Technological Forecasting and Social Change— Family Business 4.0: Reimagining Family Businesses in t

Family Business 4.0: Reimagining Family Businesses in the Age of Artificial Intelligence

This special issue invites scholarly contributions that explore the drivers and mechanisms of artificial intelligence (AI) adoption, its applications and consequences, future-of-work implications, and other AI-related phenomena within family businesses. The selected contributions will form a comprehensive special issue that maps the current state of knowledge on AI in family business, generates novel empirical insights and theoretical concepts, and outlines future research directions to advance the study of family businesses in the age of artificial intelligence.

Guest editors:

Elias Hadjielias, Cyprus University of Technology, elias.hadjielias@cut.ac.cy

Andrea Calabro, SDA Bocconi, andrea.calabro2@sdabocconi.it

Mohamed Ouiakoub, University of Lorraine, mohamed.ouiakoub@univ-lorraine.fr

Christina Theodoraki, Aix-Marseille University christina.theodoraki@iae-aix.com

Special issue information:

Artificial intelligence (AI) is profoundly transforming entrepreneurial, workplace, and managerial practices across all sectors of the economy (Grashof & Kopka, 2023; Kopka & Fornahl, 2024; Obschonka & Audretsch, 2020). Family businesses (FBs), which constitute a substantial portion of global economic activity, are no exception to this digital revolution (Appleton et al., 2025; Capolupo et al., 2025; Bornhausen & Wulf, 2024; Hevi et al., 2024; Ceipek et al., 2021; Soluk, & Kammerlander, 2021; Soluk, et al., 2021). Researchers increasingly recognize AI as a key strategic asset for FBs, with the potential to enhance continuity, growth, and competitiveness by optimizing operations, decision-making, and customer engagement (Kumar & Ratten, 2025; Tuncalp, 2025). In line with this, recent case studies and empirical investigations highlight the diversity of successful AI applications within different FB functions. For instance, in marketing and customer service, FBs are increasingly deploying AI-powered chatbots to interact with customers continuously and enhance service experiences (Rizomyliotis et al., 2022). In succession planning, AI can help FBs in screening and evaluating potential managerial successors (Ossa-Cardona, 2025), while its application in management accounting has been found to enhance the quality of financial decision-making (Alharasis, 2025). In the commercial domain, the integration of AI tools with customer relationship management (CRM) systems was particularly salient during the COVID-19 pandemic, a period in which many family SMEs were compelled to innovate through AI to ensure their survival (Chaudhuri et al., 2023). Indeed, AI is increasingly seen as a catalyst for innovation in FBs, enhancing their organizational ambidexterity to simultaneously exploit existing capabilities and explore new growth trajectories (Daskalopoulos & Machek, 2025). This view is consistent with recent evidence showing that digital transformation can stimulate breakthrough innovation by reshaping firms’ technological trajectories and capability configurations (Wang et al., 2024). Further evidence shows that digital search activities significantly enhance exploratory innovation in FBs, although this effect depends on family involvement and governance conditions (Ardito & Capolupo, 2023).

