Ethical requirements for generative AI in brand content creation: a qualitative comparative analysis

Abstract

With the rapid integration of Generative AI (GenAI) for brand content creation in content marketing, clearer guidelines for responsible adoption have become important. For this reason, this study identifies and validates ethical requirements for GenAI through qualitative comparative analysis (QCA) of 33 global AI ethical guidelines alongside key ethical concerns in content marketing through the lens of deontology theory. Eight factors became evident, namely, transparency, privacy, intellectual property, fairness, accuracy, accountability, compliance, and discrimination. Intellectual property is demonstrated to be particularly important for protecting brand reputation, which is frequently overlooked in general AI ethical guidelines. While ethical principles for AI use have been well documented, this study focuses on brand content creation, aligning ethical considerations with practical marketing requirements. Consequently, guidelines for GenAI ethics for brand content creation in content marketing are proposed.

1 Introduction

Although Artificial Intelligence (AI) has been used in marketing for years, significant attention arose with the release of ChatGPT in November 2022, a conversational AI developed by OpenAI that generates human-like text responses based on user prompts (Kim and Moon, 2025; Coltri, 2024; Kshetri et al., 2023). While a universal definition of AI is still contested in the literature, it can be explained as “a social and cognitive phenomena that enable a machine to socially integrate with a society to perform competitive tasks requiring cognitive processes and communicate with other entities in society by exchanging messages with high information content and shorter representations” (Abbass, 2021). Generative AI (GenAI) is a subset of AI and refers to advanced AI technology that can rapidly generate fresh digital content encompassing text, images, video, audio, and more by responding to specific user prompts (De Cremer et al., 2023). The release of ChatGPT highlighted GenAI’s in part of content marketing. Content marketing is a strategic approach that focuses on driving brand engagement with resonant brand content (Hollebeek and Macky, 2019).

As GenAI becomes increasingly popular for enhancing productivity in content marketing, the ethical considerations surrounding its use become more important. Examples of GenAI-related ethical violations include the dissemination of misleading information, which might damage consumer trust, as well as copyright infringements, in which AI-generated content may mimic existing works without proper credit. Such oversights can have a negative impact on a brand’s reputation (Louw, 2023; Taylor, 2023).

The ethical implications of using GenAI in brand content creation are still debated in academic and practitioner literature (Coltri, 2024; Wahid et al., 2023). Despite the availability of numerous AI frameworks, issues such as insufficient ethical knowledge, ambiguous ethical guidelines (Khan et al., 2022), and a lack of applicability in real-world business contexts (Attard-Frost et al., 2023) persist. Furthermore, conflicting views on AI ethics impede responsible implementation (Cox, 2022; Khan et al., 2022).

Consequently, this study first identifies and then validate the ethical factors (to be referred to as conditions in this study) required for responsible adoption and decision-making when using GenAI for brand content creation in content marketing.

The research question guiding this study is: Which ethical conditions are required to ensuring responsible adoption and decision-making when using GenAI for brand content creation in content marketing?

To answer this research question, the study uses Qualitative Comparative Analysis (QCA) to compare 33 AI guidelines with ethical concerns identified by scholars and experts in content marketing. While several AI ethics studies present general ethical principles, this study specifically focuses on brand content creation in content marketing and proposes ethical guidelines for GenAI.

The remainder of the paper is structured as follows: a literature review, methodological steps that include the findings, discussion, theoretical and practical implications for brand content creation using GenAI, and conclusion.

2 Literature review

A literature review serves as the first step of a QCA study to identify the relevant theories, concepts, and previous research related to the topic.

2.1 Digital content marketing

Since Rowley’s (2008) first scholarly publication about digital content marketing, the matter of how to establish expertise and advance brand awareness with brand content on different digital platforms has been well studied and documented (Bubphapant and Brandão, 2023). While there are various perspectives on content marketing in the literature, Beard et al.'s (2021) study of its history reveals consistent objectives and strategies, such as educational content distribution and brand management. Furthermore, the emphasis on value creation as part of marketing communication has been a consistent theme in content marketing practices over time, aligning with marketing theory’s service-dominant logic principles (Beard et al., 2021).

The diverse perspectives on content marketing are also evident in the numerous definitions. The Content Marketing Institute’s (n.d.) definition of content marketing as “a strategic marketing approach focused on creating and distributing valuable, relevant, and consistent content to attract and retain a clearly defined audience — and, ultimately, to drive profitable customer action,” is still widely recognised. Proposing an activity-based view, Koob (2021) defines content marketing as “a set of specific activities, comprising content marketing strategizing, content production, content distribution, content promotion, performance measurement and content marketing organization.”

