Hasil untuk "Business"

D. Ulrich

A. Carroll

Les Johnson

L. Renzulli, Howard E. Aldrich, J. Moody

J. Thong, C. Yap, K. Raman

Venkatesh Shankar, G. Urban, F. Sultan

Ken Matsuno, J. Mentzer

J. Curran, R. Blackburn

S. Coleman

T. Manolova, C. Brush, L. Edelman et al.

COVID-19 is unique in the severity of its impact as it is a humanitarian disaster that has caused both a supply and a demand shock to the global economic system. It has disproportionately affected women entrepreneurs as their firms are younger and smaller. In this commentary, we contend that while all businesses must pivot their business models in times of tumultuous change, simultaneously reducing risk and seizing new opportunities, this is particularly difficult for women entrepreneurs, whose businesses are concentrated in the industry sectors most severely affected by the economic shutdown. We draw on recent survey data from the Diana International Research Institute (DIRI) to identify business model pivots in women-owned businesses, and conclude by offering a set of gendered future research questions.

Tim J. Boonen, Engel John C. Dela Vega, Len Patrick Dominic M. Garces

This paper considers an insurer with two collaborating business lines that faces three critical decisions: (1) dividend payout, (2) reinsurance coverage, and (3) capital injection between the lines, in the presence of model uncertainty. The insurer considers the reference model to be an approximation of the true model, and each line has its own robustness preference. The reserve level of each line is modeled using a diffusion process. The objective is to obtain a robust strategy that maximizes the expected weighted sum of discounted dividends until the first ruin time, while incorporating a penalty term for the distortion between the reference and alternative models in the worst-case scenario. We completely solve this problem and obtain the value function and optimal (equilibrium) strategies in closed form. We show that the optimal dividend-capital injection strategy is a barrier strategy. The optimal proportion of risk ceded to the reinsurer and the deviation of the worst-case model from the reference model are decreasing with respect to the aggregate reserve level. Finally, numerical examples are presented to show the impact of the model parameters and ambiguity aversion on the optimal strategies.

Stephan Ludwig, Peter J. Danaher, Xiaohao Yang

The growing use of unstructured text in business research makes topic modeling a central tool for constructing explanatory variables from reviews, social media, and open-ended survey responses, yet existing approaches function poorly as measurement instruments. Prior work shows that textual content predicts outcomes such as sales, satisfaction, and firm performance, but probabilistic models often generate conceptually diffuse topics, neural topic models are difficult to interpret in theory-driven settings, and large language model approaches lack standardization, stability, and alignment with document-level representations. We introduce LX Topic, a neural topic method that conceptualizes topics as latent linguistic constructs and produces calibrated document-level topic proportions for empirical analysis. LX Topic builds on FASTopic to ensure strong document representativeness and integrates large language model refinement at the topic-word level using alignment and confidence-weighting mechanisms that enhance semantic coherence without distorting document-topic distributions. Evaluations on large-scale Amazon and Yelp review datasets demonstrate that LX Topic achieves the highest overall topic quality relative to leading models while preserving clustering and classification performance. By unifying topic discovery, refinement, and standardized output in a web-based system, LX Topic establishes topic modeling as a reproducible, interpretable, and measurement-oriented instrument for marketing research and practice.

Sidney Shapiro, Burhanuddin Panvelwala

Reproducibility remains a persistent challenge in forecasting research and practice, particularly in business and financial analytics, where forecasts inform high-stakes decisions. Traditional forecasting methods, while theoretically interpretable, often require extensive manual tuning and are difficult to replicate in proprietary environments. Machine learning approaches offer predictive flexibility but introduce challenges related to interpretability, stochastic training procedures, and cross-environment reproducibility. This paper examines Prophet, an open-source forecasting framework developed by Meta, as a reproducibility-enabling solution that balances interpretability, standardized workflows, and accessibility. Rather than proposing a new algorithm, this study evaluates how Prophet's additive structure, open-source implementation, and standardized workflow contribute to transparent and replicable forecasting practice. Using publicly available financial and retail datasets, we compare the performance and interpretability of Prophet with multiple ARIMA specifications (auto-selected, manually specified, and seasonal variants) and Random Forest, under a controlled and fully documented experimental design. This multi-model comparison provides a robust assessment of Prophet's relative performance and reproducibility advantages. Through concrete Python examples, we demonstrate how Prophet facilitates efficient forecasting workflows and integration with analytical pipelines. The study positions Prophet within the broader context of reproducible research. It highlights Prophet's role as a methodological building block that supports verification, auditability, and methodological rigor. This work provides researchers and practitioners with a practical reference framework for reproducible forecasting in Python-based research workflows.