Despite the growing awareness and use of AI in FBs, recent studies highlight a slower rate of AI adoption in FBs compared to other organizational forms (Worek, & Aaltonen, 2023). Kumar and Ratten (2025) identify several barriers to AI adoption in FBs, including limited dedicated resources, inadequate technological infrastructure, weak customization of AI solutions for business needs, and insufficient AI-related knowledge and training among family managers. Additionally, FBs are not a monolithic group but rather a heterogeneous category, characterized by diverse governance structures, familial objectives, and decision-making processes (Daspit et al., 2021), which present unique challenges for AI adoption. Ulrich et al., (2023), in turn, argues that slow AI adoption is partly due to organizational traits such as concentrated ownership, risk aversion, and a focus on non-financial goals, which can hinder the uptake of new technologies perceived as threatening to family control or legacy. This ambivalence is consistent with evidence that family management exerts a ‘Janus-faced’ influence on digitalization, simultaneously enabling and constraining technology adoption depending on governance priorities and family control dynamics (Pan et al., 2023). Adding to these complexities, AI can intensify tensions between preserving a family legacy and embracing radical innovation (Soluk et al., 2025), as new technologies can challenge control mechanisms rooted in legacy practices (Ma et al., 2026; Kumar, 2025; Kotlar et al., 2013) and can threaten the firm’s tradition (Soluk et al., 2025). Research shows that digital transformation in FBs is deeply shaped by intra-family conflict dynamics, family values, and generational involvement, which together influence openness to digital tools and the overall trajectory of technology adoption (Gutuleac et al., 2025; Maseda et al., 2025). In addition to the family tradition-innovation paradox, recent research also highlights that obstacles to AI adoption may also be rooted within the ability-willingness paradox. Specifically, FBs may fail to capitalize on opportunities offered by digital transformation, including AI adoption, due to willingness-suppressing dispositions such as resistance to change, centralization of decision-making, or intergenerational tensions (Appleton et al., 2025). The hesitation of FB to fully embrace AI can be understood through the lens of the socioemotional wealth (SEW), which posits that the desire to preserve familial identity, harmony, legacy, and control may outweigh the incentive to innovate through AI (Daskalopoulos & Machek, 2025). This aligns with evidence showing that the performance outcomes of digitalization in FBs depend heavily on the owning family’s goals and priorities, which can either enable or inhibit technology adoption (Issah & Calabrò, 2024).

Beyond the operational scope of the FB, integrating AI carries essential implications for the controlling family as a whole and for its individual members. On one hand, AI can enhance the comprehensiveness of decision-making, potentially disrupting traditional family-centered processes based on intuition and longstanding familial preferences (Daskalopoulos & Machek, 2025). AI can also pose ethical and cultural challenges for FBs (Sawang & Kivits, 2024), as its adoption may threaten to erode the firm’s cultural DNA and core values rooted in the family (Bettinelli et al., 2022). On the other hand, AI integration may intensify the need for greater involvement from the next generation, typically more digitally literate. Lannon et al., (2023) refer to successor cohorts as the “Gen AI,” and call for greater scholarly attention to their role in spearheading AI initiatives within FBs. Highlighting the potential of ‘Theory of Mind’ AI, Sawang and Kivits (2024) also suggest that AI technologies could eventually help mitigate intra-family conflict, support collective decision-making, and streamline often complex succession processes within entrepreneurial dynasties.

Anticipating the skepticism and challenges related to AI adoption in FBs, both for the business itself and the controlling family, several recommendations emerge from relevant studies. Upadhyay et al., (2023) highlight the importance of a stronger entrepreneurial orientation and an innovation-friendly culture within FBs, as these conditions can significantly enhance their willingness to adopt AI technologies. According to Atienza-Barba et al., (2025), FBs can better leverage AI by embracing external knowledge, adopting a proactive stance, and pursuing a value-creating approach rooted in intelligent innovation. Kárpáti and Szüle (2025), in turn, argue that incorporating external know-how through professionalization or the appointment of non-family executives is a key enabler of AI readiness in FBs. Tuncalp (2025) also underscores the importance of structural, cultural, and intergenerational conditions for successful AI integration, including the involvement of key family members, an innovation-oriented governance approach, and the alignment of digital priorities with core family values. Targeted interventions, such as customized training programs, external consulting, and supportive public policies, may be necessary to overcome the unique obstacles faced by FBs in adopting AI (Lannon et al., 2023). Kazancoglu et al., (2021), highlight how Industry 4.0 technologies, when aligned with family values and ethics, can significantly reinforce environmental and societal responsibility in FBs, an opportunity that AI could further amplify.