To interact with, entertain, and build long-term loyalty with the intended audience, content marketers create brand content such as blog posts, articles, videos, social media posts, whitepapers, infographics, webinars, emails, newsletters, eBooks, case studies, and podcasts (Pulizzi and Piper, 2023). Content marketing thus allows for more interactivity in brand conversations (Ahmad, 2025). To rank well in search engines, content marketers depend on human–created content that requires a significant amount of time and resources to produce (Reisenbichler et al., 2022). Creating resonating and helpful brand content also requires repetition and consistency, while content marketers must face a target audience with “content fatigue” (Shehu, 2023). Marketers can thus use GenAI tools to create and distribute content in less time and effort (Capgemini Research Institute, 2023).

This study draws from Arrivé’s (2021) work, which states that digital brand content is a hybrid strategy that includes relational, transactional, and product aspects. Brand content aims to attract consumers and establish connections (relational), whereas product-related content drives sales and meets specific marketing objectives (transactional) (Arrivé, 2021).

2.2 Content marketing and generative artificial intelligence

GenAI is rapidly evolving with OpenAI, an artificial intelligence research laboratory, adding new advanced features to GPT-4-Turbo (an advanced version of ChatGPT) as of the writing of this paper. Already, brands are able to streamline all aspects of the content creation process, from demographic research, and brainstorming ideas to generating content (Brüns and Meißner, 2024).

ChatGPT and other GenAI applications are focused on content production, which makes them especially useful for content marketing (Wahid et al., 2023). For example, content marketers can create a prompt to generate a headline that matches the content, a blog post on a specific topic, or a topic outline, to name a few. The AI will then in response generate the requested outputs in seconds (De Cremer et al., 2023; Soni, 2023).

Using AI for content marketing has been reported to have many benefits. Early empirical data emphasises GenAI’s profound role in content creation in terms of personalisation and the generation of large-scale persuasive content (Brüns and Meißner, 2024). A case in point is that content can be created and optimised for search engines more quickly and affordably, improving productivity and lowering content strategy costs. Also, topic clusters generate more content ideas and enable more personalisation (Rodrigue, 2023). GenAI thus provides significant advances in content generation efficiency, scalability, and personalisation, all of which are critical to the success of digital marketing campaigns (Soni, 2023).

Table 1, below, indicates selected areas of content marketing activities where GenAI tools can be used, with examples.

However, views on AI’s effectiveness for generating brand content are still mixed. Puntoni et al. (2021) found that AI-generated content was less engaging and less often shared than human-generated content. On the other hand, an earlier study by Thontirawong and Chinchanachokchai (2021) showed that content marketing campaigns using AI had higher click-through and conversion rates. Similarly, a study by Hartmann et al. (2023) revealed that AI can produce comparable or better findings than human-generated content. However, Reisenbichler et al. (2022) argue that, while machine-generated content is intended to rank well in search engines, the role of the human editor is still necessary.

Nonetheless, a report published in 2023 by the Capgemini Research Institute identified an increasing trend in the use of GenAI for content marketing purposes, which is expected to continue in the future. Their report is based on the findings of a survey of 1800 chief marketing officers from 14 countries and 25 in-depth interviews with industry experts across different sectors. Their findings reveal that GenAI enables independent creation, innovation, and adaptation and is rapidly transforming traditional marketing strategies. For example, marketers adopt generative AI to develop campaigns, improve customer experiences, and perform data analysis (Capgemini Research Institute, 2023). Similarly, content marketers are increasingly using GenAI technologies to improve their strategies and operations. From content development, curation, and search engine optimisation (SEO) through distribution and performance analysis, AI can be applied at many phases of the content marketing process (Capgemini Research Institute, 2023; Wahid et al., 2023).

2.3 AI ethical guidelines and studies

Standards and guidelines are important in addressing ethical concerns about AI. Because of the rapid advancement of AI technologies, the discussion of AI ethics has gained prominence, as has the number of ethical principles and standards published globally over the years (Cox, 2022). Jobin et al. (2019) identified 84 ethics guidelines issued by international agencies, governments, technology corporations, and others to guide ethical AI practices.

