‘Technology may change rapidly, but people change slowly’ (Norman 2002:xiv). While Artificial intelligence (AI) has the potential to transform industries, integrating such technologies often faces resistance because of cultural lag. The phenomenon in which technology changes faster than society is a significant source of social dysfunction (Cruz 2016:65–75). This consequently results in job losses, disillusionment with new technologies, ineffective technological solutions, and broader implications for the economy and societal acceptance of these technologies (Autor 2015:3–4; Griesmyer 2018; Spencer 2021:6–7).

The term ‘Cultural lag’ is a concept introduced nearly a century ago by William Ogburn in 1922, which describes the gap that forms when material culture (technology and tools, etc.) often changes at a faster pace than non-material culture (values, beliefs, social norms, etc.) (Ogburn 1922).

While previous studies have explored technological changes in industries, they have often failed to consider the complex relationship between technological innovations and the cultural environments in which they are introduced (Ciniselli et al. 2024; Lee 2018). A significant research gap in our understanding remains regarding how both material and non-material cultures influence the integration of AI within the selected software development company.

This study proposes to address the critical research gap by applying Ogburn’s Cultural Lag theory to the modern context of AI integration. The study intends to explore the influence of cultural lag on the integration of AI within a software development company in the Western Cape, South Africa, to identify the specific material and non-material cultures that influence AI integration, and strategies to reduce the potential cultural lag, fostering smoother technological transitions and more effective integration of AI in the company. The study holds significance in this era of technological advancement and innovation with AI. The findings could inform stakeholders in the selected software development company, software developers, and policymakers on how cultural lag evolves and on strategies to mitigate its influence not only within this software development company but across various software development industries facing similar disparities.

The literature review provides a foundation built on four tenets, as illustrated in Figure 1, which emanate from the study’s title. Certain discrete understandings of the four tenets were explored, and together they provide a theoretical foundation for the research based on Ogburn’s Cultural Lag theory. In this section, the information technology and social science literature is reviewed to inform the four tenets. Each of these four tenets is explored individually to understand current perspectives, and a proposition is made for how they come together at a theoretical level.

Figure 1 includes all tenets, starting with tenet 1, the ‘Cultural Lag’. It establishes the theoretical lens of the study, defining and introducing Ogburn’s theory, and distinguishing between material and non-material culture. Tenet 1 defines cultural lag as the temporary misalignment that happens because material culture, technologies, tools, and machines advance faster and diffuse more easily than non-material culture, beliefs, morals, customs, and laws, which are less readily changed. It further explains how these two misaligned aspects cause lag and helps explain why society might struggle to keep pace with rapid technological changes associated with AI.

Tenet 2, ‘The Influence of Culture on Technology Integration’, uses Hofstede’s Cultural Dimensions theory to explain how culture influences whether societies perceive, integrate, or resist new technologies and to compare cultures to understand how cultural dimensions influence the integration of technology in societies.

It argues that technology integration is influenced by power distance, uncertainty avoidance, individualism versus collectivism, masculinity versus femininity, long-term orientation, and indulgence versus restraint, which together influence access, risk tolerance, decision-making, and willingness to integrate new tools.

Tenet 3, ‘AI integration in Software Development’, explains that AI is being integrated into the SDLC to automate operations, accelerate development, and enhance productivity. It further highlights advantages that include enhanced productivity and efficiency, software quality, alongside challenges including heavy data dependency, a shortage of skilled professionals, issues with trust and transparency, and perceived threats to job security.

Finally, tenet 4, ‘AI integration in the Software Development Industry’, shifts the focus to the business level, demonstrating the importance of AI integration in the industry and showing that AI is revolutionising software development practices, which helps companies to gain a competitive advantage, improve cost-effectiveness, and leverage AI-powered technologies to drive revenue growth. It also discusses enabling factors that help explain to why some companies are at the forefront of AI integration, while others trail behind. For example, companies integrating AI for automation, prediction, and personalisation outperform manual approaches, improve cost-effectiveness by reducing errors and accelerating development, and use AI-powered technologies such as predictive analytics, chatbots, and virtual assistants to drive revenue growth and customer engagement.