Beyond firm-level strategy and governance, the adoption of AI in FBs offers a distinctive perspective for examining the future of work. Technological Forecasting and Social Change (TFSC) has played a central role to advancing this literature, accumulating insights over the years on how AI reshapes work roles, managerial decision-making, and the allocation of tasks between humans and algorithms, with significant implications for employee autonomy, skill development, and organizational capabilities (Cheng et al., 2024; Rezaei, 2025; Liu & Mei, 2026). Studies published in TFSC emphasize that AI-enabled work transformations depend not only on technological deployment but also on human agency, including employees’ AI self-efficacy, job crafting, and responsible AI practices that foster innovation and trust at work (Xu et al., 2026; Zhang & Hao, 2025). Additional work further highlights that AI-driven transformations of work and organizational processes are closely tied to firms’ dynamic capabilities, their ability to balance short-term performance with long-term growth, and evolving governance and control mechanisms, with important implications for managerial decision-making and organizational integrity (Chin et al., 2025; Liang & Sun, 2026; Zou & Yang, 2026). From this perspective, FBs represent a particularly relevant empirical context, as intergenerational involvement, socioemotional considerations, and governance structures shape how AI is integrated into everyday work practices, how responsibilities are delegated to algorithms, and how new forms of human–AI collaboration emerge over time (Gutuleac et al., 2025; Maseda et al., 2025).

Building on these considerations, this special issue seeks to stimulate scholarly contributions that explore the drivers, applications, adoption dynamics, consequences, the future of work, and other related phenomena of AI within the FB context. We welcome a broad range of submissions, including quantitative studies, qualitative inquiries, conceptual and theoretical papers, in-depth case studies, and systematic literature reviews, that collectively offer a holistic perspective on the technical, organizational, strategic, and human dimensions of AI in FBs. Submissions may focus on a variety of national and cultural contexts, allowing for comparisons in AI adoption across institutional environments. Comparative studies between family and non-FBs are also encouraged. We also welcome empirical studies leveraging novel AI-driven methods, such as natural language processing or machine learning (e.g., Amore et al., 2024; Veiga et al., 2025), to explore hidden dimensions of family influence, governance, or intergenerational dynamics.

RESEARCH THEMES & POTENTIAL RESEARCH QUESTIONS

We invite scholars from the FB and related fields (e.g., entrepreneurship, technology, marketing, innovation management) to submit original contributions aligned with, but not necessarily limited to, the following themes and research questions:

(1) Drivers of AI adoption in family businesses

• What are the specific enablers and barriers to AI adoption in FBs?
• How do family-specific characteristics (e.g., ownership structure, socioemotional attachment, family leadership, dynastic control) explain delays or, conversely, facilitate AI adoption?
• To what extent can external social capital (e.g., ties with suppliers, customers, and competitors) compensate for internal deficiencies in AI capabilities?
• What role do digitized support functions (e.g., accounting, finance, and HR) play in enabling broader AI adoption within FBs?

(2) AI and family business paradoxes

• How can FBs reconcile the need to innovate through AI with the desire to preserve traditional values and legacy?
• To what extent does the pursuit of SEW by the owning family shape AI implementation decisions? Conversely, can AI adoption itself alter a family’s socioemotional priorities?
• How does the trade-off between financial wealth and SEW influence AI-related decisions in FBs?
• To what extent do willingness-enabling or willingness-suppressing paradoxes mediate the tension between continuity and innovation in AI adoption?

(3) Role of the next generation and skills development

• What role do next-generation family leaders play in facilitating AI adoption and deployment?
• Does the presence of tech-savvy successors (“Gen AI”) accelerate AI implementation in FBs?
• How does intergenerational variation in willingness to engage with digital technologies affect AI adoption trajectories?
• What strategies for training, digital acculturation, and intergenerational knowledge transfer can bridge the AI capability gap in FBs?
• How can AI support succession planning within FBs?

(4) AI, family governance, and organizational design

• How does AI integration affect family governance and the power dynamics between family members and external managers?
• What governance mechanisms best manage tensions between family legacy and digital innovation imperatives, especially in contexts with dual family involvement (ownership and management)?
• Does the use of AI decision-support tools alter the traditionally centralized, family-led decision-making logic?
• Do specific governance configurations (e.g., external board members, successor-led vs. founder-led leadership) facilitate more effective AI deployment?
• What is the role of professionalization (e.g., hiring non-family executives or consultants) in developing digital maturity and AI readiness?

