This study explores how Family Collective Psychological Ownership (FCPO) functions as an emergent state that facilitates succession in family businesses. Drawing Social Exchange Theory (SET) as a foundational lens, we argue that FCPO extends SET by incorporating collective and affective dimensions that are often overlooked in traditional exchange models. Through a qualitative multi-case study of nine family firms across two generations, we identify how interdependence and identification among family members contribute to the functional and affective components of FCPO. These components interact to form a dynamic, co-constructed psychological state that supports intergenerational transitions. Our findings demonstrate that FCPO is not a static attribute, but an evolving construct shaped by shared narratives, emotional bonds, and collaborative practices. By conceptualizing FCPO as an emergent state, this study advances theoretical understanding of succession as a relational and affective process, offering new insights into how family firms sustain continuity across generations.

Manufacturing firms are increasingly pursuing AI-led transformations (e.g., predictive maintenance, quality inspection, and autonomous production), yet implementation often stalls after pilot projects. Prior literature links this “pilot-to-scale” gap to disorganised data and legacy IT (Clemens et al., 2023; Plathottam et al., 2023; Rauh et al., 2022), misaligned investment logics and budgeting routines (Hadhri et al., 2025), and capability deficits spanning technical skills and digital leadership (Mqoqi et al., 2026; Obi et al., 2025). At the same time, responsible AI introduces additional coordination challenges because accountability, transparency, and compliance remain difficult to operationalise across organisational layers and heterogeneous use cases (Besinger et al., 2025; Eng-ström et al., 2025). Based on previous studies (Engström et al., 2025), we argue that AI adoption is a socio-technical change where value arises from combining technology (models, data pipelines, and platforms) with decision-making rights, incentives, workflows, skills, and accountability. If this combination is weak, organisations might fall into “pilotism,” resulting in many projects but little learning or integration. This study asks: What organisational and governance mechanisms are required in manufacturing firms to convert AI aspirations into scalable and beneficial implementations? To answer our research question, a qualitative approach combining a focused literature review with co-creation workshops involving managers and specialists from six companies (A-F) was employed (Ahmed & Asraf, 2018) to identify AI goals, perceived blockers, and scaling mechanisms through discussions and group tasks (Ørngreen & Levinsen, 2017). The empirical material (field notes, audio recordings, and secondary data) was analysed using content analysis to find recurring patterns (Bengtsson, 2016). Across firms, evidence confirms literature: AI remains stuck in fragmented pilots because strategy, budgeting, data/legacy IT, skills, and governance are misaligned. More specifically, AI remains stuck because key organisational elements are misaligned. Firms often run many promising Proofs of Concept (PoCs), but the organisational system required for scaling (strategy → funding → data/IT → people → governance) does not “fit together”, preventing pilots from being industrialised into scalable, accountable deployments. There is a notable lack of shared direction, which results in isolated initiatives that fail to contribute to overall enterprise learning or effective scaling. The current funding model adheres to traditional ROI metrics, which create barriers to securing resources for scaling successful pilots, as they often fail to meet conventional thresholds or timelines. Many pilots are developed under conditions that do not generalise well, leading to challenges in scalability. Companies lack robust pipelines and deployable infrastructure necessary for broader implementation. There is an over-reliance on a limited number of individuals, as roles and learning routines are not well-defined. This situation hinders the establishment of standardised practices across teams. The absence of clear accountability and risk control mechanisms has blocked the operationalisation of successful pilot initiatives. On the other side, companies are working to tackle challenges by implementing a “governance spine” that merges centralised direction with decentralised execution. This approach features a dedicated AI strategy, clear decision rights, and local ownership through AI ambassadors and cross-functional teams. Key elements include a structured use-case pipeline (idea → business case → pilot → adoption), standardised tracking of ROI and KPIs, and role clarity with capability-building infrastructures for ongoing learning. Strong data governance ensures security and compliance. Successful scaling is more about organisational change than technology, requiring effective leadership and adaptive resourcing to align initiatives with operational needs.

