Leveraging Generative AI to Advance Creative Industries in the Publishing Sector: A Qualitative Grounded Theory Study

Abstract

The rapid expansion of generative artificial intelligence (GenAI)-based generative media is redefining the structures and functions of the publishing industry. In addition to enhancing efficiency and fostering innovative capabilities, this transformation raises fundamental questions concerning creative identity, authorship, public trust, and governance frameworks. Although a substantial body of the existing literature has focused on technological capabilities, the social, cultural, and regulatory dimensions of this transformation, particularly within local and context-specific settings, remain relatively underexplored. This study adopts a qualitative approach grounded in grounded theory to examine how generative media are perceived and integrated into Iran’s publishing ecosystem. Data were collected through semi-structured interviews with eight experts in publishing and technology and were analyzed using thematic analysis, structural co-occurrence analysis, and lexical analysis, leading to the development of a conceptual model. The findings indicate that generative media, as socio-technological systems, simultaneously enhance productivity and creativity while generating concerns regarding content quality, cultural homogenization, professional identity, and ethical responsibility. The core process identified as “redefining creative identity and local governance” explains the transition toward a hybrid publishing ecosystem. The proposed model provides a theoretical framework and practical guidance for balancing technological innovation with cultural values and public trust in the evolving publishing landscape.

Keywords: Generative Artificial Intelligence; Creative Industries; Publishing Ecosystem; Grounded Theory

1. Introduction

Creative industries occupy an increasingly important position at the intersection of culture, technology, knowledge production, and economic development. Publishing is particularly significant because it does more than circulate commercial products: it structures the production of meaning, mediates cultural memory, and supports the diffusion of knowledge across educational, professional, and public spheres. Contemporary accounts of the creative economy therefore treat creative production as a system in which human skill, institutional arrangements, intellectual property, technological infrastructures, and markets interact. Recent global evidence also shows that digitalization and artificial intelligence are becoming central drivers of change within the creative economy, affecting how cultural products are created, distributed, and consumed (UNCTAD, 2024).

Generative artificial intelligence has intensified this transformation. Large language models and other generative systems can produce, edit, translate, summarize, classify, personalize, and reformat textual and multimodal content at a scale that was previously difficult to achieve. In publishing, these capacities can be incorporated into idea generation, drafting, editing, translation, cover and visual development, marketing, audience analysis, and content distribution. Experimental research has shown that generative AI can increase the productivity of knowledge workers and can improve individual creative performance in some tasks (Noy & Zhang, 2023; Doshi & Hauser, 2024). At the same time, the same systems may reduce diversity across outputs, reproduce cultural tendencies, generate false or fabricated information, and create uncertainty about responsibility and intellectual property (Ji et al., 2023; Lu et al., 2025; WIPO, 2024).

These tensions are especially important in creative sectors because the value of creative work cannot be reduced to speed or efficiency. Creative production is linked to identity, originality, authorship, professional recognition, cultural diversity, and trust. The expansion of AI-generated text and images therefore changes not only workflow but also the meaning of creative agency. Research comparing humans and large language models has shown that current systems can perform strongly on divergent-thinking tasks, yet performance on constrained creativity tests does not resolve questions of cultural meaning, authorship, responsibility, or long-term creative diversity (Hubert et al., 2024). This distinction matters in publishing, where editorial judgment, contextual understanding, voice, and credibility remain central to the relationship between creators and audiences.

The problem is therefore not simply whether generative AI can perform publishing tasks. The more consequential question is how it is embedded in the social and institutional organization of publishing. Socio-technical approaches are useful here because they conceptualize technology as part of a broader configuration of actors, routines, infrastructures, institutions, and cultural expectations rather than as an independent technical force (Geels, 2004). From this perspective, generative AI becomes one component of an evolving publishing ecosystem. Its effects are shaped by professional norms, organizational capabilities, data access, skills, market structures, governance, and the ways in which human actors interpret and use the technology.