(5) AI’s impact on firm performance and continuity

• To what extent does AI adoption translate into improved performance, innovation, and resilience in FBs?
• How does AI enhance dynamic capabilities and strategic agility in the face of crises or environmental change?
• Can AI contribute to sustainable competitive advantage in FBs, and under what conditions?

(6) Specific AI applications in the family business context

• Which AI applications hold the most promise for addressing family firm–specific challenges?
• How can AI tools enhance customer relationships in businesses with a strong personal or emotional brand identity?
• In what ways can AI contribute to the transfer and management of tacit knowledge accumulated across generations?
• How do managerial knowledge transfer processes influence a family firm’s ability to formulate a proactive AI strategy?

(7) AI, emotions, and stakeholder trust

• How do internal and external stakeholders (family and non-family employees, customers, and partners) perceive the introduction of AI in a family firm? Are there specific trust issues - e.g., a founder’s acceptance of AI-generated recommendations, or customers’ reactions to automated assistance from a firm known for personal service?
• What change management approaches are most effective in overcoming cultural resistance and supporting the transition toward AI?
• Can family values such as integrity, transparency, and accountability be leveraged to accelerate AI technology adoption?

(8) AI and sustainable value creation in family business

• How can AI-driven technologies support environmental and social sustainability initiatives within FBs?
• To what extent does AI adoption enable FBs to implement responsible innovation practices aligned with long-term stewardship goals?
• What tensions arise between AI-enabled efficiency gains and the socioemotional values that underpin sustainability-oriented decision-making in FBs?
• Are FBs more or less likely than non-family firms to leverage AI for ESG performance improvements?
• How can AI help FBs measure and communicate their sustainability impact more effectively to stakeholders?

(9) Ethical issues of AI adoption in family businesses

• How do ethical values, such as integrity and honesty influence the adoption of AI technologies in FBs?
• How do contemporary challenges associated with AI use affect ethical practices in FBs?
• How do ethical governance models in FBs, such as fair practices and faith-based systems, affect the firm’s adoption of AI?
• How can FBs integrate ethical values into their digital transformation efforts?

(10) Family business and the Future of Work

• How does AI adoption reconfigure managerial and employee roles in FBs?
• How do family governance structures and socioemotional considerations shape human–AI task allocation at work?
• How does AI influence skill development, autonomy, and job crafting across generations in FBs?
• How do FBs manage tensions between automation, control, and human agency in the workplace?

(11) AI as a research tool in family business studies

• How can AI be leveraged to advance academic research on FBs?
• Could machine learning techniques be used to identify patterns of successful AI adoption or classify trajectories across large samples of FBs?

The selected contributions will form a comprehensive special issue that maps the current state of knowledge on AI in family business, generates novel empirical insights and theoretical concepts, and outlines future research directions to advance the study of FBs in the age of artificial intelligence. All submissions will undergo a rigorous double-blind peer review, following the editorial standards of the Technological Forecasting and Social Change.

Manuscript submission information:

• The submissions window is September 1, 2026 – February 28, 2027.
• Submissions must be original, unpublished works that are not concurrently under review for publication elsewhere. Submissions should be prepared using the Technological Forecasting and Social Change Manuscript Preparation Guidelines: Guide for authors - Technological Forecasting and Social Change - ISSN 0040-1625 | ScienceDirect.com by Elsevier
• Papers to be considered for this special issue should be submitted online via the Technological Forecasting and Social Change manuscript system: Editorial Manager®, under article type "VSI: Family Business 4.0".
• Papers will be reviewed according to the Technological Forecasting and Social Change blind review process.
• Guest editors will conditionally accept papers: all conditionally accepted papers will undergo a final review by the Editorial Board; only at that time will papers be formally accepted for publication.
• Informal enquiries relating to the Special Issue, proposed topics and potential fit with the Special Issue objectives are welcomed. Please direct any questions to the Guest Editors.

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