Academic debate about AI ethics is also widespread. In 2022, the journal AI published a special issue on “Standards and Ethics in AI” that examined various AI ethics standards and legislation being developed globally (Rivas and Bejarano, 2022).

Of note is a 2022 systematic review of the ethics of AI that revealed 22 global ethical principles and 15 challenges. Amidst the many global ethical principles, transparency, fairness, privacy, and accountability are highlighted as the most commonly needed AI ethics principles. On the other hand, the most significant challenges are a lack of knowledge about ethics and vague principles (Khan et al., 2022).

Hagendorff (2020) evaluated 22 AI ethics guidelines and realised that current AI ethics regulations fail due to a lack of reinforcement systems, making ethics appear as an afterthought rather than as an integral part of using AI. Also, software developers frequently prioritize economic incentives over ethical values.

In recent years, the ethical principles of GenAI have faced criticism, especially in business and academic settings. There is also scholarly concern regarding bias and discriminating trends in the outputs of GenAI systems. To illustrate, Huanga et al. (2025), in a systematic mapping review of 39 publications, observed that GenAI models routinely reproduce established social stereotypes. This tendency can be attributed to biases within the datasets used during model training. Although various corrective strategies have been proposed, their application remains inconsistent, with shortcomings in sectors such as healthcare and public governance.

Concerns regarding transparency, authorship, and intellectual property rights are also gaining attention. The OPUS Project (2024) highlights the lack of transparency of many GenAI models, which are often referred to as “black boxes” due to their limited interpretability. The lack of transparency, along with the absence of clear disclosure guidelines, makes it difficult to verify results and determine who is accountable. Transparancy and intellectual property can become important in brand content creation, where originality and traceability of content are essential.

Furthermore, the European Commission (2024) cautions against the use of third-party data without consent and refers to the legal implications of generating content based on copyrighted materials. The Commission’s recommendations call for disclosure of any GenAI tools used in research and emphasize the importance of correct attribution of such work. Singh et al. (2025) add to these concerns by addressing the broader legal and ethical uncertainties surrounding authorship and intellectual property. They point out that existing legal systems do not recognize AI as capable of holding rights or responsibilities, thus complicating the allocation of credit and accountability in AI-assisted work.

Another review of 47 AI ethics guidelines found that they were inapplicable in both business and political economy settings. According to Attard-Frost et al. (2023), AI ethics guidelines prioritise algorithmic decision-making over aspects such as fairness, accountability, sustainability, and transparency in the context of business decision-making for AI systems.

These differing views show that while existing AI ethics frameworks provide a helpful starting point, they are often inadequate when it comes to dealing with the complex, real-world ethical challenges of using GenAI in areas like content marketing. A more tailored ethical framework is therefore required that considers both normative principles and the practical implementation challenges associated with using GenAI for brand content creation in content marketing.

2.4 Concerns about the ethics of using GenAI for brand content creation

While the adoption of GenAI has increased rapidly, marketers around the globe are both cautious and aware that there are potential ethical risks when using GenAI to create content (Capgemini Research Institute, 2023). Already, large language modules, such as Chat GPT, have revealed that they can show training prejudice when generating content (Ray, 2023). Also, many incidences of inaccurate information (known as hallucinations) have been reported (Gocklin, 2023). Fake or fabricated information can harm the business’s reputation and affect trust and customer experiences (Louw, 2023). Furthermore, the ethical implications of GenAI in content marketing include privacy concerns, data protection, transparency and fairness, algorithmic bias, and the possibility of manipulation or disinformation (Coltri, 2024; Mao, 2023).

Table 2 summarizes the most common ethical concerns in the literature, including those of practitioners, regarding GenAI for brand content creation. The literature beyond those sources listed did not add new insights.

3 Deontology theory

The complexity of defining ethical behaviour arises as interpretations of right and wrong evolve, influenced by cultural, societal, and personal perspectives (Bennett, 2015). To address this complexity, this study applies deontological ethics, which is based on fundamental values such as human dignity (Winkler, 2022). Human dignity, defined as the “quality of humanness” (Weisstub, 2002), is inextricably linked to personal integrity and reflects individuals’ inherent worth (Weber, 2024). The principle of personal integrity is essential in establishing universal ethical standards that are not influenced by personal opinions or cultural differences, but based on fundamental values, like human dignity, which apply universally.