This interpretivist study employed a qualitative research method to gain a thorough understanding of individuals’ lived experiences, perceptions, and attitudes towards AI within the selected company, gathering detailed insights rather than mere statistics and numbers (Creswell 2009; Kumar 2011). In this study, as illustrated in Figure 2, this method was chosen for its ability to explore complex phenomena in real-life settings, particularly when the boundary between the phenomenon (the influence of the potential cultural lag on AI integration) and the context (the selected company within the software development industry) is not clearly defined. This blurring between the phenomenon and the context presents a valuable opportunity for in-depth exploration of the influence of the potential cultural lag on AI integration in a software development company in the Western Cape, South Africa.

This study adopted a single-case study research approach centred around a selected software development company in the Western Cape, South Africa. This qualitative approach allowed for a detailed exploration of a phenomenon within its real-world context. By focusing on a single case, the study thoroughly explores the context and the specific individual experiences of how employees within the company interact with AI, the challenges they encounter, the hesitations they face, and the factors that drive or hinder the process. Therefore, the study gains a much richer and more detailed understanding of the specific cultural aspects involved in AI integration. The single-case research strategy is a recognised qualitative research design (Baxter & Jack 2008:544; Yin 2017), particularly suited to studies in which the aim is to develop a context-rich understanding of a phenomenon rather than to produce findings statistically generalisable to a population. Baxter and Jack (2008:544) argue that a single-case study allows for a detailed exploration of a phenomenon within its real-world context, where the boundary between the phenomenon and the context is not clearly defined. Consistent with this orientation, this study aims to develop a transferable theoretical model of cultural lag and AI integration, grounded in the lived experience of one organisational setting, that future research may extend across multiple cases.

The sampling frame for this study includes Information Technology (IT) professionals directly involved with AI technology within the company, as well as senior management and decision-makers. Additionally, the study includes other relevant staff members who, while not directly working with AI, are still impacted by it. The study employed a set of criteria for inclusion in the study: (1), participants must be employed by the selected software development company and (2) must have been with the company for at least 6 months, (3) participants with knowledge or experience of AI within the software development company were selected, ensuring the data are rich and directly relevant to the research questions. Through a non-probability sampling technique, participants were purposively sampled. Eight participants from the company gave their consent to participate, and interviews were conducted. Data saturation was observed after the seventh interview, with no new concept emerging.

Nonetheless, the additional eighth participant was interviewed to verify the thoroughness of existing codes and capture any divergent perspectives. Ultimately, data collection stopped once no new insights emerged, and comparable instances were repeated. This approach helped to strengthen the credibility and confirmability of the findings.

Because the study is qualitative in nature, interviews were deemed appropriate as a data collection technique.

Specifically, semi-structured interviews were conducted via Microsoft Teams, which was convenient and compatible with the company’s standard technology stack. These interviews were transcribed in real time, with the transcripts serving as the primary data source. To ensure the study’s validity and rigour, the interview protocol was pre-tested with a single participant. Feedback from this pre-test helped to refine the interview guide to ensure clarity, relevance, and alignment with the study’s objectives. The study employed a basic interview guide to steer the conversation and cover all important topics related to the research questions (Creswell 2009). The interview instrument was designed to align with the study’s aim and was based on the four tenets identified from the literature review. The interview instrument was shared with participants beforehand so they could become familiar with the topics to be discussed.

The study employed thematic analysis, based on the framework of Braun and Clarke (2006), to analyse the information collected from the interviews. Thematic analysis was chosen for its systematic method used to identify themes and patterns of meaning throughout the data, making it particularly suitable for studies that explore complex phenomena and subjective experiences (Braun, Clarke & Weate 2016). The data analysis began with the researcher organising and preparing the transcripts, including cleaning the data for accuracy and consistency by removing irrelevant information. Once the familiarisation stage was concluded, the researcher employed ATLAS.ti (ATLAS.ti Scientific Software Development GmbH, Berlin, Germany) to manage and organise the research materials digitally. The researcher then started to analyse the codes and considered how different codes may combine to form an overarching theme.

