This opinion paper examines the transformative relationship between Generation Z and business information, arguing that this cohort is pioneering a bottom-up, information-driven approach to economic self-reliance. Unlike previous generations, Gen-Z leverages a diverse digital ecosystem from social media platforms to specialized forums to gather, synthesize, and apply business intelligence for career advancement and venture creation. While this represents a significant democratization of entrepreneurial opportunity, it also presents critical challenges, including information overload, credibility assessment, and a potential foundational skills gap. This paper discusses these promises and perils, contending that the current institutional support systems are lagging behind these informal learning models. The conclusion offers a multi-stakeholder call to action, proposing that educators, policymakers, and business leaders must collaboratively build a scaffolded ecosystem to effectively harness Gen-Z’s unique information sensibilities for sustainable economic growth.

Investigating the intensity and drivers of formalization in urban informal businesses: evidence from Ethiopia

In today’s dynamic business environment, Information and Communication Technology (ICT) serves as a pivotal force driving innovation, particularly in human resource management. One notable advancement is the emergence of smart resume applications, which are reshaping traditional recruitment practices. These systems utilize advanced technologies such as artificial intelligence (AI), machine learning, and natural language processing (NLP) to facilitate the automation of resume screening and candidate evaluation processes. This paper presents the development of an Automated Resume Parsing designed to reduce the operational load on human resource professionals. By utilizing NLP techniques, the system extracts key candidate information including name, contact details, educational background, and skills from the resume documents. A custom Named Entity Recognition (NER) model is employed to enhance the accuracy and relevance of extracted data. The model was trained using the SpaCy NLP framework to achieve an overall accuracy of 92.4% and an F1-score of 0.90. The extracted data are presented through an interactive web interface for HR personnel, enabling structured and efficient review of applicant information. The results demonstrate that integrating NLP and machine learning in recruitment systems can significantly enhance automation, consistency, and fairness in candidate evaluation processes. The system's effectiveness was demonstrated through successful extraction and structured presentation of applicant information, aligned with organizational hiring criteria. Beyond technical functionality, this study highlights the potential of such human-centered technologies to enhance decision-making, increase recruitment efficiency, and transform the recruiter-applicant interaction by fostering transparency and fairness in the hiring process.

Russia’s modern economy is currently undergoing rapid change driven by multifaceted foreign and domestic policy challenges. One of the industries most vulnerable to transformation under these conditions is the food service industry, where changes are occurring not only in supply chains, cost structures, and tax systems, but also in business formats and consumer behavior. Developing culinary tourism in the regions is crucial, as it can serve as a key driver of their economic development. The purpose of this study is to substantiate the need for monitoring and assessing the regional food service market and to develop recommendations for its transformation in the context of sanctions, using the Nizhny Novgorod Region as an example. The objectives of the study include systematizing the definitions and functions of the food service industry, as implemented in the Nizhny Novgorod Region; assessing the impact of food service on the regional economy; examining macroeconomic indicators and structural changes in the food service industry; identifying key challenges and developing recommendations for addressing them for the development of the restaurant business in the context of global economic transformation. The study systematizes definitions of the concept of "catering", identifies its essence, which leads to the emergence of gastronomic tourism in the region, and provides a number of applied functions reflecting the specifics of catering implementation. The author also identified the main challenges facing the restaurant business in the Nizhny Novgorod Region, including a high tax burden and a shortage of qualified personnel. Based on these identified challenges, the author proposes recommendations for addressing them through government support for small and medium-sized businesses, such as the restaurant industry. These recommendations include expanding grant programs, reducing administrative barriers, and promoting professional education in the restaurant industry.