Silvia Crescioli, Hélène Kaplon, Alicia Chenoweth et al.

The Antibodies to Watch article series provides annual updates on commercial late-stage clinical development, regulatory review, and marketing approvals of antibody therapeutics. Since the first article was published in 2010, the late-stage pipeline has grown from 26 antibody therapeutics to over 200, while during the same time numerous molecules in late-stage studies either transitioned to regulatory review and were approved or were terminated. In this installment of the series, we recap first marketing approvals granted to 19 antibody therapeutics in 2025, discuss 26 molecules currently in regulatory review, including the bispecific antibody-drug conjugate izalontamab brengitecan, and predict which molecules of the 209 currently in the commercial late-stage pipeline might transition to regulatory review by the end of 2026. Most antibody therapeutics in the latter category are for non-cancer indications (16/21, 76%) and have a conventional format (13/21, 62%), but the category also includes numerous antibody-oligo or -drug conjugates, such as delpacibart etedesiran, delpacibart zotadirsen, zeleciment rostudirsen, sonesitatug vedotin, trastuzumab pamirtecan, and ifinatamab deruxtecan, as well as the bispecific petosemtamab. As antibody therapeutics development is a global enterprise, we also discuss trends in annual first approvals granted to antibody therapeutics in any country since 2010, stratified by the antibody’s country of origin, documenting the notable increases in the total number of first approvals and those approved first in China. Finally, to benchmark the time typically required for clinical development and regulatory review, we calculated this period for recently approved antibody therapeutic products stratified by their therapeutic area, mechanism of action, format, and country of origin. Our data show that the development and approval period were typically ~6 years, but on average this period was shorter for China-originated products.

B. Hoogendoorn, P. Zwan, R. Thurik

Entrepreneurs who start a business to serve both self-interests and collective interests by addressing unmet social and environmental needs are usually referred to as sustainable entrepreneurs. Compared with regular entrepreneurs, we argue that sustainable entrepreneurs face specific challenges when establishing their businesses owing to the discrepancy between the creation and appropriation of private value and social value. We hypothesize that when starting a business, sustainable entrepreneurs (1) feel more hampered by perceived barriers, such as the institutional environment and (2) have a different risk attitude and perception than regular entrepreneurs. We use two waves of the Flash Eurobarometer survey on entrepreneurship (2009 and 2012), which contains information on start-up motivations, start-up barriers, and risk perceptions of approximately 3000 (prospective) business owners across 33 countries. We find that sustainable entrepreneurs indeed perceive more institutional barriers in terms of a lack of financial, administrative, and informational support at business start-up than regular entrepreneurs. Further, no significant differences between sustainable and regular entrepreneurs are found in terms of their risk attitudes or perceived financial risks. However, sustainable entrepreneurs are more likely to fear personal failure than regular entrepreneurs, which is explained by their varied and complex stakeholder relations. These insights may serve as an important signal for both governments and private capital providers in enhancing the institutional climate.