Building on this foundation, deontology, as a normative ethical theory, provides the theoretical point of departure for this study, emphasising ethical duties, including the protection of human dignity (Sola, 2023). In particular, in the context of GenAI for brand content creation in content marketing, deontological principles emphasise the importance of privacy, intellectual property protection, and transparency in order to ensure individual rights are respected.

While some scholars argue that virtue ethics is more applicable to AI (Hagendorff, 2020), deontology’s emphasis on universal moral duties is especially relevant in AI’s rapidly changing environment. For example, privacy is not merely a preference but a duty toward respecting consumers’ autonomy. Also, transparency in GenAI-generated brand content reflects a moral obligation to inform consumers, preventing deceptive practices. Virtue ethics may thus not be clear enough to address the complex ethical and legal issues of brand content creation and dissemination (Burton et al., 2017).

Deontological ethics thus directs complex ethical and legal issues in brand content creation, assisting marketers in upholding societal values and maintaining brand integrity in highly regulated environments (Burton et al., 2017; Hunt and Vitell, 1986).

This study defines ethical decision-making as actions motivated by personal integrity that adhere to deontological principles and are carried out through the responsible use of GenAI for brand content creation in content marketing. See also Table 3.

4 Method

To answer the study’s research question, the research method adopted for this study is Qualitative Comparative Analysis (QCA). QCA is both a research approach and data analysis technique that approaches causality through set theory rather than traditional statistical correlation. Instead of focusing on the strength of relationships between factors (referred to as conditions in this study), QCA examines how combinations of conditions lead to an outcome of interest (Schneider and Wagemann, 2012).

QCA is thus a comparison approach for determining conjunctural causality (a combination of conditions) between different cases, often referred to as “causal recipes” (Marx et al., 2014). However, it is important to note that QCA does not make causal inferences or infer population attributes from a sample. The goal is rather to simplify causal interpretation by using case knowledge. For this reason, set relationships are described as cross-case patterns (Ragin, 2014). Given that QCA is a theory-driven methodology, prior theoretical frameworks served as a guide when selecting the conditions for analysis (Schneider and Wagemann, 2012).

Furthermore, Boolean algebra and set theory assist QCA’s quantitative analysis of qualitative data. QCA uses logical operators such as conjunctions (AND) and disjunctions (OR) to explore how different conditions interact to produce outcomes. For instance, A*B* ~ C represents a combination where both conditions A and B must be present, but not condition C, to lead to the outcome (Schneider and Wagemann, 2012).

By examining various cases, QCA reveals how multiple conditions can work together to influence the outcome of interest (conjunctural causation), how different combinations of conditions can lead to the same outcome (equifinality), and how explanations for an outcome can sometimes differ from their opposites (asymmetric relationships). In doing so, QCA not only identifies but also validates the necessary and sufficient conditions for an outcome. Because of its systematic approach, QCA is useful to gain insights into complex phenomena, such as the topic of this study (Thomann and Maggetti, 2017; Wagemann and Schneider, 2015).

Researchers that use QCA can use a binary (crisp) set, where cases are either completely included (assigned a value of 1) or completely excluded (assigned a value of 0), with well-defined boundaries and no uncertainty. On the other hand, they can also use a fuzzy set (fsQCA), where cases can have partial membership with values ranging from 0 to 1, indicating varying degrees of membership and acknowledging uncertainties, accommodating subtle variations in data but introducing complexity to analysis and interpretation (Emmenegger et al., 2013)—see Step 4.

An fsQCA asymmetrical analysis was most appropriate for this study since it helped the researcher identify and validate conditions that were sufficient or essential to explain the outcome, including those that were insufficient yet necessary (Rihoux and Ragin, 2009; Schneider and Rohlfing, 2016; Pappas and Woodside, 2021). For this study, the conditions were the most frequent ethical considerations that are associated with the use of GenAI (see Table 3 and Step 4), while the outcome of interest was which of these conditions are required when using GenAI for brand content creation in content marketing (referred to as precedence).

Thus, the ambiguity, uncertainty, and complexities surrounding ethical decisions to do with brand content creation using GenAI were captured since fsQCA provided a structured and rigorous way to understand how different conditions interact in the selected cases and how they affect the outcome.

A post-positivist research worldview acted as a lens through which to examine the topic, focusing on both objective empirical evidence and subjective interpretations (Gannon et al., 2022). The study received ethics approval from the researcher’s institution on 11 July 2023, which guided implementation.

