This study examines the psychological and behavioral mechanisms driving the success of AI-powered virtual influencers. Drawing on the Stimulus-Organism-Response framework, the study investigates how specific digital stimuli, including posting frequency, credibility, visual attractiveness, and expertise, trigger internal cognitive (opinion leadership) and emotional (parasocial interaction) evaluations, which subsequently drive the consumer’s intention to follow the influencer’s advice. Utilizing a near-census dataset of 116 virtual influencers analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings reveal that expertise, credibility, and visual attractiveness are robust predictors of both perceived authority and parasocial closeness. Notably, and contrary to established models of human influencer marketing, frequent posting does not significantly impact these internal psychological states for virtual personas. The findings further suggest that perceptions associated with authority and parasocial closeness may emerge even without human lived experience. Overall, the effectiveness of virtual influencers depends more on perceived expertise and a consistent, believable persona than on simply posting frequently or engagement metrics alone.
Retail site selection profoundly impacts business success by influencing customer attraction. To address the limitations of traditional linear modelling in this strategic area, an innovative, integrated methodology that combines Geographic Information Systems (GIS) and fuzzy-set Qualitative Comparative Analysis (fsQCA) is developed as a configurational profiling tool to characterise the complex configurations of spatial variables consistent with store presence. By identifying recurring spatial patterns rather than testing causal sufficiency or necessity, the proposed GIS-fsQCA methodology enables the development of a spatial model that captures patterns in existing store locations and identifies potential areas for future expansion. Its applicability is demonstrated through a case study focused on a recent major entrant into the competitive Portuguese grocery retail market. The analysis reveals that variables well aligned with classical location theories are still highly relevant for explaining the retail chain’s location patterns. Proximity to competitors and distance to the city centre are identified as consistently present spatial features across established locations. Furthermore, the study identifies multiple distinct configurational patterns characterizing the retailer’s locational choices, empirically validating the principle of equifinality in a spatial context. By also applying the developed methodology to competitor locations and conducting a cross-coverage analysis, this study provides unique insights into the competitive dynamics shaping the market. Relying on empirical data and spatial analysis, the developed framework offers a novel, asymmetric modelling approach for both researchers and practitioners seeking to optimize retail site selection strategies.
This study explores how supervisors in the hospitality industry can enhance employees’ contextual performance through servant leadership. Although prior research has shown a direct and positive effect of servant leadership on contextual performance, little is known about the underlying mechanisms. Drawing on reasoned action, social learning, and social exchange theories, we propose that servant leadership may foster employees’ contextual performance by sequentially strengthening their perspective taking and servant attitude. To examine this model, data were collected from 403 employees across 59 hospitality firms in the Canary and Balearic Islands (Spain). Using partial least squares structural equation modeling (PLS-SEM) analysis, the findings suggest that perspective taking and servant attitude mediate the relationship between servant leadership and employees’ contextual performance. Furthermore, a serial mediation effect was observed, consistent with the notion that perspective taking is associated with servant attitude, which, in turn, relates to higher contextual performance. These findings suggest that servant leadership may play a meaningful role in the hospitality industry, where employee behaviors that extend beyond formal job duties are essential for delivering high-quality service and ensuring guest satisfaction. Overall, this study provides insights into the psychological processes through which servant leadership is associated with contextual performance in service-intensive organizational settings, thus contributing to both servant leadership and hospitality research.
We investigate how servant leadership promotes team contextual ambidexterity via team behavioral integration from a conservation of resources theory perspective, and how such an indirect effect is conditional on two boundary conditions: the “climate strength” of servant leadership and of team behavioral integration. By climate strength we refer to the degree of agreement or consensus among team members regarding those two constructs. Findings from a field study and an experiment indicate that (a) servant leaders foster team contextual ambidexterity by cultivating a supportive context of team behavioral integration, and (b) this indirect effect is amplified when within-team agreement (i.e., the climate strength of servant leadership and team behavioral integration) is higher. We contribute to identifying the micro-foundations of organizational ambidexterity at the team level and exploring why, how and when servant leadership influences such team outcomes. Furthermore, drawing on both conceptual work and empirical evidence, we emphasize the underexplored role of “climate strength” in shaping team contextual ambidexterity through its underlying mechanisms.