A second relevant perspective is media governance. Media governance focuses on the set of formal and informal rules through which media systems are organized and made accountable. It includes public regulation, professional standards, organizational rules, self-regulation, and other institutional arrangements that shape media activity (Puppis, 2010). The rise of generative AI makes this perspective particularly relevant because algorithmic content production raises questions concerning disclosure, accountability, copyright, data use, bias, editorial responsibility, and the preservation of public trust. International guidance increasingly emphasizes that AI governance should combine innovation with human oversight, transparency, accountability, rights protection, and context-sensitive implementation (UNESCO, 2021; WIPO, 2024).

The Iranian publishing ecosystem presents a useful context for examining these issues because technological adoption takes place alongside local cultural expectations, uneven infrastructure, institutional constraints, and evolving regulatory arrangements. This study investigates this context through the experiences of experts from publishing and technology. Its central concern was how generative AI can be used to enhance creative industries in publishing while preserving human creative identity, cultural values, and trust.

Accordingly, the study addressed four related questions: What roles and capacities can generative AI perform within the publishing industry? What cultural, ethical, legal, and economic consequences accompany its use? Which institutional and contextual factors shape its integration? And how can a grounded conceptual model explain the responsible development of a locally appropriate hybrid publishing ecosystem? By answering these questions, the study seeks to move beyond technologically deterministic accounts and to explain generative AI as a socio-technical and governance challenge as well as an innovation opportunity.

2. Literature and Theoretical Framework

The theoretical framework combines socio-technical systems thinking with media governance. In socio-technical theory, technological change is understood as a process that unfolds through interaction between technical artifacts and social structures. Technologies acquire practical meaning through organizational routines, professional roles, institutional expectations, and user practices. Geels (2004) argues that socio-technical systems include networks of actors and institutions, knowledge and cultural meaning, technologies, infrastructure, and regulatory arrangements. This approach is well suited to publishing because the introduction of generative AI simultaneously changes tools, workflows, skill requirements, organizational boundaries, and assumptions about authorship.

In creative work, the socio-technical perspective also helps distinguish augmentation from substitution. Generative systems may extend the range of ideas available to creators, accelerate routine production, and allow small organizations to perform tasks that would otherwise require additional labor. At the same time, a purely efficiency-based implementation may standardize outputs or weaken the professional role of human creators. Noy and Zhang (2023) demonstrated productivity gains when professionals used generative AI for writing tasks, while Doshi and Hauser (2024) found that access to generative AI enhanced individual creativity but reduced the collective diversity of outputs. A systematic review of generative AI in creative contexts similarly identifies both performance benefits and unresolved questions about creative practice, professional roles, ethics, and long-term socio-economic effects (Heigl, 2026).

The risks are not limited to creativity. Generative models may hallucinate incorrect information, reflect biases in training data, and reproduce dominant cultural patterns. A large review of hallucination in natural language generation describes factual unreliability as a persistent technical and evaluation problem rather than a minor implementation issue (Ji et al., 2023). Cultural tendencies are also empirically observable: Lu et al. (2025) showed that generative AI outputs can vary systematically with language and can reproduce different cultural orientations. For publishing, these findings make editorial verification and cultural sensitivity necessary components of any responsible AI workflow.

Media governance complements this analysis by asking how these capabilities and risks are governed. Puppis (2010) conceptualizes media governance broadly as the rules and arrangements that organize media systems. In the context of generative AI, governance includes copyright, attribution, disclosure, quality assurance, professional accountability, data protection, and organizational responsibility. WIPO (2024) emphasizes that organizations adopting generative AI need to identify intellectual-property risks and establish safeguards, while UNESCO’s Recommendation on the Ethics of Artificial Intelligence stresses human oversight, transparency, accountability, fairness, and the protection of cultural diversity (UNESCO, 2021). These principles are particularly relevant for publishing because the sector depends on identifiable authorship, rights management, trust, and the circulation of culturally meaningful content.

The combined framework therefore treats generative AI neither as an autonomous creative agent nor as a neutral tool. It is conceptualized as a component of a hybrid creative ecosystem whose outcomes depend on the relationship among technical capabilities, human judgment, institutional arrangements, and cultural context. This framework provided the interpretive basis for the grounded analysis undertaken in the study.

