide the personalized guidance and practical alignment needed for success. Focusing on the business plan as a key learning tool and evaluation method, this study investigates the design needs for an AI-empowered scaffold system to address these challenges. Based on qualitative insights from educators and students, the findings highlight three critical dimensions for system design: mastery of business plan development, alignment with entrepreneurial learning goals, and integration of adaptive system features. These findings underscore the transformative potential of AI in bridging gaps in entrepreneurship education while emphasizing the enduring value of human mentorship and experiential learning.
The rapid advancement of LLMs sparked significant interest in their potential to augment or automate managerial functions. One of the most recent trends in AI benchmarking is performance of Large Language Models (LLMs) over longer time horizons. While LLMs excel at tasks involving natural language and pattern recognition, their capabilities in multi-step, strategic business decision-making remain largely unexplored. Few studies demonstrated how results can be different from benchmarks in short-term tasks, as Vending-Bench revealed. Meanwhile, there is a shortage of alternative benchmarks for long-term coherence. This research analyses a novel benchmark using a business game for the decision making in business. The research contributes to the recent literature on AI by proposing a reproducible, open-access management simulator to the research community for LLM benchmarking. This novel framework is used for evaluating the performance of five leading LLMs available in free online interface: Gemini, ChatGPT, Meta AI, Mistral AI, and Grok. LLM makes decisions for a simulated retail company. A dynamic, month-by-month management simulation provides transparently in spreadsheet model as experimental environment. In each of twelve months, the LLMs are provided with a structured prompt containing a full business report from the previous period and are tasked with making key strategic decisions: pricing, order size, marketing budget, hiring, dismissal, loans, training expense, R&D expense, sales forecast, income forecast The methodology is designed to compare the LLMs on quantitative metrics: profit, revenue, and market share, and other KPIs. LLM decisions are analyzed in their strategic coherence, adaptability to market changes, and the rationale provided for their decisions. This approach allows to move beyond simple performance metrics for assessment of the long-term decision-making.
Predictive Business Process Monitoring (PBPM) aims to forecast future outcomes of ongoing business processes. However, existing methods often lack flexibility to handle real-world challenges such as simultaneous events, class imbalance, and multi-level attributes. While prior work has explored static encoding schemes and fixed LSTM architectures, they struggle to support adaptive representations and generalize across heterogeneous datasets. To address these limitations, we propose a suite of dynamic LSTM HyperModels that integrate two-level hierarchical encoding for event and sequence attributes, character-based decomposition of event labels, and novel pseudo-embedding techniques for durations and attribute correlations. We further introduce specialized LSTM variants for simultaneous event modeling, leveraging multidimensional embeddings and time-difference flag augmentation. Experimental validation on four public and real-world datasets demonstrates up to 100% accuracy on balanced datasets and F1 scores exceeding 86\% on imbalanced ones. Our approach advances PBPM by offering modular and interpretable models better suited for deployment in complex settings. Beyond PBPM, it contributes to the broader AI community by improving temporal outcome prediction, supporting data heterogeneity, and promoting explainable process intelligence frameworks.
Shanping Hu, Wafa Ghardallou, Rebecca Kechen Dong
et al.
In the realm of organizational life, exchanging different resources is crucial for the success and survival of both the employer and the employee. Green voice behavior (GVB), leader-member exchanges (LMX), and perceived green organizational support (PGOS) form a part of those exchanges, i.e., munificent and constrained conditions of these resources have implications for the organizational stakeholders. To better understand those implications, we have utilized the resource theory of social exchange while delineating the relationship dynamics between environmentally specific ethical leadership (ESEL) and GVB. By examining the moderating roles of leader-member exchange (LMX) and perceived green organizational support (PGOS), we address significant gaps in understanding the mechanisms that enhance ESEL impact on GVB in organizations. A time-lagged survey of 304 middle management employees from Karachi's petroleum sector was analyzed using SPSS and AMOS. Results indicate that when leader-employee and organization-employee-based resources, i.e., LMX and PGOS were readily available in plentiful condition, employees were also generous in offering their possessed resource, i.e., green voice in response to ESEL. Whereas a resource constrained condition from leaders and organization as a whole was unable to keep employees munificent in speaking up green ideas, hence the prediction of 3-way interaction effects has been validated in the context of petroleum industry in Pakistan. The study shows a three-way interaction effect where leader-member exchange compensates for limited organizational resources.