Recent advances in artificial intelligence (AI) have accelerated organizational experimentation with new forms of value creation across cultural and creative industries. While prior research has examined AI adoption, creative augmentation, and ethical concerns, limited attention has been paid to how AI contributes to regenerative forms of value creation. Addressing this gap, this study investigates how regenerative practices emerge from different configurations of AI-enabled organizational capabilities in cultural and creative industries. Drawing on survey data from 110 European creative organizations and applying fuzzy-set qualitative comparative analysis (fsQCA), the study identifies one dominant pathway to regenerative practices, centered on the joint presence of adaptive learning and collaborative engagement, alongside supplementary configurations with limited empirical coverage. The findings reveal that regenerative practices do not arise from AI adoption or human-centric design alone, but from specific combinations of resource efficiency, environmental impact reduction, adaptive learning, and collaborative engagement. The results further outline causal asymmetry, indicating that the absence of regeneration cannot be explained as the simple inverse of its presence. This study contributes to research on AI, regenerative business models in cultural and creative industries, offering theoretical insights into AI-enabled regeneration and practical guidance for managing human–AI collaboration toward regenerative outcomes.
This study examines the impact of executive compensation restrictions on the quality of corporate accounting information and the underlying mechanisms driving this relationship. Leveraging China’s 2015 compensation regulation as a quasi-natural experiment, we employ a Difference-in-Differences design using firm-level panel data from 2011 to 2023. We find that compensation restrictions significantly impair accounting information quality. Mechanism analyses indicate that this deterioration operates through three channels: exacerbated managerial myopia, weakened executive team stability, and heightened rent-seeking behavior. Cross-sectional analyses further reveal that these adverse effects are more pronounced in firms with weaker internal governance—specifically those with low ownership concentration, limited institutional investment, as well as those subject to limited external oversight, such as low media attention. These findings underscore the unintended consequences of rigid compensation controls and offer actionable insights for designing more balanced executive compensation frameworks in emerging markets.
This study examines how the digital expertise of the board of directors (BoD) is related to the successful implementation of digital transformation for better financial performance. BoDs, who are crucial in guiding strategic changes such as the digital transformation, need relevant expertise. Nevertheless, whether BoDs possess digital expertise and how it influences firm performance is unknown. Drawing on Resource Dependence Theory (RDT) and the Resource-Based View (RBV), this study conceptualizes digital board expertise and explores its (contingent) effects on the financial performance of German Prime Standard firms. We find that directors with digital expertise take an important resource-providing role and are valuable in (1) accessing information from the external environment and (2) implementing the crucial knowledge in internal capabilities for superior financial performance. In relation to these two mechanisms, we argue that the external and internal context matter for the relationship between digital board expertise and financial performance. We find support for our arguments.
This study explores how small and medium-sized enterprises (SMEs) in the European Earth Observation (EO) sector leverage Artificial Intelligence (AI) to develop innovative business models. Amid the rapid diffusion of AI technologies and increasing availability of open satellite data, SMEs are emerging as key players in the evolving EO landscape. Drawing on a multiple case study of ten AI-enabled SMEs across the EO value chain, the research identifies two distinct business model archetypes: Specialized AI Services for Space Missions and Data Knowledge as a Service. Each archetype is characterized by unique configurations of AI capabilities across four domains: data capability, technology/algorithm development capability, foundation capability, and AI democratization capability. The findings reveal how upstream firms prioritize onboard automation, embedded AI systems, and institutional collaboration, while downstream firms emphasize scalable platforms, predictive analytics, and accessible, user-centric services. This study contributes to the literature on AI-driven business model innovation by extending capability frameworks to the space sector, challenging assumptions about SME limitations, and offering practical insights for founders, policymakers, and ecosystem stakeholders.