3. Methods

This study used a qualitative design situated within an interpretive-critical orientation to examine how experts in publishing and technology understand the integration of generative AI into publishing, while also attending to institutional power, cultural consequences, and ethical and legal concerns. The study followed a grounded-theory-informed approach using open, axial, and selective coding, complemented by thematic analysis, structural co-occurrence analysis, and lexical analysis.

The study population consisted of experts in the Iranian publishing industry with relevant knowledge or experience concerning generative AI. Participants were selected purposively and theoretically. Eight in-depth semi-structured interviews were completed. Theoretical saturation was reached after the seventh interview because no new code was identified, and the eighth interview was retained to complete and confirm the analytical set.

The semi-structured interview guide was prepared in advance and addressed the integration of generative AI into publishing processes, technological opportunities, cultural and ethical challenges, and conditions required for responsible development. Semi-structured interviewing was appropriate because it allowed the researchers to obtain expert interpretations and tacit professional knowledge while maintaining enough consistency to compare themes across interviews. Qualitative interviewing is particularly useful when the objective is to understand meanings, practices, and contextualized experience rather than to estimate population parameters (Patton, 2015).

The analytical process proceeded through several connected stages. First, interview material was reviewed and coded using open, axial, and selective coding in NVivo 12, moving from lower-level concepts toward broader categories and the core phenomenon. Second, codes and categories were organized through thematic analysis. Thematic analysis is a flexible method for identifying patterns of shared meaning across qualitative material (Braun & Clarke, 2006, 2019). The analysis generated four major themes: opportunities and benefits; challenges and barriers; resistance and doubts; and development and policy requirements.

Third, structural relationships among nodes were examined using coding-matrix queries and co-occurrence analysis. This stage identified concepts that appeared together and examined the structural centrality of innovation, efficiency, reader experience, quality, bias, and governance-related concepts. Because the software performed poorly with Persian text, codes and subcodes were translated into English before being entered for this part of the analysis.

Fourth, lexical analysis was performed using VOSviewer. The purpose of this stage was not to produce an independent statistical test but to compare recurrent vocabulary with the thematic and structural results. The analysis highlighted terms such as innovation, summarization, translation, quality, rights, bias, and editing. The convergence of thematic, structural, and lexical findings was then used to construct the final grounded conceptual model.

To strengthen trustworthiness, codes were reviewed with colleagues, summaries of interviews were returned to participants for feedback, and the analytical process and decisions were documented. These procedures supported the credibility and dependability of the qualitative analysis.

4. Results

The findings were derived from the combined thematic, structural, co-occurrence, and lexical analyses of the eight expert interviews. The three analytical layers converged around two broad axes: functional usefulness and innovation in content production, and governance, trust, and management of technological risk. Integration of these axes produced the core category of “redefining creative identity and local governance in the critical transition to a hybrid creative ecosystem.”

The thematic analysis identified four main themes: (1) opportunities and benefits, (2) challenges and barriers, (3) resistance and doubts, and (4) development and policy requirements. These four themes formed the primary conceptual structure of the findings and were subsequently examined through structural and lexical analysis. Table 1 summarizes the four themes and their coding evidence, while the complete coding matrix is provided in Appendix Table A1.

Opportunities and benefits. Participants generally described generative AI as a means of increasing efficiency, accelerating content production, and expanding the range of creative possibilities. The main subthemes were faster content production through drafting, summarization, rewriting, and translation; support for creativity and ideation through human-AI collaboration; personalization of content and reader experience; reduction of production costs and democratization of publishing; and the creation of new content types and multimodal forms. The coding output gives the strongest reference counts in this domain to “Support for innovation and Create new ideas” (B6: 8 files, 71 references) and “AI Writing & Translating Assistants” (B7: 8 files, 67 references). “Interactive and Engaging Reader Experiences” (B11) appeared in 6 files with 48 references, while “Automation of Production Process and Increased Speed” (B1) appeared in all 8 files with 38 references. Personalized content (B2) was recorded in 7 files with 39 references, creation of multimedia and interactive content (B4) in 7 files with 36 references, and reduced production and publishing costs (B10) in all 8 files with 33 references. These values are reproduced directly from the original coding table.