This paper considers the attenuation of agglomeration economies. Put another way: how close is close? The paper presents evidence of agglomeration effects operating at various levels of spatial aggregation, including the regional, metropolitan, and neighborhood scales. In fact, agglomeration effects also seem to operate below the neighborhood level, including within buildings and organizations. These effects attenuate, with nearby activity exerting the strongest effects. The attenuation of agglomeration economies has implications for urban spatial structure, the microfoundations of agglomeration economies, and commercial real estate. It also affects the ability of governments and businesses to internalize agglomeration economies.
The real estate sector plays a vital role in China's economy and is a crucial engine of economic growth. The massive investment and construction activity has promoted the upgrading of infrastructure and the development of manufacturing, construction, and related services, thus strongly supporting the expansion of the overall economy. However, with the rapid growth of the real estate market, some problems that cannot be ignored have also been exposed, including rising housing prices, intensified real estate "speculation," and the environmental impact of real estate projects. These problems may threaten the financial system's stability and negatively impact social equity and the ecological environment. This article aims to delve into the main issues facing the real estate industry today, explore their causes, and propose practical countermeasures, aiming to promote the development of the real estate industry in a healthier and more sustainable model to promote long-term stable economic growth.
Bastián Real, Pablo Solano, Carla Hermann-Avigliano
Controlling light propagation in photonic systems fosters fundamental research and practical application. Particularly, photonic lattices allow engineering band dispersions and tailor transport features through their geometry. However, complete controllability requires external manipulation of the propagating light. Here, we present a resonant excitation scheme to observe quasi-1D and uni-directional propagation of light through the bulk of two-dimensional lattices. To this end, we use the highly anisotropic light propagation exhibited at the energy of saddle points in photonic bands. When multiple drives with judicious amplitudes and phases are tuned to such energy, interference effects between these drives and photonic modes result in controllable directional propagation through the bulk. Similarly, one can formed localized states with controllable localization degrees. We illustrate these effects with driven-dissipative photonic lattices. Our work highlights the importance of external drives for dynamically controlling directional light transport in lattices, a relevant feature for all-optical routing and processing in photonics.
This paper presents a chronology, beginning in the early 1900s, of the regulatory environment faced by residential real estate appraisers in the United States. The presentation informs the reader about two financial crises, the savings and loan crisis and the Subprime mortgage crisis. The conditions that led to each crisis, the response of Congress to address each crisis, and the effect of both on residential real estate appraisers are included in the presentation. Prior to these crises, appraisers were basically self-regulated, but today because of concern that inflated appraisals were a contributing cause of the crises they are subject to rules and regulations imposed by both federal, and state authorities. The regulatory measures instituted to address the savings and loan crisis failed to prevent the subsequent Subprime mortgage crisis, and measures instituted to end the Subprime mortgage crisis had unintended negative effects. This begs the question, will the regulations in place now prevent the occurrence of a future crisis.
In order to launder large amounts of money, (drug) criminals can seek help from financial facilitators. According to the FATF, these facilitators are operating increasingly business-like and even participate in professional money laundering networks. This study examines the extent to which financial facilitators in the Netherlands exhibit business-like characteristics and the extent to which they organize themselves in money laundering networks. We further examine the relationship between business-like behavior and individual money launderers’ position in the social network. Using police intelligence data, we were able to analyze the contacts of 198 financial facilitators who were active in the Netherlands in the period 2016–2020, all having worked for drug criminals. Based on social network analysis, this research shows that financial facilitators in the Netherlands can be linked in extensive money laundering networks. Based on the facilitators’ area of expertise, roughly two main types of professional money laundering networks can be discerned. Some subnetworks operate in the real estate sector, while others primarily engage in underground banking. Furthermore, the application of regression models to predict business-like behavior using individual network measures shows that facilitators with more central positions in the network and those who collaborate with financial facilitators from varying expertise groups tend to behave more business-like than other financial facilitators.