Enterprise architecture plays an important role in aligning business strategy and information technology. However, comprehensive frameworks such as TOGAF 10 are often perceived as complex due to extensive artifact requirements and high resource demands, which create challenges for organizations with limited resources. Therefore, this study aims to examine the role of lean and agile principles in simplifying enterprise architecture through the Mini-TOGAF approach to improve implementation efficiency and adaptability. This research adopts a design science research approach, focusing on the design and development of the Mini-TOGAF framework as a conceptual artifact. To establish a solid theoretical foundation and systematically define the components of Mini-TOGAF, a systematic literature review was conducted. The findings indicate that lean principles simplify enterprise architecture by eliminating waste through “just enough” documentation and by consolidating and prioritizing artifacts based on core business value. In addition, Agile principles ensure that the resulting architecture remains flexible and outcome-oriented through iterative development, rapid adaptation to change, and continuous stakeholder involvement. This study concludes that Mini-TOGAF represents a pragmatic and effective adaptation of TOGAF 10, enabling more efficient, agile, and strategically aligned enterprise architecture implementation for resource-constrained organizations.

The rapid advancement of digital technologies has fundamentally transformed modern business operations, making digitalization and information systems essential components of organizational success. This study aims to examine the importance of digitalization and information systems in business through a comprehensive literature review. Using a qualitative research approach, relevant academic articles, books, and conference proceedings were analyzed to identify key themes related to business performance, decision-making, competitive advantage, and implementation challenges. The findings reveal that digitalization significantly enhances operational efficiency, productivity, and customer responsiveness, while information systems such as management information systems, decision support systems, and enterprise resource planning systems play a crucial role in supporting data-driven decision-making and organizational integration. Additionally, the review highlights that digitalization contributes to innovation and sustained competitive advantage when aligned with organizational capabilities and strategic objectives. However, challenges including high implementation costs, skills gaps, cybersecurity risks, and resistance to change remain significant barriers, particularly for small and medium-sized enterprises. This study contributes to the existing body of knowledge by synthesizing fragmented literature and providing a holistic understanding of the strategic importance of digitalization and information systems in business. The findings offer valuable insights for researchers, practitioners, and policymakers seeking to navigate digital transformation effectively.

The paper examines the Sudirman Loop project in Makassar as a pilot project of sustainable urban mobility. As the city government aims to promote the development of the city towards effective pedestrian and transit orientation, the Sudirman Loop was evaluated based on the field surveys, aerial drone recording, review of policies, and interviews with the main stakeholders. The results demonstrate that the current pedestrian infrastructure is below the basic safety, accessibility, and inclusiveness thresholds. Pavements frequently get destroyed, clogged with trees, kiosks, or parked cars, and do not provide accommodations to the disabled, including the use of tactile surfaces and ramps. Drainages are not covered, there are no crossings at major intersections, and the pedestrian areas are often abused by informal businesses. These circumstances undermine comfort and safety, especially among the vulnerable groups. A critical analysis framework shows that the majority of pedestrian-related facilities are half-heartedly enforced or not enforced at all, with the shortcomings being in terms of accessibility, continuity, and social interaction. Although the corridor is strategically positioned and symbolically important, redesigning it comprehensively, enforcing more stringent regulations, and maintaining it are all urgent needs. The research finds that the Sudirman Loop is a good opportunity to implement sustainable mobility, yet it should be accompanied by physical interventions and institutional dedication. What can be learned in this case is useful in coming up with replicable models in other cities in Indonesia.