The steps that were followed are depicted in Figure 1 below:

Figure 1

After the literature review, the next step in a QCA study was case selection.

4.1 Step 2: case selection: choosing the specific cases (units of analysis) to examine

A typical QCA study comprises 10 to 50 cases, although several studies have used more cases (Hanckel et al., 2021). Cases can be selected from either primary or secondary sources, which for this study comprised secondary sources (Mello, 2021).

Selection bias was addressed by establishing transparent and systematic selection criteria (Ragin, 2000). For adequate case selection, the documents had to be homogeneous with sufficient heterogeneity (Wagemann and Schneider, 2015). To accomplish this, documents were selected that could possibly include or exclude the conditions identified for this study as follows (see Table 4):

• Relevance to AI use in brand content creation and content marketing.

• Published or endorsed by credible organisations across sectors (government, public, private, academic).

• Publicly accessible and widely acknowledged as benchmarks in AI ethics.

• The number of cases ideally had to be at least four times more than the number of conditions, for the purpose of logical minimisation (see Step 5), which, for this study was a minimum of 32 cases (Emmenegger et al., 2013).

To ensure a broader perspective, the guidelines were collected from diverse sectors, including (see also Table 4):

• Government and public sector: National AI strategies and legislative frameworks from countries like the United States of America (USA), European Union (EU), and Japan.

• International organisations: Guidelines issued by organisations such as UNESCO and the OECD to reflect global standards.

• Private sector and industry: Corporate AI principles from top tech companies like Google, Microsoft, and IBM.

The 2021 UN Resource Guide on AI Strategies Around the World served as a starting point for this study’s case selection (United Nations, 2021). Search engine queries yielded recent AI frameworks and Acts relevant to AI that were published after 2021, reflecting the latest perspectives. Although the documents examined do not represent all AI guidelines published worldwide, they were adequate to represent the eight conditions investigated. It was thus necessary to include enough documents to adequately represent the eight conditions being studied, as depicted in Table 3 (Ragin, 2000).

To become acquainted with the cases, the NVivo Plus software program was used. NVivo is a powerful qualitative data analysis software that enables researchers to explore, organise, and analyse qualitative data (Tang, 2023). For the purpose of this study, detailed annotations helped in understanding the degree of membership in ethical considerations related to the most common conditions associated with GenAI adoption for brand content creation in content marketing (Mello, 2021 and Table 4).

4.2 Step 3: selecting the specific conditions to analyze as potential contributors to the outcome of interest

The insights gained from the literature in Step 1 (see Table 2) guided the selection of the most common conditions associated with the ethics of GenAI adoption for brand content creation in content marketing (Rihoux and Ragin, 2009; Thomann et al., 2022). Conditions in QCA refer to key factors or variables whose presence or absence may influence the outcome and must not exceed eight (Mello, 2021). The eight ethical conditions (transparency, privacy, fairness, accuracy, accountability, compliance, discrimination, and intellectual property) were selected based on their academic relevance and practical pertinence for brand content creation in content marketing. To illustrate, these conditions include ensuring transparency in AI-generated content, protecting user privacy, promoting fairness by preventing bias, maintaining accuracy, holding content marketers accountable, ensuring compliance with legal standards, preventing discrimination, and protecting intellectual property rights.

These conditions are also frequently cited in AI ethics literature (see Table 2), demonstrating wide agreement on their importance in guiding ethical AI use which can be extended to brand content in content marketing. Also, these conditions align with deontological ethics, emphasizing duties like protecting human dignity and ensuring fairness that are crucial to ethical GenAI practices. These conditions thus address crucial ethical considerations that encourage the responsible adoption and decision-making of GenAI for brand content in content marketing.

Because of their multidimensional nature, the conditions identified in this study were examined through the lens of personal integrity literature and deontological theory, as shown in Table 3. It is important to note that these conditions are interdependent and should not be viewed in isolation.

4.3 Step 4: calibration: assigning membership scores

During step 4, membership scores were assigned via a process known as calibration. For this study, indirect calibration to assign membership scores for a data matrix was compiled (see Tables 5 and 4).

Indirect calibration relied on the researcher’s broad groupings of cases according to their degree of membership based on case knowledge (Ragin, 2008). Guided by the work of Rihoux and Ragin (2009), and due to the complexity and interconnection of ethics, a four-value approach to assigning membership scores was adopted.

The membership scores for this study were assigned as follows:

1: full membership (a case fully includes the condition).

