Directing the future: artificial intelligence integration in family businesses

IF 3.6 Q2 MANAGEMENT
Deniz Tuncalp
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引用次数: 0

Abstract

Purpose

This study explores the integration of Artificial Intelligence (AI) within family businesses. It seeks to understand how family-owned enterprises navigate the adoption of AI technologies amidst balancing traditional business values and the imperatives of digital transformation. The research addresses the gap in the existing literature by providing insights into the strategic, operational and cultural dynamics influencing AI adoption in family businesses, highlighting the unique challenges and opportunities they face in leveraging AI for competitive advantage while preserving their legacy.

Design/methodology/approach

Employing a qualitative research design, this study utilizes semi-structured interviews with key stakeholders in Turkish family businesses actively engaging in AI projects. Purposive sampling was adopted to ensure a diverse representation of industries and AI adoption stages. The interviews aimed to capture in-depth insights into the motivations, strategies and outcomes of AI integration within these enterprises. Thematic analysis was conducted on the interview transcripts to identify recurring themes and patterns, providing a nuanced understanding of the factors driving AI adoption decisions in the context of family business values and traditions.

Findings

The findings reveal that family businesses in Turkey perceive AI as a strategic tool to enhance operational efficiency and customer engagement. However, integrating AI technologies is often met with challenges, including resource constraints, digital literacy gaps and concerns over maintaining family legacy. Notably, businesses that successfully navigate AI adoption tend to employ tailored strategies that align with their core values, involving key family members in the decision-making process and fostering a culture of innovation. The study also highlights the importance of ethical considerations and governance in ensuring AI initiatives resonate with the family business ethos.

Research limitations/implications

The study’s reliance on qualitative interviews within a single country context limits the generalizability of the findings. Future research could expand the geographical scope and incorporate quantitative methods to validate the identified themes across broader populations. Additionally, exploring the impact of generational differences within family businesses on AI adoption could offer more profound insights. The study underscores the need for a more nuanced understanding of the interplay between technology and tradition in family businesses, suggesting avenues for further investigation into how these enterprises can leverage AI to foster innovation while preserving their legacy.

Practical implications

This research offers practical guidance for family businesses contemplating AI integration. It emphasizes the importance of aligning AI strategies with family values and involving stakeholders across generations in the adoption process. The findings suggest that family businesses can benefit from investing in digital literacy and fostering a culture open to technological innovation. Additionally, the study highlights the need for robust governance structures to navigate ethical considerations in AI adoption. By adopting a strategic approach to AI integration, family businesses can enhance their competitiveness without compromising their core values, ensuring long-term sustainability and success in the digital era.

Social implications

Integrating AI in family businesses has significant social implications, particularly regarding employment and preserving the family legacy. The study suggests that thoughtful AI adoption can contribute to job creation and skill development, counteracting concerns over job displacement. Moreover, by leveraging AI to align with their core values, family businesses can reinforce their role as stewards of social and economic stability within their communities. This research underscores the potential of AI to support the intergenerational transfer of knowledge and values, fostering innovation while preserving the unique cultural heritage of family enterprises.

Originality/value

This study contributes to the emerging literature on AI adoption in family businesses by exploring the Turkish context. It fills a gap in the literature by examining the unique challenges and opportunities family businesses face in integrating AI, highlighting the interplay between technological innovation and traditional values. The research offers valuable insights into tailored strategies for successful AI adoption that respect the legacy and ethos of family enterprises. By focusing on the socio-cultural dimensions of technology integration, this study enriches our understanding of how family businesses can navigate digital transformation while preserving their identity.

引导未来:家族企业的人工智能整合
目的 本研究探讨了人工智能(AI)与家族企业的融合。它旨在了解家族企业如何在平衡传统商业价值和数字化转型的当务之急的同时采用人工智能技术。本研究通过深入了解影响家族企业采用人工智能的战略、运营和文化动态,突出强调了家族企业在利用人工智能获得竞争优势的同时保留其传统所面临的独特挑战和机遇,从而填补了现有文献的空白。采用了有目的的抽样,以确保行业和人工智能采用阶段的多样性代表性。访谈旨在深入了解这些企业整合人工智能的动机、战略和结果。对访谈记录进行了主题分析,以确定重复出现的主题和模式,从而深入了解在家族企业价值观和传统背景下推动采用人工智能决策的因素。 研究结果研究结果表明,土耳其的家族企业将人工智能视为提高运营效率和客户参与度的战略工具。然而,整合人工智能技术往往会遇到各种挑战,包括资源限制、数字素养差距以及对维护家族传统的担忧。值得注意的是,成功采用人工智能的企业往往会采用符合其核心价值观的定制战略,让主要家族成员参与决策过程,并培养创新文化。研究还强调了道德考虑因素和治理在确保人工智能举措与家族企业精神产生共鸣方面的重要性。研究局限性/影响研究依赖于在单一国家背景下进行的定性访谈,这限制了研究结果的普遍性。未来的研究可以扩大地域范围,并采用定量方法在更广泛的人群中验证已确定的主题。此外,探讨家族企业内部代际差异对采用人工智能的影响,可以提供更深刻的见解。这项研究强调,有必要更细致地了解家族企业中技术与传统之间的相互作用,为进一步研究这些企业如何利用人工智能促进创新,同时保留其传统提出了建议。它强调了将人工智能战略与家族价值观相结合以及让各代利益相关者参与采用过程的重要性。研究结果表明,家族企业可以从投资数字扫盲和培养对技术创新开放的文化中获益。此外,该研究还强调,在采用人工智能的过程中,需要建立健全的治理结构,以解决伦理方面的问题。社会影响在家族企业中整合人工智能具有重大的社会影响,特别是在就业和保护家族遗产方面。研究表明,深思熟虑地采用人工智能有助于创造就业机会和发展技能,消除人们对工作岗位流失的担忧。此外,通过利用人工智能与其核心价值观保持一致,家族企业可以加强其作为社区社会和经济稳定管理者的作用。这项研究强调了人工智能支持知识和价值观代际传承的潜力,在促进创新的同时保护了家族企业独特的文化遗产。它研究了家族企业在整合人工智能时面临的独特挑战和机遇,强调了技术创新与传统价值观之间的相互作用,从而填补了文献空白。研究为成功采用人工智能的定制战略提供了宝贵的见解,这些战略尊重了家族企业的传统和精神。通过关注技术整合的社会文化层面,本研究丰富了我们对家族企业如何在保持自身特性的同时驾驭数字化转型的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.50
自引率
33.30%
发文量
51
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