Under the current macro trend of deep integration of global green transformation and digital revolution, the relationship between the digital economy and environmental sustainability has become a cutting-edge issue as a key force in reshaping the economic and social form. However, digital technology is a “double-edged sword”, which may also be accompanied by new resource consumption and governance challenges while enabling efficiency improvement and model innovation. Although existing studies have focused on the impact of digital economy on green development, most of them focus on the discussion of linear relationships, and lack of systematic testing of their inherent complex mechanisms, especially ignoring the nonlinear moderating effects that may arise from institutional factors such as environmental regulation, as well as the structural constraints caused by the dependence on traditional development paths. Based on this, this study aims to construct an integrated analysis framework including direct effects, nonlinear thresholds and structural constraints, and deeply reveal the complex mechanism of digital economy development affecting green development efficiency. Based on the panel data from 2011 to 2022 in 30 provinces in China, this study uses the two-way fixation effect model, panel threshold model and dynamic lag model to conduct an empirical test. The results show that, firstly, the development of digital economy has a significant and continuous direct promotion effect on green development efficiency, and this conclusion is still stable after endogenous treatment and lag effect analysis, which confirms that digital technology effectively drives the “decoupling” of economic growth and ecological pressure by optimizing resource allocation, improving energy efficiency and promoting green business formats. Secondly, the intensity of environmental regulation plays a key nonlinear moderating role in the relationship between the two, showing a significant single threshold effect: when the intensity of environmental regulation is in a reasonable range (below the threshold value), it forms an “incentive compatibility” effect with the digital economy, jointly promoting the improvement of green efficiency. However, when the intensity of regulation exceeds the critical point, excessive compliance costs and innovation crowding out effects will weaken the green empowerment potential of the digital economy, resulting in a diminishing marginal contribution. Finally, the study also identifies important structural constraints: the traditional extensive urbanization model and the under-optimized government intervention mode have a significant inhibitory effect on green efficiency, which highlights the necessity of collaborative promotion of governance and development mode transformation in the process of digital transformation. The contribution of this study is not only to empirically verify the green dividend of the digital economy, but more importantly, to reveal the institutional conditions and structural constraints of this dividend release, breaking through the linear assumptions of previous studies. The conclusion provides multi-dimensional enlightenment for policy formulation: at the strategic level, we should adhere to the synergy of digitalization and greening; At the regulatory level, it is necessary to implement precise and differentiated environmental policies and seek a dynamic balance between “incentive innovation” and “strict constraints”; At the path level, it is necessary to promote the transformation of urbanization and government governance to a high-quality model, so as to systematically unlock the green development potential of the digital economy.

Micro-enterprises play a strategic role in the Indonesian economy, particularly in rural areas. However, limited resources and a low level of financial management understanding often lead to operational cost inefficiencies. This study aims to analyze the implementation of a cost control system in improving the efficiency of a micro-enterprise, focusing on a grocery business owned by Mrs. Delin in Tuni Hamlet. The study employs a descriptive qualitative approach using a case study method. Data were collected through in-depth interviews and direct observation. Data analysis was conducted using the Miles and Huberman model, which includes data reduction, data display, and conclusion drawing. The results indicate that the implementation of a simple cost control system through expense recording, budgeting, and expenditure evaluation can improve operational efficiency, as reflected by reduced waste and better cost management. The main challenges in implementing cost control include limited accounting knowledge and informal business management habits. This study is expected to serve as a reference for micro-enterprise owners in improving efficiency through simple and applicable cost control practices.

Purpose: This study aimed to investigate the influence of social factors on the operation of hairdressing salons within the Central Business District (CBD) of Lira City, Uganda. Methodology: A mixed-methods approach was employed, utilizing questionnaires for salon operators and key informant interviews with salon owners, targeting a total sample size of 191 respondents. Findings: Findings revealed that social expectations, such as respect for elders, community identity, and cooperation, play a crucial role in shaping the quality of service and client relations. Lifestyle trends, especially the shift toward beauty enhancement, digital engagement, and cultural pride, significantly influence customer preferences and salon innovation. Interpersonal relationships, including family and community ties, were shown to foster customer loyalty, staff morale, and overall business sustainability. Unique Contribution to Theory, Policy and Practice: The findings provide valuable insights for entrepreneurs, researchers, and development partners interested in the intersection between social behavior and informal business operation. It is thus, recommended that enhanced training on cultural sensitivity, digital marketing, and community engagement for salon practitioners. Policymakers and educational institutions are encouraged to support the sector through strategic interventions and vocational education tailored to these emerging social dynamics.

This study investigates how generative AI tools such as Research Rabbit, Elicit, and Litmaps, can support the development of business information literacy among undergraduate and graduate students. Conducted in two writing-intensive courses, one in a business research methods course and one in an honors course, the mixed-methods study explores students’ engagement with AI-powered tools during the literature review process. Using pre/post surveys, tool usage tracking, and interviews, the study assesses changes in efficiency, comprehension, and research skill development. Findings suggest that AI tools enhanced students’ ability to identify relevant literature, visualize research networks, and organize information by theme. However, limitations were observed in source verification, workflow integration, and tool performance in niche subject areas. Students valued AI tools as complements, not replacements, to traditional library databases and research support services. The study concludes that when AI tools are introduced with instructional scaffolding, they can enhance research confidence, organization, and efficiency while maintaining critical research competencies. Implications for librarians include embedding AI literacy into instruction, aligning tool use with learning outcomes, and developing rubrics that account for ethical and effective tool integration. This research provides a framework for supporting AI-enhanced research while reinforcing foundational business information literacy skills.

