Hasil untuk "Business"

T. Schwartz, T. Deal, A. Kennedy

Dimitrios Georgakopoulos, M. Hornick, A. Sheth

Nancy M. Carter, W. Gartner, P. Reynolds

Clayton M. Christensen, M. Raynor

J. Cope, G. Watts

Frédéric Delmar, P. Davidsson

M. Belitski, Christina Guenther, A. Kritikos et al.

The existential threat to small businesses, based on their crucial role in the economy, is behind the plethora of scholarly studies in 2020, the first year of the COVID-19 pandemic. Examining the 15 contributions of the special issue on the “Economic effects of the COVID-19 pandemic on entrepreneurship and small businesses,” the paper comprises four parts: a systematic review of the literature on the effect on entrepreneurship and small businesses; a discussion of four literature strands based on this review; an overview of the contributions in this special issue; and some ideas for post-pandemic economic research. Responding to COVID-19 involves not just shielding small business jobs, supporting entrepreneurship, and raising government debt but also creating productive entrepreneurship and resilient location-specific entrepreneurial ecosystems. The COVID-19 pandemic is an unprecedented challenge for small businesses that also brings new market opportunities. The papers in this special issue of Small Business Economics Journal aim to shed light on the economic effects of the COVID-19 pandemic by looking at the macro- and microeconomic effects on entrepreneurship and small businesses as well as the role of financial support policies and well-being in both developed and developing countries. Future research should focus on the role of digitization and financial mechanisms supporting small businesses during crises.

Femi Olan, Emmanuel Ogiemwonyi Arakpogun, Jana Suklan et al.

The evolution of organizational processes and performance over the past decade has been largely enabled by cutting-edge technologies such as data analytics, artificial intelligence (AI), and business intelligence applications. The increasing use of cutting-edge technologies has boosted effectiveness, efficiency and productivity, as existing and new knowledge within an organization continues to improve AI abilities. Consequently, AI can identify redundancies within business processes and offer optimal resource utilization for improved performance. However, the lack of integration of existing and new knowledge makes it problematic to ascertain the required nature of knowledge needed for AI ’ s ability to optimally improve organizational performance. Hence, organizations continue to face reoccurring challenges in their business processes, competition, technological advancement and finding new solutions in a fast-changing society. To address this knowledge gap, this study applies a fuzzy set-theoretic approach underpinned by the conceptualization of AI, knowledge sharing (KS) and organizational performance (OP). Our result suggests that the implementation of AI technologies alone is not sufficient in improving organizational performance. Rather, a complementary system that combines AI and KS provides a more sustainable organizational performance strategy for business operations in a constantly changing digitized society.

L. Moir

There have long been conflicting expectations of the nature of companies’ responsibilities to society. However, for those businesses that do undertake what might be termed “corporate social responsibility”, what is actually socially responsible behaviour as opposed to management of corporate image management or other activity aimed predominantly at business benefits? This article reviews definitions of corporate social responsibility from both practice and the literature and looks at theories to explain why such behaviour takes place. The literature has strong divides between normative or ethical actions and instrumental activities. The article concludes by posing the question of when instrumental activities become business activities rather than largely social responsibility.

Peter van der Zwan, R. Thurik, I. Verheul et al.

The scholarly literature often distinguishes between so-called opportunity and necessity entrepreneurship and between “pull” and “push” motivations. Despite the pervasive use of this terminology, empirical analyses are mostly based on a single country. The present paper contributes by investigating business owner survey data for the United States and 32 countries in Europe and Asia. We analyze the differences between business owners motivated by opportunity and necessity in terms of their (1) socioeconomic characteristics, (2) personality, and (3) perceptions of entrepreneurial support. Descriptive statistics reveal that the two groups of business owners have very different profiles along these three dimensions. Moreover, multinomial logit regressions indicate that the determinants of business ownership (versus paid employment) differ for opportunity and necessity business ownership. A specific result of the present study (covering all 33 countries) is that the probability of being an opportunity versus a necessity business owner is higher for male, younger, wealthier, proactive, and optimistic business owners. Furthermore, those who prefer being a business owner and those who have more favorable perceptions of financial start-up support are more likely to be an opportunity versus a necessity business owner.