This study investigates how hybrid configurations of entrepreneurial social identity influence radical innovation in new technology-based ventures. Building on the widely used typology by Fauchart and Gruber (2011), which distinguishes among Darwinian, Communitarian, and Missionary identity orientations, we conceptualize founder identity as a multi-dimensional construct, with orientations varying in strength rather than existing as discrete types. Drawing on a survey of 368 early-stage technology firms, we examine how different combinations of these orientations shape radical innovation. Our findings show that hybrid configurations involving two strongly expressed orientations, particularly those combining a Missionary identity with either a Darwinian or Communitarian one, are associated with higher levels of radical innovation. By contrast, configurations where all three orientations are simultaneously strong are less conducive to innovation. These results contribute to entrepreneurial identity research by demonstrating the importance of identity configuration and focused hybridity, and by linking identity patterns to a core organizational-level outcome.

Purpose: This paper examines how gender shapes employees' emotional reactions to workplace AI, and whether these differences are associated with epistemic legitimacy, explained by perceived AI knowledge and conditioned by psychological safety. Design/methodology/approach: We analyse survey data from 104 employees using a conditional process framework. Multiple regression models test direct gender effects on cognitive and emotional reactions to AI, mediation via perceived AI knowledge, and moderation by team-level psychological safety. Findings: Women report less favourable cognitive and emotional reactions to AI than men. These differences are largely attributable to lower perceived AI knowledge. Psychological safety attenuates the direct gender effect: in high-safety climates, gender gaps in reactions diminish; in low-safety climates, they re-emerge. Overall, the pattern supports a contextualised partial-mediation model in which perceived knowledge is pivotal, but its explanatory power depends on climate. Research limitations/implications: The cross-sectional design limits causal inference and the generalisability is bounded by the organisational context studied. Future research should use longitudinal or experimental designs, examine additional inequality dimensions (e.g. age, role), and unpack how AI literacy interventions reshape appraisal dynamics over time.Practical implicationsAI initiatives should build employees' perceived understanding (how AI works, limits, and human-AI complementarity) and foster psychological safety so that questions and uncertainty are acceptable. Monitoring gendered participation and confidence during roll-out helps prevent AI from amplifying existing inequalities. Social implications: Managing AI adoption as an inclusion challenge-rather than solely a technical one-can reduce uneven emotional costs of digital transformation and support fairer access to AI-enabled opportunities. Originality/value: The study integrates gender, appraisal (perceived AI knowledge), and climate (psychological safety) into a single framework explaining both cognitive and emotional reactions to AI. It reframes gender gaps as contextual, highlighting levers - literacy and climate - that organisations can use to enable more equitable AI adoption.

The purpose of this work is to explore whether high levels of emotional intelligence as a competence enhance students' and graduates' perceived fit with innovative organizations, thereby increasing the likelihood of employment. Our empirical analysis is conducted using original data from an online survey of 478 respondents, including both current students and employed alumni in business studies at an Italian university. Findings highlight EI as a significant predictor of employment in innovative organisations, regardless of their size (SMEs vs larger firms) and industry (manufacturing vs services). These results suggest that universities should design their courses and curricula to focus on both technical and soft skills that foster students' employability in innovative organizations.

AI is viewed as the next major technological breakthrough for organizations. The range of areas, professions, and practices that can be improved with AI assistance or automation is overwhelming. However, this wide array of possibilities also brings a variety of expectations about how AI will change organizations and employees’ everyday work. Considering that voiced expectations influence adoption processes by both reflecting and shaping certain relational, belief-driven dynamics, we can learn a great deal about AI adoption by studying the organizational plurality of AI expectations. Therefore, this study examined AI expectations held by employees in three organizations currently adopting AI for use in the workplace. The study is based on a thematic analysis of empirical material form 15 focus groups in three Swedish AI-adopting organizations and shows how AI expectations shape the following: (1) a growing desire to move from exploring AI to establishing AI routines and regulations, (2) emerging dilemmas related to both the violation and fulfillment of AI promises, and (3) how dynamics and unpredictability in the AI field require organizations to adapt to shifting trends and innovations.

Purpose

Artificial intelligence (AI) has the potential to be a disruptive technology instigating paradigm shifts and significantly impacting both operational and strategic processes while presenting organizations with contradictory demands. This paper aims to explore tensions in the early stages of AI adoption in manufacturing firms.

Design/methodology/approach

Drawing on paradox theory and 23 focus groups with 112 white-collar employees, we identify 9 themes categorized into 2 overarching dimensions: (1) temporal tensions between current capabilities and anticipated futures and (2) relational tensions between human and machines.