Employment serves as the foundation of people's livelihood and the source of economic vitality. The employment of university students is directly related to the stability of the social structure and the momentum for national innovation and development. Against the backdrop of the digital economy restructuring the industrial ecosystem, systematically deconstructing the mechanism through which emerging business formats influence the career decision-making of university students holds significant practical importance. The development of digital new business formats has opened up flexible and diversified career paths for university students while simultaneously posing structural challenges to the traditional paradigm of employment choices. Based on this, this paper analyzes the opportunities and challenges brought about by the development of digital new business formats to the employment choices of university students. It proposes optimization strategies from the dimensions of education, industry, and policy, aiming to provide a systematic solution for enhancing the career competitiveness of the university student population in the era of the digital economy.

The economic landscape of the United States in the mid-2020s is defined by a profound "vibecession" a statistical paradox wherein robust macroeconomic indicators, particularly record-breaking business formation, coexist with historically depressed consumer sentiment. As of late 2025, U.S. Census Bureau data indicates a surge in high-propensity business applications, specifically in the retail and professional services sectors, signaling a revitalized entrepreneurial ecosystem. However, the University of Michigan Index of Consumer Sentiment (ICS) remains entrenched at recessionary levels, revealing a decoupling of psychological economic outlook from transactional reality. This divergence suggests that classical predictive models, which rely on linear rationality and historical precedence, are increasingly insufficient for capturing the non-linear, entangled nature of modern consumer decision-making. This research paper introduces a Quantum-Enhanced Framework for Consumer Behavior Prediction (QE-CBP), a novel methodological approach that integrates Quantum Machine Learning (QML) algorithms with traditional econometric analysis to empower U.S. entrepreneurs. By leveraging the principles of quantum mechanics—superposition, entanglement, and interference—this framework addresses the high-dimensionality and sparsity inherent in modern e-commerce data. We employ Quantum Support Vector Machines (QSVM) for precise customer segmentation, Hybrid Quantum-Classical Neural Networks (HQCNN) for purchase intent classification, and Quantum Reinforcement Learning (QRL) for dynamic pricing strategies. Through a rigorous analysis of 2025 Business Formation Statistics, historical consumer sentiment data, and micro-level e-commerce transaction logs, this study demonstrates that quantum-enhanced models can identify latent behavioral clusters and predict "impulsive high-value" purchasing anomalies that classical models dismiss as noise. The findings provide a strategic roadmap for startups to transcend survival metrics and achieve market resilience, arguing that the integration of quantum decision intelligence is not merely a technological upgrade but a fundamental necessity for navigating the stochastic complexity of the post-2025 economy.

The rapid growth of mobile financial services in emerging economies presents a vital opportunity to assess their impact on informal business performance, yet current empirical evidence remains mixed. Utilizing mixed methods, including quantitative analysis of the World Bank's 2023 Informal Enterprise Survey and qualitative interviews in Zambia and Kenya, this study investigates this relationship. Our analysis, which considers endogeneity, confirms a strong positive effect of mobile money adoption on the profitability of informal businesses. However, this benefit largely depends on the ability of businesses to overcome additional operational challenges. Importantly, we find that mobile money usually supports, rather than replaces, formal financial services, with adoption closely linked to existing practices such as bank account ownership. Despite its potential, the adoption rate remains relatively low due to transaction fees, security concerns, fraud, and infrastructure issues. This gap between high impact for adopters and low overall penetration is a key insight. It challenges overly optimistic views of technology and emphasizes that the transformative power of mobile money is influenced by structural factors. By resolving conflicting evidence and highlighting this conditional reality, this study provides a detailed evidence base for financial inclusion efforts, stressing that tackling systemic obstacles is crucial for achieving broader development goals.