P. Westhead, M. Wright

J. Arrégle, Francesco Chirico, Liena Kano et al.

Martin Bichler, Julius Durmann, Matthias Oberlechner

The rise of algorithmic pricing in online retail platforms has attracted significant interest in how autonomous software agents interact under competition. This article explores the potential emergence of algorithmic collusion - supra-competitive pricing outcomes that arise without explicit agreements - as a consequence of repeated interactions between learning agents. Most of the literature focuses on oligopoly pricing environments modeled as repeated Bertrand competitions, where firms use online learning algorithms to adapt prices over time. While experimental research has demonstrated that specific reinforcement learning algorithms can learn to maintain prices above competitive equilibrium levels in simulated environments, theoretical understanding of when and why such outcomes occur remains limited. This work highlights the interdisciplinary nature of this challenge, which connects computer science concepts of online learning with game-theoretical literature on equilibrium learning. We examine implications for the Business & Information Systems Engineering (BISE) community and identify specific research opportunities to address challenges of algorithmic competition in digital marketplaces.

Sarah Xue Dong, Agung Satyadini, Mathias Sinning

Tax compliance among small businesses remains low in developing countries, yet little is known about how regional context shapes the effectiveness of enforcement strategies. Both theory and evidence suggest an ambiguous relationship between compliance and geographic proximity to tax offices. We study this issue using a large-scale natural field experiment with Indonesia's tax authority involving 12,000 micro, small, and medium enterprises (MSMEs). Businesses were randomly assigned to receive deterrence, information, or public goods letters, or no message. All letters improved compliance, with deterrence messages producing the largest gains - substantially increasing filing rates and raising monthly tax payments. Each dollar spent on deterrence letters generated about US$30 in additional revenue over the course of a year. We observe high compliance among non-treated MSMEs near metropolitan tax offices and find that enforcement messages successfully raise compliance in non-metropolitan regions to comparable levels. However, targeting already compliant MSMEs near metropolitan tax offices backfires, underscoring the need for geographically tailored tax administration strategies. These results provide novel experimental evidence on the relation between geographic proximity and the effectiveness of tax enforcement, helping to reconcile mixed findings in the tax compliance literature.

Manijeh Haghighi Nasab, Hamid Reza Yazdani, Fatemeh Goli

ObjectiveThe lack of perceived fairness in pricing can lead to various negative consequences, including diminished trust in the seller and the prices, reduced demand, lower customer satisfaction, negative word-of-mouth advertising, loss of customer loyalty to the company or brand, increased complaints, and a reluctance to make future purchases. Considering all the mentioned destructive consequences, it is necessary to design a perceived fairness model for dynamic pricing. Accordingly, this study to present the perceived fairness model of dynamic pricing using a meta-synthesis approach. MethodologyThis research is descriptive in terms of its practical purpose, data collection, and qualitative approach. The method used in this study is qualitative. Meta-synthesis involves several steps: searching, evaluating, synthesizing, expressing, and partially interpreting both quantitative and qualitative research. Transcombination can be performed using various methods; in this study, the 7-step model of Sandelowski and Barso was employed. The data collection tool consisted of past documents, including 32 articles. Content analysis was used as the method for data analysis. FindingsThe findings indicated the drivers affecting the perceived fairness of dynamic pricing in three categories: customer-related factors (demographic characteristics, price knowledge, price expectation, consumption and behavioral experience, familiarity with dynamic pricing), company-related factors (pricing transparency, communication with customers, trust), and market-related factors (price position, price dispersion). These drivers are the set of factors that affect the perceived fairness of dynamic pricing. The drivers include various factors. Perceived fairness of dynamic pricing includes two dimensions: emotional fairness and cognitive fairness. Customers who expect low prices will be suspicious and unfair about price increases. The actions taken by companies to raise customer awareness about the reasons for price increases, the timing of price changes, and similar actions in past periods significantly influence customers' accurate understanding of the price changes, preventing them from perceiving them as unfair. Perceived fairness consequences of dynamic pricing are categorized into two categories: positive consequences (customer satisfaction, customer loyalty, repurchase intention) and negative consequences (negative feelings, negative behaviors, and negative advertising by customers). ConclusionAny price difference should be based on a logical reason and serve as a tool for segmentation. While pricing policies inevitably influence consumers' purchase intentions, they should not undermine the perceived fairness from the customer's perspective. The identified drivers of perceived fairness in dynamic pricing, when applied to pricing the same goods under different conditions, provide a foundation for pricing decision-makers within companies.