0.67: more in than out (a case mostly includes the condition but not entirely).

0.33: More out than in (a case mostly lacks the condition but contains some dimensions).

0: full non-membership (a case does not include the condition at all).

However, it is acknowledged that the indirect calibration process inherently involves some degree of subjectivity, particularly when it comes to assigning membership scores. Thus, to minimise subjectivity, the calibration process relied on clearly defined thresholds for each score (the four-value approach) and transparent documentation (annotations). These thresholds draw upon QCA literature (Rihoux and Ragin, 2009) while the scores allocated were derived from a combination of case knowledge and empirical observations (Ragin, 2008; Rihoux and Ragin, 2009).

Table 5 depicts the thresholds for calibration of the conditions. For transparency, Table 5 was developed using a coding scheme derived from NVivo annotations, which identified the membership of each ethical condition across the 33 documents. To illustrate, a full membership score of 1 for IP was only assigned if the document clearly mentioned enforceable IP protections or referenced legal instruments (for example, World Intellectual Property Organization or national copyright laws), or provided strategies for infringement prevention. However, documents that lacked such recommendations or treated IP in vague terms were given lower scores.

In this study, the independent variables were transparency (TP), privacy (Priv), fairness (Fair), accuracy (Accur), accountability (Account), compliance (Compl), discrimination (Discr), and intellectual property (IP), while the dependent variable, representing the outcome of interest, was precedence (Prec).

The calibrated data matrix in the table below shows the values of each independent variable and their corresponding outcomes for 33 cases. Using a fuzzy set theory approach, the outcome score is based on the weakest link in the dataset (Kacprzyk, 2023) and represented as a binary crisp number (Dusa, 2020).

The calibrated data matrix was then imported into the fsQCA software (version 4.1) for further analysis. The software is intended to make it easier to apply QCA through the use of fuzzy set theory. The descriptive statistics for the dataset are as follows:

The findings show consistent high levels of transparency, privacy, fairness, accuracy, accountability, compliance, and discrimination across the cases examined, as well as significant variations in intellectual property (Table 6).

4.4 Step 5: truth table construction

During this step the data matrix was regenerated as a truth table, displaying condition configurations and their effects on the outcome. Truth tables list all logical combinations of the conditions under consideration. Each combination was compared to the empirical data to establish if it caused the outcome. Providing evidence for a combination was sufficient for the outcome (Mello, 2021; Wagemann and Schneider, 2015). The truth table was then minimized while retaining essential data. The minimized truth table includes only the essential configurations that cover all outcomes, simplifying the representation (Ragin and Davey, 2022).

Using the Quine-McCluskey algorithm, the Table 7 depicts the minimized truth table with a frequency of 1 and a consistency threshold of 0.8 (Goertz, 2017).

The minimized truth table analysis revealed two main configurations: one where all conditions, including intellectual property, are present, leading to precedence in 26 cases; and another where all conditions except intellectual property are present (for 7 cases), resulting in the absence of precedence. This variation highlights the essential role of intellectual property in determining precedence.

4.5 Step 6: analysis of necessity

This step examined how much each condition is needed for the outcome of interest by comparing cases and determining how it affected the outcome (Schneider and Wagemann, 2012).

QCA emphasizes consistency and coverage. Consistency measures how steadily a set of conditions causes the outcome across cases, while coverage measures how well the identified conditions account for all cases with the outcome (Mello, 2021; Schneider and Wagemann, 2012). Exploring the complexity of causal relationships within the dataset by focusing on the overarching patterns and configurations of the outcome was necessary to find the necessary conditions with which a subset/superset analysis helped. Subset/superset analysis tested the sufficiency of a condition or any combination of conditions, meaning that their presence was enough to produce the outcome even if they were not present in every case (Ragin, 2008; Schneider and Wagemann, 2012).

Table 8 highlights the conditions or combinations of conditions that are crucial for the outcome, precedence. It is evident that all the conditions tested individually show high consistency and raw coverage, implying they are all crucial and applicable to a broad range of cases in determining the outcome, precedence. Interestingly, the condition of intellectual property stands out with a high consistency score of 0.971342, but when its contribution to the outcome is not considered, the consistency score drops to 0.584025 (TP* Priv* Fair* Accur *Account*Compl* Discr ~ IP).

When all of these conditions are taken into account (TP* Priv* Fair* Accur* Account* Compl* Discr, *IP), the consistency score is the highest, indicating that these conditions have a significant effect on precedence.