Digitalization, e-commerce, and sustainability pressures have accelerated the transformation of the logistics sector, turning it into an industry that is rapidly evolving. However, this progress faces challenges, including poor infrastructure, slow technological adoption, and intense competition. Companies are therefore pushed to seek innovative ways to improve efficiency while meeting sustainability goals. One promising approach is horizontal collaboration. By pooling resources and capabilities, companies in similar positions can create synergies and overcome their own operational limitations. This study analyzes the literature on horizontal collaboration in logistics, focusing on research trends, methodologies, and thematic content. An extensive literature review was conducted using the Web of Science Core Collections database, and the resulting set of 133 articles is examined in detail. The review highlights emerging topics such as common infrastructure, fair distribution, digital technologies, cold chain logistics, and sustainability within collaboration frameworks. It also identifies research gaps that can inform practice and scholarship, offering insights for managers, policymakers, and future academic work. The findings emphasize the growing importance of horizontal collaboration in logistics and its expansion into diverse applications. Moreover, themes identified in the literature matter at strategic, tactical, and operational levels. Finally, the review underscores the role of collaboration in advancing economic, environmental, and social sustainability.
This study investigates how small and medium-sized enterprises (SMEs) develop and leverage collaborative learning capabilities to mitigate effects of institutional voids. Drawing on data from 44 entrepreneurs in a developing economy, we identify four phases in developing the collaborative learning capabilities. Phase 1 emphasizes recognizing the nature and scope of institutional constraints and resource deficits. These motivate the entrepreneurial firms to focus on elevating venture performance, exemplified by consistently adhering to high standards (Phase 2). Phase 3 involves cultivating diverse ties to navigate and overcome institutional voids. Phase 4 is characterized by network learning as a mechanism for capability development and for accessing heterogeneous resources and competencies. The broader theoretical and practical implications of these findings are examined.
The rapid advancement of IoT technologies has enabled new forms of data flow among devices, people, and organizations. This has brought about the emergence of innovative business models with increasingly sophisticated value propositions. This study develops a typology and progression framework for IoT business models, highlighting how value creation and capture evolve as firms integrate IoT technologies. Based on a systematic literature review and multiple case studies, the present research identifies three distinct business model archetypes—namely, connectivity, servitization, and data ecosystems. Each archetype is characterized by specific mechanisms of value creation and capture. The resulting framework provides insights into support firms seeking to align IoT investments with their capabilities and market positioning. By offering a structured view of business model sophistication, this study contributes to the theoretical understanding of IoT business models, business model sophistication, and data ecosystems, while also informing managerial practice.
Embodied Artificial Intelligence (AI) refers to AI systems in which intelligence is embedded in physical systems and emerges through interaction with its environment. Embodied AI (partly referred to as embedded AI also) acts in the real world through continuous cycles of sensing, decision-making, actuation, and learning. Embodied AI participates in operations and moves beyond supporting decision-making-support to a constitutive element of value creation. Firms must redesign what activities are performed, how they are linked, and who controls them. Embodied AI implies a double loop: a closed learning loop inside the adopting firm, where embodied AI transforms situated use into operational feedback and workflow changes, and an external learning loop across the ecosystem of technology providers, component suppliers, software firms, platform orchestrators, and users. Data generated through physical use travels beyond the adopting firm. Our conceptual study provides pioneering theoretical accounts of embodied AI in management research and focuses on its implications for business models. We develop two complementary models. First, a transition model shows how business models shift from asset-based and episodic logics toward adaptive, data-driven systems. Second, an integrative embodied AI business model grid explains how changes are generated through reconfiguration of value activities, interdependencies, and governance across actors and technologies. We derive nine propositions specifying how embodied AI transforms business models. We further show four systemic tensions: Openness versus control, scaling versus local fit, automation ambition versus reliability constraints, and monetization versus trust. Using agriculture as a revealing template, carves out how embodied AI reshapes business models in traditional industries moving from product performance toward continuous workflow optimization, lifecycle-based orchestration, and recurring, trust-based monetization.