In the era of deepening the "New Liberal Arts" construction and the high-quality development of the sports industry, the explosive growth of new business formats in the sports industry poses brand-new challenges to the competency structure of practitioners. However, current sports management courses in higher education institutions remain deeply locked into the "Skill-Based" paradigm. Structural drawbacks such as fragmented teaching objectives, lagging content, unidirectional teaching processes, and low-order evaluation systems are prevalent, leading to a severe "supply-demand mismatch" between talent training and actual industrial needs. Based on the philosophy of Outcome-Based Education (OBE), this study adopts the Action Research method, utilizing core courses such as Appreciation of Sports Business Cases as the practical field to conduct a two-year empirical exploration of teaching paradigm transformation. Strictly following the spiraling upward logic of "Plan-Action-Observe-Reflect," and through three iterative cycles, the study systematically constructs a "Competency-Core" objective system covering Knowledge, Skills, and Attitudes (KSA). It reconstructs modular curriculum content based on deep industry-education integration, innovates a "Dual Mentor + Full-Cycle Project-Driven" teaching mode, and establishes a whole-process evaluation mechanism focusing on student growth increments. The study finds that the leap from "Skill" to "Competency" not only effectively enhances students' comprehensive literacy in solving complex, unstructured business problems and achieves deep internalization of interdisciplinary knowledge, but also promotes the reshaping of teachers' roles from "knowledge transporters" to "learning designers." This provides a replicable paradigm reference and theoretical evidence for the cultivation of interdisciplinary composite talents in sports and economics/management in China.

The advent of artificial intelligence-driven chatbots has brought about a paradigm shift in online shopping. This study employs Structural Equation Modeling (SEM) to comprehensively investigate the transformative potential of AI chatbots in e-commerce. We delve into three critical dimensions: user satisfaction, choice dynamics, and web insights, with SEM serving as a robust analytical framework to understand the complex interplay of variables. Our findings reveal intricate relationships between AI chatbot interactions and user satisfaction, shedding light on the factors that significantly impact users' overall contentment. Moreover, we explore how AI chatbots influence the decision-making processes of online shoppers, uncovering the nuances of choice dynamics in the digital retail landscape. Furthermore, SEM allows us to extract valuable web insights from the data generated by chatbot interactions, offering actionable information for businesses to optimize their online shopping platforms. By integrating SEM analysis into our research, this study provides a holistic understanding of how AI chatbots are reshaping online shopping, offering insights that can guide businesses in enhancing customer experiences and driving success in the digital marketplace

The current research paper aims to examine the role of antecedents of Business Information Technology (BIT) alignment encompassing the topics of shared domain knowledge, Information Technology (IT) success, organization size, IT management sophistication, employee satisfaction, and customer loyalty) in enhancing BIT alignment, and in turn, financial performance at five and four-star hotels operating in Jordan. In order to achieve the main goal, data of the study were acquired through 400 questionnaires of 37 items delivered to the study respondents. The study hypotheses were tested through Partial Least Squares Structural Equation Modeling (PLS-SEM). The results of the paper showed that that shared domain knowledge, IT success, IT management sophistication, Employee satisfaction, and customer loyalty impact BIT alignment, while organization size did not. Further, the results revealed an impact imparted by BIT alignment on hotel financial performance. The results of this study contribute to the hotel industry by examining antecedent factors that enhance BIT alignment, and in turn, financial performance at five and four-star hotels operating in Jordan. Thus, the current study is a valuable addition to the literature in two ways namely in theory and in practice. Business Information Technology Alignment could also help firms learn more about information technology and increase their usage of it. Furthermore, Business Information Technology Alignment has the potential to attenuate the relationship between IT expenditure and corporate performance. This indicates that organizations can improve their performance just by aligning their business and information technology, without increasing their information technology investment. Also, the usage of IT systems assists the service business by improving service performance and increasing operational efficiency.