J. Haltiwanger, Ron S. Jarmin, Javier Miranda

Suhua Tian, Li Wang, Yonghan Zhao

This study examines the impact of TFP shock, world interest rate shock, domestic deposit rate shock, and fiscal policy shock on China's macroeconomic variables—such as capital credit scale and output growth rate—under the framework of endogenous and exogenous capital controls. Quantitative analysis reveals that an increase in the world interest rates has a negative impact on the domestic credit market, leading to a simultaneous decline in both household savings and capital inflows. Endogenous capital control can mitigate the adverse effect, playing a macroprudential role, whereas exogenous capital controls tend to amplify the negative shock. Expansionary fiscal policy through tax cutting proves effective in stimulating output growth rate. When facing economic downturns, priority should be given to implementing proactive fiscal measures, complemented by appropriate monetary easing with endogenous capital controls, to achieve output growth with less fluctuations.

Aqil al hasoon, Seyed Abbas Hashemi, Narges Hamidian

The financing policies implemented by managers play a pivotal role in risk management and shareholder wealth creation. Consequently, identifying the factors that influence managerial financing decisions is critically important. This study examines the impact of managerial ability on short-term debt usage, incorporating the moderating effects of financial constraints and financial reporting quality. The sample includes 100 firms listed on the Tehran Stock Exchange, selected through systematic elimination for the period 2012–2023. A multivariate regression model based on panel data analysis was employed to test the hypotheses. The findings demonstrate that managerial ability has a positive effect on debt maturity. Additionally, while financial constraints do not significantly moderate this relationship, financial reporting quality strengthens the influence of managerial ability on short-term debt utilization. Specifically, high-ability managers—equipped with superior business acumen and strong incentives to signal their competence—tend to employ greater short-term debt to mitigate information asymmetry and enhance their reputational capital.Keywords: Debt Structure, Managers' Ability, Financial Constraints, Financial Reporting QualityJEL Classification: M40, H63, D04, M41 IntroductionDebt financing is a fundamental component of corporate capital structure, playing a crucial role in firm sustainability and growth. The composition of debt—particularly its maturity structure—serves as a key determinant of financial stability and long-term success. Consequently, decisions regarding debt structure are critical, as misjudgments can expose firms to financial distress or even bankruptcy. Prior research has examined various determinants of debt maturity structure, including macroeconomic and institutional factors such as financial and political environments, legal and tax systems, information asymmetry, and capital provider characteristics. Another stream of literature focuses on firm-specific influences, particularly managerial traits, given their significance in mitigating agency conflicts between shareholders and managers. Among these traits, managerial ability stands out as a pivotal factor shaping debt maturity decisions. Aligned with theoretical foundations, this study proposes the following hypotheses:H₁: Managerial ability positively influences debt maturity.H₂: Financial constraints attenuate the effect of managerial ability on debt maturity.H₃: Financial reporting quality amplifies the impact of managerial ability on debt maturity.Materials & Methods and dataThe study examines firms listed on the Tehran Stock Exchange (TSE) over the period 2012–2023. The sample was selected through systematic elimination to ensure data integrity and representativeness. To test the hypotheses, we employed panel regression analysis using the Generalized Least Squares (GLS) estimator, which accounts for heteroskedasticity and autocorrelation in the data. Managerial ability was operationalized following Demerjian et al. (2012), while financial reporting quality was measured using the Dechow and Dichev (2002) accruals quality model. FindingThe empirical results demonstrate several key insights. As presented in Table 2, managerial ability exhibits a statistically significant positive relationship with firms' utilization of short-term debt. This finding aligns with theoretical expectations, as short-term debt instruments can serve as effective mechanisms to mitigate information asymmetry between managers and investors. Moreover, the preferential use of short-term debt may function as a positive market signal, conveying managers' confidence in the firm's near-term financial prospects. Table 3 reveals that financial constraints do not significantly moderate the relationship between managerial ability and debt maturity structure. This suggests that capable managers maintain their influence over financing decisions regardless of external financial limitations. Finally, Table 4 presents evidence that financial reporting quality strengthens the positive association between managerial ability and short-term debt usage. This amplification effect likely occurs because high-quality financial reporting enhances transparency, thereby increasing the credibility of managers' financing decisions. Discussion and ConclusionCorporate financing decisions are predominantly shaped by managerial discretion, with short-term debt instruments gaining increasing prominence over the past three decades. Our findings align with signaling theory, which posits that short-term debt issuance serves dual purposes: it reduces information asymmetry while simultaneously functioning as a positive market signal of managerial competence. Conversely, agency theory would predict an inverse relationship, suggesting that higher managerial ability might correlate with reduced short-term debt due to inherent agency conflicts in firms where managerial capabilities are less observable. The empirical evidence supports the signaling perspective, demonstrating that high-ability managers strategically utilize short-term debt to distinguish themselves from their less competent counterparts. This behavior stems from their superior capacity to assess market conditions and capitalize on favorable financing opportunities. Furthermore, our analysis reveals that managerial ability plays a particularly significant role in firms with higher reporting quality. In such organizations, which typically possess more robust project portfolios, short-term debt issuance serves as an additional quality indicator. High-ability managers in these firms are more inclined to employ short-term debt instruments, thereby reinforcing their reputation for financial acumen and strengthening market confidence. These findings contribute to the ongoing theoretical discourse by reconciling competing perspectives from signaling and agency theories. They also offer practical implications for corporate governance, suggesting that boards should consider managerial ability as a key factor in financing policy decisions, particularly in firms with transparent financial reporting environments.

