Generation AI and family business: a perspective article

Abstract

Purpose

This perspective article responds to the emergence of artificial intelligence (AI) as a significant opportunity for growth among family businesses, highlighting the need for future research to attain a clear picture of the next generation of family business successors.

Design/methodology/approach

This is a literature review of current technology adoption within family businesses. The authors offer some research insight to spur critical thinking and discourse around the impact of AI on family business successors.

Findings

Family businesses are initially skeptical of AI technology. However, its use and adoption are crucial for the survival of the family business. To leverage this technology, the authors need to investigate the role of the family business successors as “Gen AI.”

Originality/value

It is challenging but necessary to develop policies and educational support for successors to ensure the survival of family businesses worldwide. The authors propose four key areas of future research.

Citation

Lannon, F., Lyons, R. and O'Connor, C. (2023), "Generation AI and family business: a perspective article", Journal of Family Business Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JFBM-07-2023-0116

Introduction

Artificial intelligence (AI) presents a significant opportunity for family business growth (Liu, 2020). However, family firms are slow to adopt technological innovation (Chrisman et al., 2015; McElheran et al., 2021). Given that the next generation of family business successors will manage this technological revolution (Zellweger et al., 2015), it is necessary to consider how they may adopt pervasive AI within the family firm. This perspective article examines extant research on the technology adoption of family businesses, with an emphasis on the role of successors, and proposes key areas of research relating to AI adoption in this context. We focus specifically on the next generation of successors to the family firm.

Past research

While it provides competitive advantage, historically, the adoption of technology across the family business sector has been slow (Ulrich et al., 2023; Rashid and Ratten, 2020). Reasons for this resistance to technology are many, including financing, legacy, time constraints and fear of change (Basly and Hammouda, 2020). This trend may be continuing in relation to AI adoption, which is currently more concentrated in large firms (McElheran et al., 2021). Recently, Soluk, (2022) found that only 4.9% of their German family business sample were using AI in their day-to-day business. This may be costly as the disruptive force of generative AI is considered to be more significant than previous technological waves, offering new business opportunities “like never before” (Giuggioli and Pellegrini, 2023, p. 817).

Past scholarly reviews examine technological innovation in family firms, using theories related to Davis (1989) Technology Acceptance Model and Rogers (2003)' Diffusion of Innovations framework (De Massis et al., 2013; Haynes et al., 2021). However, in their recent systematic review of family business succession and innovation, Baltazar et al. (2023) highlights a gap in our knowledge of the effect of succession on company innovation. We extend this thinking to consider the effect of succession on the adoption of recent technological innovations. The next era of family business successors will be from Gen Z (born 1997–2010) and Gen Alpha (born post-2010). These individuals are digital and social media natives and have experienced a global industrial shutdown, pandemic and hybrid working patterns. This cohort currently has elevated levels of access to AI, and it is expected that generative AI tools will permeate their education and working lives. We have termed these generations collectively as “Gen AI.”

As this research area is underdeveloped, having been termed as a “green field” (Levesque et al., 2022), we offer some research insight to spur critical thinking and discourse. In an ongoing research project facilitated by the authors across three Irish institutions, students who have parents who own their own business (124, 31.3%) were compared to those who do not (272, 68.7%) under relevant criteria. Two distinct findings are of significance in discussing the future of Gen AI in family business:

1. Participants who had one or more parents who own their own business (M = 3.11, SD = 1.2) are less nervous about using AI text generation tools for their studies than those who don't have parents who own their own business (M = 3.47, SD = 1.14), t(394) = −2.81, p = 0.005, d = 1.16.

Participants who have one or more parents who own their own business (M = 1.79, SD = 0.95) use AI tools to develop their understanding of business concepts and learn about the industry more than those who do not have parents who own their own business (M = 1.52, SD = 0.85), t(394) = 2.81, p = 0.005, d = 0.88. This insight piques inquiry about future research in this area, with many questions still remaining about Gen AI and the future of family business within the next technological revolution.

Future research

We consider the ways that the next-generation owners may learn about and adopt AI into their future business. Extant research suggests that parental support influences successor self-efficacy (Carr and Sequeira, 2007; Lyons et al., 2023) and their understanding of business activities (Jaskiewicz et al., 2015). We contend that parental (owner) curiosity and desire for innovation within their business operations will have a formative effect on their children. Significantly, the will to embrace technological innovation may also be drawn from the children to the parents (Litz and Kleysen, 2001), thereby creating Gen AI successors. The family business manager is the key technical decision maker; and familiarity, previous use and integration of technology are important drivers of technology adoption (Niehm et al., 2010). This presents an opportunity for future research to examine AI adoption among successors exploring their behaviors and attitudes towards AI and their willingness to change and adapt family business practices as well as investigating existent barriers to AI.

Knowledge is viewed as an important asset within the family business (Ge and Campopiano, 2022). Successors will often engage with business education and courses to learn about the career or relevant discipline (Perez and Puig, 2004). Formal education provides the potential to engage with business tools such as AI. Giuggioli and Pellegrini (2023) note the transformative potential of AI in educating future business owners, unlocking their potential to analyze data, communicate and make decisions in new and robust manners. However, given the surprisingly slow rate of adoption of technology (Ulrich et al., 2023), there is a need to explore the future education of Gen AI. Similarly, understanding is required in relation to ethics of AI and the capacity for discriminative behaviors and misuse of AI by successors. We propose key research opportunities in Table 1 for the exploration of next-generation successors.

Conclusion

Although family businesses remain initially skeptical (Ulrich et al., 2023), the use and adoption of AI technology is a crucial requisite for the successful continuance of the family business sector (Liu, 2020). Leveraging this will necessitate inquiry at the individual and firm level, particularly in relation to family business successors. The development of robust policy and educational support for this cohort, at a pace paralleled to the speed of AI and other advancements, is challenging but necessary for family business survival globally.

Table 1

Key research opportunities

Sub-topic	Themes	Relevant literature
Behaviors of Gen AI successors	Innovation strategies adopted, tools used, ethical considerations	Li and Daspit (2016), Giuggioli and Pellegrini (2023)
Barriers to successor adoption of AI	Stability vs change, legacy, hierarchy	Basly and Hammouda (2020), Chrisman et al. (2015)
Opportunities/antecedents for successor education on AI	Self-efficacy, computer science or innovation education/pedagogy	Ulrich et al. (2023), Pérez and Puig (2004)
Firm-level adoption and use of AI	Technical decision makers; effect of AI on company performance and turnover	Niehm et al. (2010), Chaudhuri et al. (2023)

Source(s): Created by authors