Findings

Temporal tensions unfold along emergent trajectories where progress is uncertain, nonlinear and continuously negotiated. Relational tensions are marked by ethical reflexivity, as employees navigate shifting expectations around trust, control and human identity. Our findings show how these tensions reinforce one another and contribute to organizational hesitation and strategic drift.

Research limitations/implications

The study focuses on large multinational manufacturing firms, limiting the generalizability of findings to other industries or small and medium-sized enterprises. Additionally, the study captures only the early stages of AI implementation, necessitating further longitudinal research to examine long-term organizational adaptations. Future research should explore cross-industry comparisons and investigate how different leadership styles influence AI-driven transformations.

Practical implications

This research provides actionable insights for managers navigating AI integration. Addressing tensions proactively – through strategic workforce upskilling, clear AI governance frameworks and fostering human-AI collaboration – can mitigate resistance and maximize AI’s benefits. Organizations should embrace paradoxical thinking, leveraging AI to enhance productivity while supporting employees in transitioning to evolving roles.

Social implications

The study highlights the societal impact of AI adoption in the workplace, particularly concerning job displacement fears and the evolving human-machine relationship. Managing AI transformation responsibly requires ethical considerations, workforce empowerment and inclusive policies to ensure that AI-driven innovations benefit employees and organizations alike.

Originality/value

By conceptualizing AI adoption as a process shaped by interwoven temporal and relational paradoxes, the study offers new insights into the emotional, ethical and structural dimensions of integrating AI into established organizational settings.

Knowledge about the antecedents of collective psychological ownership (CPO) in family firms is limited, despite its relevance for theory and practice. To address this gap, we adopted a multilevel configurational approach, encompassing organizational and individual attributes, to consider multiple possible combinations of the causal determinants of the perception of CPO over the organization. The empirical data comprise a sample of 162 individuals from 33 privately held family businesses in Belgium. More specifically, we conducted a fuzzy set qualitative comparative analysis (fsQCA), which was particularly appropriate given the aims of our study. By identifying distinctive configurational paths and three key mechanisms operating across such paths (socialization, identification and signaling), our results offer two important contributions. First, we contribute to the conceptual understanding of the antecedents of CPO in family firms, both for family and non-family members. Second, we expand the general literature on the paths leading to CPO by proposing an approach that combines individual attributes with organization-level features.

Research Summary: This study draws on the behavioral theory of the firm and a duality perspective to investigate the impact of founders' focus on academic goals on the financial performance of academic spin-offs (ASOs)—a specific type of hybrid venture. We theorize that such relationship follows an inverse U-shaped curve and is moderated by the degree of academic ownership. These hypotheses are tested using a sample of 179 Italian ASOs. Our findings indicate that when academic ownership is low, the relationship displays an inverted U-shape. Moreover, as academic ownership increases, the relationship flattens and eventually shifts to a U-shape. These results challenge the prevailing notion of inherent conflicts between economic and non-economic logics in hybrid ventures, demonstrating when focusing on non-economic (e.g., academic) goals enhances financial outcomes. Managerial Summary: Academic spin-offs (ASOs) play a pivotal role in science commercialization and often pursue academic goals due to their academic origins. However, the extent to which founders' focus on academic goals benefits or hinders ASOs' financial performance has remained largely underexamined. In this study of 179 Italian ASOs, we investigate the relationship between a focus on academic goals and firm performance. Our findings reveal that at lower levels of academic ownership, a moderate focus on academic goals is optimal for ASOs' financial performance. Conversely, at higher levels of academic ownership, either a low or high focus on academic goals proves optimal for financial performance. These insights can help practitioners improve ASO performance by aligning goal and ownership structures.

Corporate venturing initiatives are crucial for the survival, profitability, and growth of family firms. Although research on corporate venturing in family firms has expanded significantly over the past decades, it remains fragmented and conceptually inconsistent. To address these gaps, this systematic literature review comprehensively maps and integrates the existing research on corporate venturing in family firms, focusing on antecedents, intervening factors, outcomes, and their interrelationships. The review specifically considers the intertwined family, ownership, and business levels that together form the social system in which corporate venturing is embedded. Based on this analysis, the review offers several recommendations and suggests potential directions to advance the field further.