The new space economy (NSE) provides a rich context in which managers from both established firms and startups exploit entrepreneurial opportunities across industry boundaries. Anchored in a microfoundational perspective on entrepreneurial action, we conceptualize identity, opportunity beliefs, and individual-level capabilities as interdependent microfoundational conditions whose alignment within distinct configurational pathways jointly shapes cross-industry opportunity exploitation rather than any single factor in isolation. Although prior research acknowledges the interrelation of these factors, it often overlooks how they combine and the tensions individuals face when aligning them. Addressing this gap, we examine how distinct configurations of identity, opportunity beliefs, and capabilities shape opportunity exploitation. We apply fuzzy-set qualitative comparative analysis (fsQCA) based on data from 60 managers in the NSE, a cross-industry context that reflects increasing interconnectedness across industries. Our study identifies two configurations associated with high opportunity exploitation: Cross-Industry Orchestrators are not reliant on a strong identification with their origin industry; rather, they combine distinct capabilities to apply a wide range of knowledge to industry-specific problems. Industry-Rooted Problem Solvers maintain a strong sense of belonging to their origin industry while effectively absorbing problem-related knowledge from other industries. Additionally, we identify two configurations with lower opportunity exploitation: The Motive-Lacking Isolationist and the Non-Absorbing Networker.
The disclosure of soft, forward-looking information plays a crucial role in shaping investor decisions and firm price. Such information often relies on managerial expectations and private knowledge, giving managers incentives to present it strategically—most notably by biasing the tone of their communication. Investors, however, may learn about this bias over time and adjust their valuation of future managerial reports accordingly. We analyze the strategic use of tone in mandatory disclosures within a multi-period setting where investors adaptively update their beliefs. Our findings show that a manager’s optimal tone choice is shaped by the underlying soft information, the market’s response to past tone choices, managerial compensation, and the strength of internal controls. When incentives are strong and internal controls are weak, managers may choose an excessively biased tone in the disclosure of soft information.
The use of Artificial Intelligence (AI), and more recently the rise of Generative AI (GenAI), is fundamentally transforming businesses. This study explores the emerging role of GenAI in business, identifying and analyzing concrete use cases from business practice. Despite recent growth, research on GenAI in business is still in its infancy and lacks comprehensive foundational studies for understanding the phenomenon. To address this gap, the study’s objective is to organize the landscape of GenAI use cases in business, enabling theory building, while also offering practical insights for managers. To achieve this objective, a scoping review was conducted, collecting 680 real-world use cases of GenAI in business from both academic and non-academic sources. This sample was analyzed using an inductive concept development approach. First, a typology was developed, providing an understanding of six main types and 21 subtypes organized along two dimensions: existing business enhancement and new business enablement. Second, a framework was derived, extending the typology by mapping the configuration of human-AI collaboration in each subtype, showing how GenAI is assisting, augmenting, or automating business activities. Overall, this study provides a comprehensive typology and application framework for the growing academic discussion and equips practitioners with guidance on navigating and deploying GenAI within organizations.
New venture teams often develop shared leadership competencies and practices as an inherent capability during venture creation. Yet, entrepreneurial leadership conceptualizations tend to focus on single leader-follower perspectives, while shared leadership traditionally emphasizes intra-firm dynamics when vertical leadership structures are in place. Based on an analysis of 184 empirical articles, this paper provides a comprehensive, systematic literature review at the intersection of shared entrepreneurial leadership (SEL). We categorize existing research concerning the process of sharing leadership and concentrate to advance on what is the distinct conceptualization of SEL associated with the new venture context (i.e., high uncertainty, information asymmetries, limited knowledge, scarce resources, weak institutions, and legitimacy issues). Reviewing the underlying mechanisms of leadership in entrepreneurial contexts, we highlight items studied as antecedents to and outcomes of shared leadership associated with innovation and new venture performance outcomes. We close with future research directions highlighting to study the lifecycle process perspective, the intersections of SEL and entrepreneurial stakeholders, the role of SEL when facing ethical challenges, and the dynamics of vertical and horizontal leadership when adding more startup employees.