Introduction: The informal food sector in Kisumu City, largely run by women informal food vendors, plays a crucial role in the urban food system. However, these female-led businesses faced disproportionate risks stemming from COVID-19-related policies, exacerbating gendered vulnerabilities. This paper explores the gender gaps of post-pandemic recovery strategies and their implications for resilience, recovery, and sustainability of women-led informal food businesses. Methods: This cross-sectional study was guided by the Intersectionality-Based Policy Analysis (IBPA) framework. In collaboration with the Pamoja Community-Based Organization, we employed qualitative methods grounded in community-based participatory approaches. Data were collected through key informant interviews (n = 20), depth interviews (n = 20), focus group discussions (n = 40), and a review of policy documents (n = 2). Data was analyzed guided by the eight principles of the IBPA framework alongside Braun and Clarke’s six-phased thematic analysis approach. Results: Findings indicated that power dynamics in the formulation of post-pandemic policies and top-down implementation approaches excluded women informal food vendors from meaningfully participating in policy processes. For example, female vendors were excluded from the recovery priorities as the strategies adopted had limited to no targeted gender-responsive interventions. As such, women informal food vendors faced several challenges during recovery, including limited government support, barriers to accessing credit facilities, heightened household and unpaid care work, gender-based violence, sexual exploitation, and insecurity. The female vendors employed both individual agency and collective action to facilitate recovery. Discussion: Gender-responsive COVID-19 policies were critical to addressing the disproportionate impact of the pandemic on women-led informal food businesses. Moving forward, a comprehensive understanding of existing sociocultural inequalities is crucial for designing post-pandemic strategies that are gender-inclusive and promote equitable recovery. Such an approach would enhance women informal food vendors’ resilience to emergencies and their contribution to urban household food security and livelihood.

Street vending is one of the oldest forms of informal business and plays a critical role in poverty reduction by creating employment opportunities, particularly for women and youth who are excluded from the formal sector due to limited education, capital constraints, and socio-economic barriers. Despite its importance, the entrepreneurial ecosystem supporting street vendors is often overlooked by governments and formal business actors. This study aims to examine and understand the essential entrepreneurial ecosystem required to support street traders in Tanzania. The study employed a mixed-methods approach, collecting both qualitative and quantitative data from street vendors, municipal officers, and business experts to assess the current entrepreneurial environment and identify areas requiring improvement. The findings indicate that street vendors operating in the informal sector require significantly more support than has been highlighted in previous studies, which largely focus on formal businesses. Key needs identified include designated business locations, access to municipal services, formal recognition, business permits, protection from harassment, access to financial services such as loans, and entrepreneurial training. The study focuses on street vendors in Tanzania, which may limit the generalizability of the findings to other countries with different regulatory and socio-economic contexts. Policymakers and municipal authorities should adopt a more positive and inclusive approach toward street vendors by fostering a supportive entrepreneurial ecosystem. Although developing such an ecosystem may require initial public investment, the long-term benefits—such as enhanced livelihoods and increased contributions to national development—are likely to outweigh the associated costs.

Abstract Data scarcity undermines the precision of empirical and analytical research by limiting sample sizes and reducing statistical power. In domains such as business operations, financial management, and information systems, failure data often arise from rare events, introducing substantial aleatoric and epistemic uncertainty. Existing synthetic data generation methods, including interpolation‐based oversampling and generative models, face persistent challenges. They often fail to capture rare events, preserve temporal dependencies, or model multiple sources of uncertainty, leading to unrealistic samples and degraded performance in downstream tasks. This study introduces the uncertainty generative model with neural attention (UGMNA), a synthetic data generation approach that integrates attentive neural processes, the Heston stochastic volatility model, and stochastic differential equations within a continuous‐time latent framework. UGMNA addresses data scarcity by generating synthetic samples that emulate the distributional characteristics of original datasets while explicitly modeling both aleatoric and epistemic uncertainty. Its design enhances statistical power by augmenting limited datasets and ensures that synthetic data reflect key patterns, temporal dynamics, and complex distributions encountered in real‐world scenarios. Experimental results across multiple case studies demonstrate that UGMNA reduces both types of uncertainty while preserving essential data patterns. Compared with conventional baselines and state‐of‐the‐art generators, UGMNA consistently improves predictive accuracy, ranking performance, and model calibration in data‐scarce, high‐variance environments. These findings establish UGMNA as a robust framework for generating reliable synthetic data, offering practical utility for research and decision‐making in contexts where data scarcity and uncertainty hinder model development.