Challenges and barriers. Alongside perceived benefits, participants identified a broad set of quality, legal, ethical, and infrastructural risks. The principal concerns were hallucination and low-quality or inconsistent output; uncertainty about copyright, ownership, and data security; the need for human review and continuous quality control; algorithmic bias; black-box decision processes; and technical or cost barriers. The coding table shows that “Low Quality & Inconsistency” (D3) occurred in all 8 files with 33 references, while “Copyright and Ownership Issues and data security” (D2) appeared in 7 files with 28 references. “Data Bias” (D5) was present in all 8 files with 21 references. Ethical concerns (D1), decreased creativity and mental laziness in authors (D11), and content homogenization and repetitiveness (D12) each had 20 references. “Loss of Emotional Authenticity and Human Touch in Content” (D13) occurred in 7 files with 23 references. “Need for Human Review and Editing” (D15) and “Hallucination” (D16) each appeared in 5 files with 10 references.

Resistance and doubts. The third theme concerned the social and identity dimensions of technological adoption. Participants described fear of job displacement, doubts about the quality and authenticity of AI-generated work, cultural resistance to changes in established publishing practices, and concern that excessive reliance on generative systems could weaken professional identity. The coding table records “Fear of Job Displacement and Reduced Human Role & Reduce Wages” (D8) in all 8 files with 27 references and “Resistance from Traditional Stakeholders” (D9) in all 8 files with 33 references. The source analysis further notes that nodes relating to job concerns and distrust were linked to quality and bias concerns, suggesting that resistance was not purely emotional or technological but emerged from an interaction among technical unreliability, ethical uncertainty, and professional identity.

Development and policy requirements. Participants emphasized that sustainable use of generative AI in publishing requires conditions beyond technical adoption. The most important requirements were clear legal and intellectual-property frameworks; ethical standards based on transparency and accountability; investment in technical infrastructure and access; professional education and human-resource development; quality assurance and standardization; and institutional collaboration among government, industry, and academia. The coding output shows that “Legal and Copyright Regulations” (E3) was the most frequently referenced requirement, appearing in all 8 files with 51 references. “Ethical and Regulatory Frameworks” (E4) appeared in 7 files with 34 references, “Developing a clear strategy” (E12) in 5 files with 29 references, and “Training and Empowering HumanRresources and Audiences” (E2) in 7 files with 25 references. “Quality assurance and Standardization” (E11) had 20 references. The roles of government, private sector, and academia were also present in the coding output, reflecting the perceived need for distributed rather than purely organizational governance.

Structural and co-occurrence analysis. Coding-matrix and node-relationship analyses organized the coded material into four clusters corresponding to the four thematic categories. The strongest relationships occurred among nodes associated with innovation, efficiency, and reader experience, while preliminary or descriptive nodes had lower co-occurrence intensity. Figure 1 presents the co-occurrence network of publications concerning generative AI in scholarly publishing for 2020-2025, comprising three principal clusters - generative AI, automation, and workflow efficiency - with 48 nodes and a total link strength of 312. Figure 2 presents the NVivo 12 word-similarity clustering of the interview codes.

Lexical analysis. The VOSviewer lexical analysis and combined word cloud identified innovation, summarization, translation, quality, rights, bias, and editing as central terms in the interview discourse. Their overlap with high-frequency nodes in the thematic and structural analysis was interpreted as convergence across the three analytical layers. The lexical evidence reinforced the dual pattern running through the dataset: participants were strongly interested in practical gains from generative AI but repeatedly linked those gains to quality control, rights, bias, and human editorial involvement.

Integration of findings and core phenomenon. The integrated interpretation proposes that generative media function as a hybrid creative infrastructure. They can strengthen human creativity, increase speed, reduce some production costs, support multilingual production, and enable new forms of reader engagement. At the same time, they create new dependencies on algorithmic systems and intensify concerns about content quality, cultural homogenization, authorship, professional identity, and ethical responsibility. Consequently, participants did not describe generative AI as an autonomous substitute for human creativity. The value of the technology was repeatedly tied to human supervision, editorial judgment, and appropriate governance.