Entrepreneurial ecosystem research has expanded significantly in recent years; nonetheless, we argue that the role of the entrepreneurs in relation to those systems remains inadequately theorized. Drawing on a discourse analysis of 147 publications, we identify four dominant discourses that position entrepreneurs along the axes of agency and embeddedness. Those discourses frame entrepreneurs as (1) shaped by ecosystem externalities, (2) interactive components within relational systems, (3) active agents strategically reshaping their environments, or (4) actors who both influence and are influenced by ecosystem structures. While each of those perspectives offers valuable insights, they collectively conceptualize entrepreneurs as occupants of structural positions, rather than people having experiences. Consequently, there is limited understanding of how entrepreneurs interpret, navigate, and emotionally engage with ecosystem conditions. To address that gap, we introduce a novel perspective for studying entrepreneurial ecosystems. We label it the entrepreneur‑focused entrepreneurial ecosystems (EFEE) perspective, and it focuses on experience as a core explanatory mechanism. The EFEE perspective integrates micro-foundational, practice-based, and interpretive approaches to demonstrate how entrepreneurs’ sensemaking, learning, and capability development shape the routines and configurations upon which ecosystems are built. We also conceptualize ecosystems as service-providing systems, meaning that research could use service-design methods to capture entrepreneurial journeys, touchpoints, and value co‑creation. That would yield experiential evidence linking micro-practices to ecosystem transformation. Overall, this perspective extends entrepreneurial ecosystem theory by reinstating the entrepreneur at the centre and providing a foundation for more inclusive, entrepreneur‑centred policy and ecosystem design.
This paper tests a potential new determinant of discounts commonly found in Seasoned Equity Offerings (SEOs): the time distance between SEO announcement and the last publication of (quarterly) financial statements by the issuer. These statements constitute broadly disseminated and standardized information updates available to all market participants. Contrary to theories of information asymmetry and demand elasticity, but in line with theories of the superior sophistication of institutional investors, we find no robust significant effect of time distance on discounts. While some specifications suggest a negative association as predicted by the first two theories, this pattern proves sensitive to the inference approach and disappears once an interaction structure is taken into account. This interaction overlaps the main effect and, taken together, negates the influence of time distance on discounts. Further robustness analyses underscore the fragility of the relationship, leading to practically relevant near-null results. Our analysis utilizes peculiarities of the German stock market, which help isolate a potential effect, and thus focuses on accelerated SEOs in Germany between 2007 and 2021.
Corporate social responsibility (CSR) encompasses companies’ voluntary actions that integrate economic, social, and environmental concerns into their operations. Concurrently, the circular economy (CE) has emerged as a transformative organizational paradigm that replaces the traditional linear “take-make-dispose” model with a regenerative system to minimize waste and optimize resource use. This study harnessed artificial intelligence (AI) as a tool for predicting circular practices and exploring how CSRI can indicate organizations’ orientation toward CE principles. To this end, the research conducted partial least squares (PLS) structural equation modeling using SmartPLS software to analyze a sample of 129 social economy organizations in Spain. The findings confirm the validity of the proposed causal relationships and demonstrate the potential of AI-assisted analytical approaches for modeling complex constructs. Overall, the results contribute to the literature on the intersection of CSR and CE by providing theoretical and practical insights into how digital technologies can facilitate the transition from linear to circular business models in the social economy sector.
This study explores whether prosocietal individuals can use influence tactics to gain power and reduce socially harmful practices in organizations, despite resistance from peers prioritizing short-term gains. By theoretically driven integration of championing, CSR/ESG, and organizational politics literature, we introduce Benevolent Machiavellianism, i.e., prosocietal organizational politics. In a behavioral task with four-person groups (N = 466), participants repeatedly withdrew funds from a charity pool before competing for votes on decision-making authority. Widespread antisocial behavior persisted, as prosocietal players failed to curb it. Those who deviated from group norms received fewer votes, showing the importance of group alignment over traditional influence tactics. While political skill boosted confidence in persuasion, it had little impact on actual outcomes. Meanwhile, malevolent Machiavellianism and power motives predicted greater charity withdrawals in the final stage. By treating the rarity of Benevolent Machiavellians (estimated to consist around 1.5