The next stage involved the researcher reviewing and refining the identified themes. Thereafter, the researcher defined and further refined the themes by identifying the ‘essence’ of what each theme is about (as well as the themes overall) and determining what aspect of the data each theme captures. Having established a set of fully developed themes, the researcher conducted the final analyses and composed the analytical narrative. This narrative included data extracts to illustrate the prevalence of each theme and built an argument related to the research questions as well as the final themes, which provided a thorough understanding of how material and non-material cultural factors influence the integration of AI in the software development company. These were contextualised within and supported by the existing literature. The thematic analysis provided a rigorous approach that directly addressed the study’s aim. The analysis was driven by the information gathered, allowing patterns and themes to emerge naturally without forcing preconceived ideas. This approach laid a foundation for contributing to the theoretical and practical understanding of the phenomenon.

Reflexivity is essential in qualitative research, as researchers cannot fully detach themselves from their study (Palaganas et al. 2017:426–427). The researcher acknowledged that her background in information technology and personal assumptions about AI may have subtly influenced data analysis, potentially making her more sensitive to specific narratives shared by participants. To manage this influence, reflexive notes were maintained throughout the study, allowing the researcher to remain aware of her own biases, particularly during thematic coding. For example, INT1’s frustration that ‘when the AI tools aren’t integrated into your databases or into your systems, it doesn’t actually know who you are as a company’ (INT1, Head of Business Development, 3 years) was initially coded as ‘Resistance to Complexity’, but after re-evaluation and member checking it was recoded as ‘Cost and Procurement Barriers’, emphasising integration challenges rather than user frustration.

In addition to reflexivity, the study employed several practical strategies recommended by Ahmed (2024) to establish trustworthiness through credibility, dependability, and confirmability. Credibility was strengthened through member checking, in which interviewees were sent their transcription and a summary of the main points from their interview, and any amendments were incorporated to ensure the transcript accurately reflected their responses (McKim 2023:45). Dependability was supported by maintaining an audit trail covering the interview process, audio recordings, transcription notes, and analysis, with ATLAS.ti used to organise transcripts, track theme development, and ensure consistency. Confirmability was supported by the reflexive notes described above, which captured personal assumptions and helped to ensure that the findings were not unduly shaped by the researcher’s biases. Further bias-mitigation procedures included pre-testing the interview protocol with one participant to refine wording and remove leading questions, sharing the interview guide with participants in advance to reduce the influence of in-the-moment framing, supervisor review of transcripts and emerging codes, and the conduct of an additional eighth interview after data saturation was reached at the seventh, specifically to test for divergent perspectives and confirm code thoroughness. Together, these procedures support the credibility, dependability, and confirmability of the analysis.

Having outlined the methodological approach employed in the study, which included the research design and data collection procedure, this section provides a detailed analysis of the empirical data and presents the findings based on interviews with participants in the selected software development company. The thematic analysis aligned with Ogburn’s Cultural Lag theory as the underpinning lens.

The participants’ demographics were analysed, providing a clear overview of the eight participants’ details.

The participants were anonymised and are referred to as INT1 to INT8 (Interviewee 1 to Interviewee 8). There were eight participants in the study, and Table 1 presents their demographics and illustrates the diversity of their expertise, ranging from senior executives to technical and creative employees, and reveals varying levels of engagement with and attitudes towards AI integration. This variety strengthens the validity of the study by capturing a broad range of experiences and perspectives in the company.

Ethical approval was obtained from Cape Peninsula University of Technology’s Faculty Research Ethics Committee (No. 217194540/2025/2) prior to the commencement of the data collection phase. Participants were fully informed about the nature and aim of the research and provided written informed consent to participate voluntarily. Confidentiality was strictly upheld. Data were securely stored and accessed only by the supervisor and the researcher. Audio recordings were stored in a secure location for 5 years, after which they will be permanently deleted. The study ensured that participants were treated fairly and equitably, and that selections were made without bias.

As mentioned previously, according to Ogburn (1922), modern culture develops in two aspects: material culture (technology, tools, inventions, etc.) and non-material culture (values, beliefs, ethics, social norms, etc.).