4.6 Step 7: analysis of sufficiency

Step 7 involved an analysis of sufficiency (how much each condition is required for the outcome to occur). The conditions that must be present (or absent) for the outcome to occur consistently across cases were highlighted. The complete range of sufficiency solutions is presented in terms of the necessary, complex, the parsimonious and intermediate solutions (Mello, 2021; Schneider and Wagemann, 2012).

The complex solution identifies a set of conditions that, when present together, consistently lead to the occurrence of the outcome, which for this study represent the eight conditions measured (Schneider and Wagemann, 2012).

During this analysis, no counterfactuals were considered.

The complex solution considers the necessity of the condition intellectual property which is a counterfactual for the outcome (Table 9).

The findings of the parsimonious solution highlight the essential role of intellectual property for the outcome, emphasising its relevance despite being a “difficult counterfactual” (Schneider and Wagemann, 2012; Table 10).

The intermediate solution, which strikes a balance between the complex and parsimonious solutions, shows that all eight conditions must be met to achieve the outcome (Ragin, 2000). Table 11 now summarises all solution types for this study.

While considering the views of Schneider and Wagemann (2012:278), the intermediate solution is presented for further discussion.

According to the findings in Table 11, while all eight conditions are required for ensuring responsible GenAI adoption, intellectual property stands out as a prerequisite for precedence. Without considering intellectual property, the outcome is significantly lower, demonstrating its importance in protecting creative ownership and avoiding legal consequences, despite the fact that general AI guidelines frequently fail to include it.

In contrast, other conditions (such as transparency and privacy) were consistently applied across cases, indicating that they are important but less likely to change than intellectual property. Thus, to maintain brand integrity and avoid reputational damage, content marketers must respect intellectual property rights in addition to other ethical considerations.

5 Discussion

The findings of the intermediate solution are now interpreted and discussed in the same manner as for qualitative studies (see Oana, 2024).

The study identified and validated ethical conditions that are required to ensuring responsible use and decision-making when using GenAI for brand content creation in content marketing.

While the findings highlight that all eight conditions are required, intellectual property stands out as particularly significant for achieving the desired outcome confirming relevance for brand content creation (Schneider and Wagemann, 2012). The importance of intellectual property as a condition demonstrates that it is important for content marketers to respect and protect original content and its authors. Aligning with ongoing concerns in marketing over plagiarism and improper use of existing material (Capgemini Research Institute, 2023; Harris, 2023; Kumar and Suthar, 2024; Wahid et al., 2023). In the existing AI ethics literature, intellectual property is frequently under-emphasized, yet it is fundamental for creative ownership and brand integrity in content marketing (Taylor, 2023).

While intellectual propery emerged as a necessary condition in the QCA findings, its treatment across regulatory contexts differ significantly. To illustrate, in the European Parliament and Council of the European Union (2024) and the European Parliament and Council of the European Union (2016) emphasise intellectual propery and data protections, including provisions for algorithmic transparency and copyright compliance (European Parliament and Council of the European Union, 2024, 2016).

On the other hand, US frameworks such as the California Legislature (2020) focus more on data privacy than creative ownership, overlooking protection for AI-generated content (California Legislature, 2020).

Some Asian jurisdictions have emphasized human-centric AI in their governance, with a focus on innovation. For example, Cabinet Secretariat, Government of Japan (2019) has a more flexible approach to IP. In such contexts, IP is treated more as a guiding principle than legally binding.

This geographical difference strengthens the argument of this paper, namely that IP must be considered when using GenAI in brand content creation because what is permissible in one context may result in legal consequences in another.

The findings thus contradict some AI frameworks that do not prioritize intellectual property (Attard-Frost et al., 2023; Hagendorff, 2020). This suggests that existing frameworks may overlook specific ethical considerations relevant to GenAI in brand content creation such as copyright (Kumar and Suthar, 2024; Louw, 2023).

Also, the fact that all eight conditions identified in the calibrated data matrix are required for ethical brand content creation with GenAI indicates their interdependence. While this study treats the eight ethical conditions as separate factors for the purposes of calibration and comparison, they are interdependent. For example, transparency is closely linked to accountability in that without transparent documentation of how GenAI tools function, it becomes difficult to hold content marketers responsible for ethical mistakes. Similarly, fairness is linked with discrimination and compliance. A failure to ensure fairness in algorithms may lead to discrimination, which may violate compliance standards in jurisdictions with anti-discrimination laws. Privacy and intellectual property frequently overlap, especially where personal or proprietary content is involved. Protecting one without compromising the other is increasingly difficult with ethical risks. Being aware of the interdependence of the eight conditions is thus important since ethical risks may increase when multiple conditions are only partially fulfilled (see also Table 3).