The core phenomenon identified was “redefining creative identity and local governance in the transition to a hybrid creative ecosystem.” This core category captures the central tension of the data: innovation is accepted, but its legitimacy and sustainability depend on arrangements that preserve human agency and cultural values. Causal, contextual, and intervening conditions shape this core phenomenon; action/interaction strategies lead to consequences; and the model includes a feedback loop through which outcomes are continuously evaluated and the context is revised. The resulting grounded conceptual model is presented in Figure 3.

5. Discussion

The findings present generative AI as a socio-technical transformation rather than a narrow productivity tool. Participants clearly recognized the ability of generative systems to accelerate drafting, rewriting, translation, ideation, and multimodal production. This is consistent with experimental evidence showing that generative AI can improve productivity in professional writing tasks (Noy & Zhang, 2023). The prominence of innovation-related and writing-assistance nodes in the coding results also supports the interpretation that users experience immediate practical value from these systems.

However, the findings equally reject a simple narrative in which greater technical capability automatically produces a better creative ecosystem. Participants repeatedly connected generative AI with content unreliability, hallucination, bias, intellectual-property uncertainty, loss of emotional authenticity, homogenization, and reduced human roles. These concerns are supported by external evidence. Hallucination remains a documented limitation in natural language generation (Ji et al., 2023), and recent creativity research shows that although generative AI can raise individual creative performance, widespread use may reduce collective diversity (Doshi & Hauser, 2024). In a publishing environment, where distinctiveness and voice are valuable, this trade-off is particularly important.

The findings also show that resistance is partly a problem of identity rather than merely technological acceptance. Fear of job displacement and resistance from traditional stakeholders were among the repeatedly coded concerns. This suggests that implementation strategies based only on technical training may be insufficient. Publishing professionals interpret generative AI through their understanding of authorship, expertise, value, and professional legitimacy. The core category of redefining creative identity therefore has explanatory value: it captures the fact that the technology changes how people understand creative work itself.

This interpretation aligns with socio-technical theory. A new technology becomes consequential through its interaction with institutions, skills, infrastructures, routines, and cultural expectations (Geels, 2004). The same generative model can therefore produce different outcomes in different publishing systems. Where editorial capacity, data quality, training, and governance are strong, AI may function primarily as augmentation. Where these conditions are weak, the same system may amplify errors, reduce accountability, or shift control toward external technology providers.

Governance emerged as the second major axis of the findings. Legal and copyright regulation received the highest reference count among policy requirements, while ethical and regulatory frameworks, strategic planning, training, and standardization were also prominent. These results are consistent with contemporary international guidance. WIPO (2024) emphasizes that generative AI creates complex intellectual-property risks involving training data, outputs, rights ownership, and organizational safeguards. UNESCO (2021) similarly places human oversight, accountability, fairness, transparency, and cultural diversity at the center of ethical AI governance.

The emphasis on local governance is especially important. Participants did not simply call for the adoption of global rules; the integrated model stresses the need for governance that is culturally and institutionally appropriate. This concern is reinforced by evidence that generative AI systems can exhibit cultural tendencies that change with language and training context (Lu et al., 2025). In multilingual and culturally specific publishing environments, reliance on models optimized primarily for other linguistic contexts may create subtle forms of cultural standardization. Local datasets, human editorial review, and context-sensitive rules are therefore not merely technical refinements but mechanisms for preserving cultural agency.

A further implication is that the appropriate organizational model is likely to be hybrid. The results favor a human-in-the-loop arrangement in which AI supports ideation, drafting, summarization, translation, and optimization while human professionals retain responsibility for factual verification, interpretation, final editing, and accountability. This arrangement follows directly from the finding that productivity benefits and trust risks coexist. It also matches the broader literature on human-AI co-creation, which treats the most effective creative configuration as collaborative rather than fully automated.

The study’s theoretical contribution lies in joining the socio-technical and governance perspectives through a grounded core category. The conceptual model proposes that a sustainable hybrid publishing ecosystem emerges when human agency, technological infrastructure, and culturally sensitive governance develop together. Innovation without governance may weaken trust; governance without skills and infrastructure may block useful innovation; and technology without preserved professional agency may intensify resistance. The model therefore explains adoption as a negotiated transition rather than a one-time technology decision.