The authors observed that these two aspects do not evolve at the same pace, with material culture typically advancing more quickly than non-material culture. As shown in Figure 6, this disparity, termed cultural lag, highlights the temporary misalignment between these cultural aspects and the challenge societies face in aligning their behaviours with innovation (Ogburn 1922:199).

The findings of this study led to the development of a modified version of Ogburn’s depiction of Cultural Lag theory, as illustrated in Figure 7. This modified version, ‘A Strategy Model to Reduce Cultural Lag for AI Integration’, is specifically designed for the integration of AI in the selected software development company in the Western Cape, South Africa. Unlike the original depiction of Ogburn’s Cultural Lag theory, this model introduces three strategies to reduce the cultural lag, which is particularly relevant in this context. This modified model contributes to expanding the study’s theoretical framework and serves as a practical tool to aid in reducing cultural lag. The model could potentially be applied beyond this single software development company to the software development industry and other industries experiencing similar misalignments.

Figure 7 indicates that as time progresses, material culture advances rapidly, supporting Ogburn’s (1922) argument that material culture typically evolves faster than non-material aspects of society. The material culture is represented by the green solid curve, which rises sharply during the initial phases of AI integration because of the company’s readiness to use AI tools.

The non-material culture, including employees’ attitudes, leadership training and communication processes, is represented by the dashed red curve. Non-material culture initially lags behind material culture as employees deal with distrust, skill gaps and the lack of formal policies.

Over time, however, the dashed red curve is likely to accelerate upward as the company implements strategies such as leadership communication, structured enablement, and operational improvements. The three strategies are presented in chronological order, where each one builds on the previous: firstly, leadership communication aligns the company; secondly, structured enablement then empowers employees; and thirdly, operational improvements formalise the AI integration process.

The distance between the two lines represents the cultural lag, which Ogburn (1922:199) describes as a temporary misalignment between these cultural aspects and the challenge societies face in aligning their behaviours with innovation. Over time, the two curves can converge, symbolising the closing of the cultural lag where technological innovation and company culture evolve harmoniously. This alignment improves AI integration, supporting Ogburn’s theory by suggesting that alignment is both possible and important through deliberate adaptation of non-material culture to match technological advancement.

The study’s limitations include a relatively small sample size of eight participants from a single software development company located in a specific geographic region, the Western Cape, South Africa. The sample size limits the generalisability of the findings to a broader population of software development companies. A larger sample, consisting of more software development companies, may have provided a broader understanding of the varied experiences and perspectives within the industry. Moreover, because this study focuses on the Western Cape region, it may not fully represent the experiences of all software development companies in other parts of South Africa, especially those with different technical and cultural contexts.

This study’s single-case study research strategy limits the ability to generalise the findings to other software development companies. The replication of this single case may prove challenging if the organisational context, technology landscape, or culture changes significantly over time. By recognising these limitations, the study’s findings should be interpreted with caution, and further research is necessary to validate and extend these findings.

The boundedness of the case is a methodological strength as much as a limitation: it permits the depth of engagement necessary to surface the cultural dynamics that broader survey-based designs may miss. Strengths of the study include its alignment with Ogburn’s Cultural Lag theory as an underpinning lens, the use of a pre-tested interview protocol, the collection of data until saturation followed by a confirmatory eighth interview, the application of member checking to support credibility (McKim 2023), and the systematic application of Braun and Clarke’s (2006) thematic analysis framework supported by ATLAS.ti.

Future research could build directly on this study by conducting multicase comparative studies that examine the influence of cultural lag on AI integration across multiple software development organisations, ideally spanning different sizes, sectors, and cultural contexts. Such studies would enable testing and refinement of the proposed Strategy Model in varied settings. In addition, future research could explore conducting longitudinal studies to track changes in AI integration over time. Furthermore, quantitative studies and mixed-method studies could be undertaken to gain a more comprehensive understanding of AI integration.

Finally, further research could focus on the ethical issues arising from the influence and integration of AI in the software development industry.