This underlines the complex and interconnected nature of responsible action and ethical decision-making when using GenAI for brand content creation in content marketing (Clinger, 2018). To ensure ethical use of GenAI, all of these conditions must be met and thus a more holistic approach to brand content creation in content marketing is required while also respecting intellectual property.

The findings align with deontological theory by emphasizing the importance of responsibility and obligation in ethical decision-making. They show that upholding ethical duties such as the eight identified conditions is essential for content marketers using GenAI, supporting the view that certain ethical actions must be maintained regardless of their outcomes (Hunt and Vitell, 1986).

5.1 Theoretical implications

Because GenAI ethical guidelines for content marketers are currently lacking, this study focuses on the unique ethical challenges that arise from integrating GenAI into content marketing, distinguishing it from broader discussions on AI ethics.

The method used in this study also adds to the literature by demonstrating how different conditions interact to support responsible GenAI adoption, resulting in a multi-condition analysis that can be used to guide future research in AI ethics and content marketing.

QCA confirmed the importance of ethical decision-making in GenAI for brand content creation in content marketing, implying that both the nature of ethical actions and complex decision-making are crucial. This demonstrates the importance of ethical guidelines that emphazise moral responsibilities in a fast-changing industry.

This study bridges the gap between general AI guidelines and GenAI’s specific ethical challenges in content marketing. The findings highlight inconsistencies between general AI ethical recommendations and real-world ethical decision-making in content marketing, particularly intellectual property risks that can affect brand reputation.

The findings add to the ongoing AI ethics debate by addressing the ethical concerns unique to the specialised area of using GenAI for brand content creation in content marketing.

The study will also stimulate additional academic debate and inform future research on the ethical use of GenAI for brand content creation in content marketing.

5.2 Practical implications

The findings of this study have significance for marketing professionals involved in GenAI for brand content generation in that they can help brands implement responsible use and ethical decision-making in their content marketing strategies.

Given the eight ethical conditions measured, the findings may assist businesses to use GenAI responsibly for brand content creation and brand reputation. Intellectual property was shown to be essential for brand content creation in content marketing in order to protect brand reputation.

Businesses can use the findings of this study to develop internal guidelines for responsible AI brand content generation.

Businesses must monitor compliance with legal and ethical standards to ensure that AI-generated brand content complies with regulatory requirements.

5.3 Proposing guidelines for GenAI ethics for creating brand content in content marketing

Guidelines for GenAI ethics for creating brand content in content marketing are now proposed (Table 12).

6 Conclusion

The findings help to broaden our understanding of which ethical conditions are required to ensuring responsible use and decision-making when using GenAI in content marketing for brand content creation. However, continuing research is required to keep up with the rapidly changing nature of AI technologies and their implications for the content marketing industry.

The study also has some limitations. While fsQCA was useful for identifying configurations of conditions that lead to an outcome, it does not allow for an understanding of the causal relationships underlying these relationships. QCA highlights patterns and associations rather than determining causality, providing insights into condition interdependencies without implying direct cause-effect relationships. Furthermore, the scope of this study was limited to 33 guidelines which were extended to brand content creation in content marketing, therefore, the findings may not be applicable to other fields where GenAI is used or other frameworks. The findings can thus only be generalized to the sample used for the study. Furthermore, the 33 guidelines analyzed reflect the state of ethics at the time of the study and may not account for more recent standards or updates, particularly given the rapid development of AI regulations.

Nonetheless, the findings highlighted the complexities and interconnectedness of responsible use and ethical decisions about using GenAI for brand content creation in content marketing. As a starting point, content marketers could consider the proposed guidelines while marketing strategies could state the intention of providing ethically valuable and relevant content to the target audience to attract and retain consumers’ trust.

Future research could examine how and why these conditions influence the outcome and also add more conditions. QCA researchers could explore the interactions of the eight conditions in more detail by applying fuzzy-set techniques to assess configurations of conditions that consistently co-occur. It would also be interesting if future studies could explore ethical AI considerations in other marketing contexts.

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