Abdillah Ahsan, Maulida Gadis Utami, Yuyu Buono Ayuning Pertiwi et al.

Objectives This study investigated the correlation between the type of health insurance membership as a proxy for the economic status of patients and the severity of their type two diabetes mellitus (T2DM) in Indonesia.Design The study conducted a secondary analysis of National Health Insurance (Jaminan Kesehatan Nasional) claim data provided by the Indonesian Social Security Agency, Badan Penyelenggara Jaminan Sosial (BPJS). We used ordered logistic regression with four severity levels for T2DM (0=outpatient, I=mild, II=moderate, III=severe) as dependent variables. The main independent variables (insurance membership categories) included subsidised insurance members (PBI), a combination of formally employed and nonsalaried informal workers (PBPU & PPU) and nonworkers (BP).Setting Secondary healthcare facilities in Indonesia.Participants The dataset included 2 989 618 claims for hospital visits of people with T2DM from 2018 to 2022.Primary outcome measures Severity level of T2DM patients.Result A higher percentage of T2DM patients who visited healthcare facilities with subsidised insurance (PBI), which represents a low-income group, have severe disease (6.9%) than patients in the PBPU & PPU (4.9%) and BP categories (5.5%). Moreover, regression analysis revealed that having PBI membership status was associated with a greater OR of having severe T2DM than nonsubsidised members. Among T2DM patients in the nonsubsidised insurance category, workers (PBPU & PPU) had an OR of 0.74 (95% CI: 0.735 to 0.745; p<0.0001) for having severe disease during hospital visits. Moreover, non-workers (BP) had a lower OR of 0.718 (95% CI: 0.711 to 0.725; p<0.0001) for severe disease than the PBI category.Conclusion These findings illustrate the lack of optimal access to health services for diabetes patients in low-income insurance membership categories and the challenges of better treatment in health facilities for low-income patients.

Tianbao Song, Zongyi Huang, Xin Liu et al.

This paper introduces a novel variational autoencoder model termed DVAE to prevent posterior collapse in text modeling. DVAE employs a dual-path architecture within its decoder: path A and path B. Path A makes the direct input of text instances into the decoder, whereas path B replaces a subset of word tokens in the text instances with a generic unknown token before their input into the decoder. A stopping strategy is implemented, wherein both paths are concurrently active during the early phases of training. As the model progresses towards convergence, path B is removed. To further refine the performance, a KL weight dropout method is employed, which randomly sets certain dimensions of the KL weight to zero during the annealing process. DVAE compels the latent variables to encode more information about the input texts through path B and fully utilize the expressiveness of the decoder, as well as avoiding the local optimum when path B is active through path A and the stopping strategy. Furthermore, the KL weight dropout method augments the number of active units within the latent variables. Experimental results show the excellent performance of DVAE in density estimation, representation learning, and text generation.