6. Practical and Policy Implications

The findings have direct practical implications for publishers, editors, media organizations, and policymakers. First, organizations should formalize human-in-the-loop workflows. Generative AI may be used for ideation, first drafts, summarization, translation, content adaptation, and data-supported optimization, but final responsibility for factual accuracy, editorial interpretation, and publication should remain identifiable and human.

Second, publishing organizations need written policies covering AI-assisted authorship, disclosure, copyright, data security, confidential material, source verification, and responsibility for errors. These policies should be reviewed regularly because both the technology and the legal environment are changing rapidly. Third, investment in technical infrastructure should be accompanied by investment in professional capability. Training should include not only prompt use but also bias detection, fact-checking, copyright awareness, data governance, and critical AI literacy.

Fourth, the findings support development of local and multilingual AI capacity. Curated data resources and evaluation protocols in Persian and other relevant languages may reduce cultural and linguistic mismatch and make quality assurance more meaningful. Finally, policy development should involve government, publishers, professional associations, universities, and technology providers rather than relying on a single actor. Such distributed governance reflects the structure of the publishing ecosystem identified in the data.

7. Limitations and Future Research

The study has several limitations. It is based on eight expert interviews and is therefore context dependent; the findings are analytically rather than statistically generalizable. The sample is focused on the Iranian publishing and technology context, so institutional and cultural conditions may differ elsewhere. The study also combines grounded coding, thematic organization, structural co-occurrence analysis, and lexical analysis. This multi-layered strategy provides triangulation, but it should be interpreted as an integrated qualitative analysis rather than as independent quantitative validation. Finally, generative AI is changing rapidly, so some practical implications may require periodic revision as models, market structures, and regulation evolve.

8. Conclusion

Generative AI is reshaping publishing by changing both what can be produced and how creative work is organized. The study shows that experts perceive substantial opportunities in efficiency, innovation, multilingual production, personalization, and new content forms, but they also identify serious concerns involving quality, hallucination, rights, bias, cultural homogenization, professional identity, and trust. The central finding is therefore not that generative AI should simply be adopted or rejected. Its sustainable use depends on the design of a hybrid creative ecosystem in which technological capability is combined with human editorial agency and locally appropriate governance.

The grounded core phenomenon—redefining creative identity and local governance—captures this transition. A viable publishing ecosystem requires simultaneous attention to human skills, technical infrastructure, legal and ethical rules, institutional collaboration, and continuous evaluation. Under these conditions, generative AI can function as an enabling creative infrastructure rather than an unaccountable substitute for human authorship and judgment.

Declarations

Acknowledgments

This article was extracted from the doctoral dissertation entitled “Development of Creative Industries in the Publishing Sector through the Use of Artificial Intelligence.” The authors acknowledge the guidance and support of the faculty members of the Department of Media Management, Islamic Azad University, Semnan Branch, and appreciate the constructive cooperation and suggestions provided by experts from the Science and Technology Park of Semnan University.

Ethical Considerations

The authors confirm that all ethical requirements applicable to the study were observed, including consent-related requirements, intellectual property, research integrity, responsible use of artificial intelligence, prevention of plagiarism, avoidance of data fabrication or falsification, and avoidance of duplicate publication or other research misconduct.

Conflict of Interest

The authors declare that they have no conflict of interest regarding the publication of this study.

Funding

This study was conducted independently and received no external financial support.

Author Contributions

Abolfazl Kordi: Conceptualization, investigation, data curation, formal analysis, visualization, and writing - original draft. Ehtesham Rashidi: Supervision, methodology, validation, and writing - review and editing.

Declaration on the Use of Artificial Intelligence Tools

Generative AI tools were used solely for language editing, improving textual coherence, translation between Persian and English, and assistance in locating recent scientific literature. All references were verified by the authors. The interviews, coding, analyses, and results were derived from the study data, and the authors retain full responsibility for the content of the manuscript.

Appendix A. Complete Initial Coding Table

Table A1. Complete initial coding matrix