An unfriendly partition of a graph $G = (V,E)$ is a function $c: V \to 2$ such that $|\{x\in N(v): c(x)\neq c(v)\}|\geq |\{x\in N(v): c(x)=c(v)\}|$ for every vertex $v\in V$, where $N(v)$ denotes its neighborhood. It was conjectured by Cowen and Emerson that every graph has an unfriendly partition, but Milner and Shelah found counterexamples for that statement by analyzing graphs with uncountably many vertices. Curiously, none of their graphs have vertices with finite degree. Therefore, as a natural direction to approach, in this paper we search for the least cardinality of a graph with that property that admits no unfriendly partitions. Actually, among some other independence results, we conclude that this size cannot be determined from the usual axioms of set theory.
Smart roads have become an essential component of intelligent transportation systems (ITS). The roadside perception technology, a critical aspect of smart roads, utilizes various sensors, roadside units (RSUs), and edge computing devices to gather real-time traffic data for vehicle-road cooperation. However, the full potential of smart roads in improving the safety and efficiency of autonomous vehicles only can be realized through the mass deployment of roadside perception and communication devices. On the one hand, roadside devices require significant investment but can only achieve monitoring function currently, resulting in no profitability for investors. On the other hand, drivers lack trust in the safety of autonomous driving technology, making it difficult to promote large-scale commercial applications. To deal with the dilemma of mass deployment, we propose a novel smart-road vehicle-guiding architecture for vehicle-road cooperative autonomous driving, based on which we then propose the corresponding business model and analyze its benefits from both operator and driver perspectives. The numerical simulations validate that our proposed smart road solution can enhance driving safety and traffic efficiency. Moreover, we utilize the cost-benefit analysis (CBA) model to assess the economic advantages of the proposed business model which indicates that the smart highway that can provide vehicle-guided-driving services for autonomous vehicles yields more profit than the regular highway.
The purpose of the article is to develop a classification of the goals for valuation of all kinds of real and financial assets and businesses. Business valuation may be used for different directions and plays a key role in many segments of the financial industry: corporate finance, mergers and acquisitions, and portfolio management. The study used such scientific methods as analysis and synthesis of results, logic and analytical methods. Proposed classification of the goals for valuation of all kinds of real and financial assets and businesses involves the allocation of such assessment goals as business creation and acquisition; sale of business or real assets; increase equity and debt financing; property damage assessment; estate investment planning; liquidation or reorganization of the enterprise; compliance with legal requirements and court proceedings; pledge of state property. Authors have researched the different goals of valuation, taking into account the legislation of Ukraine.
This article describes one of the ways of solving the problem of housing affordability for regions where individual housing construction prevails. According to the National Project “Housing and Urban Environment” by 2030, the annual commissioning of housing in the country should be up to 120 million square metres, of which more than a third should be for individual housing construction. Statistics show that 68 % of Russian families consider an individual house the most preferable type of housing. The transition to the cluster system of territorial development is aimed at improving the efficiency and competitiveness of projects in the field of individual housing construction. The ideology of cluster development projects implies the creation of a common social environment for all residents of the settlement, the main goal of which is: the development of commercial and social infrastructure within the cluster. The main example of this approach is an infrastructure node located within a low-rise development. Within this approach, several small settlements are gathered around one infrastructural core, within which commercial and social infrastructure is located. The concept of cluster development of territories will allow optimizing the costs of infrastructure development not only for one single settlement, but will also contribute to the development of the entire territory. It is assumed that in the future the scheme of cluster development of territories will become the main principle of suburban real estate development. Another indirect advantage of cluster development of territories is the opportunity for the developer to apply additional methods to increase the profitability of their projects. The most popular of them is the sale of ready-made houses with finishing, landscaping, furniture and additional options. As a result of the formation of a cluster, common property is formed, for the management of which a management company is needed, a specialized management company can be attracted to manage the cluster of individual housing construction, which, in addition to the main services, will provide additional services for the maintenance of personal plots. Transition to the cluster scheme for the development of territories will significantly improve the quality of life of people who consider individual housing construction as a place for permanent residence.