Wenda Zhang

The advancements of Large language models (LLMs) have provided great opportunities to text-to-SQL tasks to overcome the main challenges to understand complex domain information and complex database structures in business applications. In this paper, we propose a meta-aware learning framework to integrate domain knowledge, database schema, chain-of-thought reasoning processes, and metadata relationships to improve the SQL generation quality. The proposed framework includes four learning strategies: schema-based learning, Chain-of-Thought (CoT) learning, knowledge-enhanced learning, and key information tokenization. This approach provides a comprehensive understanding of database structure and metadata information towards LLM through fine-tuning to improve its performance on SQL generation within business domains. Through two experimental studies, we have demonstrated the superiority of the proposed methods in execution accuracy, multi-task SQL generation capability, and reduction of catastrophic forgetting.

M. Mavin De Silva, Callie Clark, Tadachika Nakayama et al.

Understanding the economic impacts of the placement of electric vehicle charging stations (EVCSs) is crucial for planning infrastructure systems that benefit the broader community. Theoretical models have been used to predict human behavior during charging events, however, these models have often neglected the complexity of trip patterns, and have underestimated the real-world impacts of such infrastructure on the local economy. In this paper, we design a quasi-experiment using mobile phone GPS location and EVCS deployment history data to analyze the causal impact of EVCS placement on visitation patterns to businesses. More specifically, we leverage the staggered placement of EVCSs in New York City and California Bay Area to match treated and control businesses that share similar characteristics including the business sector, location, and pre-treatment visitation count. By comparing three alternative matching strategies, we show that staggered adoption avoids selecting controls from non-treated clusters, and yields greater spatial overlap in dense urban areas. We find that EVCS installations significantly increase customer traffic, with effects concentrated in recreational venues in New York City and routine destinations such as groceries, pharmacies, and cafes in California Bay Area. Our results suggest that the economic spillovers of EVCSs vary across urban contexts and highlight the effectiveness of leveraging the staggered nature of adoption timings for evaluating infrastructure impacts in heterogeneous urban environments.

Lukas Kirchdorfer, Konrad Özdemir, Stjepan Kusenic et al.

Business Process Simulation (BPS) is a critical tool for analyzing and improving organizational processes by estimating the impact of process changes. A key component of BPS is the case-arrival model, which determines the pattern of new case entries into a process. Although accurate case-arrival modeling is essential for reliable simulations, as it influences waiting and overall cycle times, existing approaches often rely on oversimplified static distributions of inter-arrival times. These approaches fail to capture the dynamic and temporal complexities inherent in organizational environments, leading to less accurate and reliable outcomes. To address this limitation, we propose Auto Time Kernel Density Estimation (AT-KDE), a divide-and-conquer approach that models arrival times of processes by incorporating global dynamics, day-of-week variations, and intraday distributional changes, ensuring both precision and scalability. Experiments conducted across 20 diverse processes demonstrate that AT-KDE is far more accurate and robust than existing approaches while maintaining sensible execution time efficiency.

Geòrgia Escaramís, Anna Arbussà

Most empirical works that study the financing decisions of family businesses use financial ratios. These data present asymmetry, non-normality, non-linearity and even dependence on the results of the choice of which accounting figure goes to the numerator and denominator of the ratio. This article uses compositional data analysis (CoDa) as well as classical analysis strategies to compare the structure of balance sheet liabilities between family and non-family businesses, showing the sensitivity of the results to the methodology used. The results prove the need to use appropriate methodologies to advance the academic discipline.

Noah Broestl, Benjamin Lange, Cristina Voinea et al.

Instruction-tuned Large Language Models (LLMs) are increasingly deployed as AI Assistants in firms for support in cognitive tasks. These AI assistants carry embedded perspectives which influence factors across the firm including decision-making, collaboration, and organizational culture. This paper argues that firms must align the perspectives of these AI Assistants intentionally with their objectives and values, framing alignment as a strategic and ethical imperative crucial for maintaining control over firm culture and intra-firm moral norms. The paper highlights how AI perspectives arise from biases in training data and the fine-tuning objectives of developers, and discusses their impact and ethical significance, foregrounding ethical concerns like automation bias and reduced critical thinking. Drawing on normative business ethics, particularly non-reductionist views of professional relationships, three distinct alignment strategies are proposed: supportive (reinforcing the firm's mission), adversarial (stress-testing ideas), and diverse (broadening moral horizons by incorporating multiple stakeholder views). The ethical trade-offs of each strategy and their implications for manager-employee and employee-employee relationships are analyzed, alongside the potential to shape the culture and moral fabric of the firm.