How AI Awareness Develops and Manifests within Local Owner-Operated Businesses

Introduction

In November of 2022, OpenAI released ChatGPT, forever changing how the world perceives artificial intelligence (AI) (Mila et al., 2025). Prior to the release of ChatGPT, the general public seemingly recognized AI as a future technology rather than one with immediate individual and business impact. AI was primarily implemented within organizations possessing dedicated technical expertise and manifesting within specialized business functions (Chui et al., 2022). Within months of ChatGPT's release, however, AI shifted from a niche technology to a mainstream business topic, dominating news feeds, social media, organizational strategy and daily conversations. Today, organizations of every size are evaluating how AI can improve productivity, reduce costs, and create competitive advantage (Mila et al., 2025).

With the rise of AI's implementation, both practitioners and researchers have increasingly focused on implementation. Organizations seek to identify profitable AI use cases, improve operational efficiency, and redesign products and services around AI capabilities. Academic research has similarly concentrated on technology adoption, organizational implementation, and workforce acceptance (Zhu & Hussain, 2025). Surprisingly, little attention has been given to a crucial step that precedes adoption: AI awareness. Before organizations adopt AI, decision makers must first balance cognitive knowledge, behavioral intentions and attitudes, and ethical issues regarding the technology to determine whether AI is relevant for their company (Zhu & Hussain, 2025). Despite representing the earliest stage of the adoption process, AI awareness remains a relatively underdeveloped construct within academic literature.

This gap is particularly important for owner-operated small businesses (OOSB). Large organizations possess advantages that facilitate AI adoption. Research and development costs can be distributed across the organization, AI talent can be recruited with strong compensation packages, and dedicated teams can evaluate emerging technology without disrupting day-to-day operations (Mila et al., 2025). Larger firms also benefit from organizational infrastructure that supports technology implementation and innovation adoption (Thong, 1999).

OOSBs operate under fundamentally different conditions. Rather than relying on specialized departments, owners often act as the company strategist, operator, marketer, financial manager, salesperson, and technology decision maker. Because of this, decisions regarding AI adoption are deeply personal, requiring owners to balance business needs, financial risk, customer expectations, limited time, as well as non-business personal time (Kozlov, 2026). Unlike larger organizations, OOSBs often lack dedicated resources to comprehensively evaluate emerging technologies, like AI, before deciding whether to consider adoption.

These differences are particularly important as OOSBs represent a substantial portion of the United States economy. Nearly half of all U.S. employees are employed by a small business, while 97% of small businesses have fewer than 20 employees (Forbes Advisor, 2024). Understanding how OOSBs become aware of AI, and whether they should consider adopting AI, has implications that extend beyond an individual business. As AI capabilities exponentially expand, the ability of OOSBs to recognize, evaluate, and make sense of AI may increasingly influence their long-term success.

Although AI adoption has received considerable scholarly attention, relatively little is known about how AI awareness develops within OOSBs. Existing research has largely examined AI awareness among workforce dynamics within larger organizations, leaving owner-operated businesses largely absent from post-ChatGPT literature (Zhu & Hussain, 2025). As OOSBs operate under vastly different conditions than larger firms, existing models of AI awareness may not fully explain how awareness develops within this context.

Rather than assuming that existing AI awareness literature fully captures owner experiences, this study aims to examine whether the current theory adequately explains how OOSBs become aware of AI, interpret its capabilities, and incorporate their awareness into decision making. In the following sections, I review the burgeoning literature on AI awareness and then offer a conceptual map to guide my research. Then, I offer a preliminary methodology: qualitative research employing the tool of analytical induction. Ultimately, the study begins with Zhu and Hussain's (2025) model of AI awareness while remaining open to refinement based on research findings.

Literature Review

AI Awareness

AI awareness has recently emerged as a new construct within broader AI literature. Although research surrounding AI adoption, acceptance, and implementation has expanded rapidly in recent years, less attention has been focused on understanding the awareness factors and implications prior to those behaviors. As the construct is in its infancy, literature around AI awareness contains competing definitions and limited empirical validation.

One of the earliest attempts to conceptualize AI awareness was conducted by Zhu and Hussain (2025), who reviewed 71 peer-reviewed studies using the SPAR-4-SLR protocol. They define awareness as how individuals and entities understand, feel about, and react to AI's functionalities, uses, and broader consequences (Zhu & Hussain, 2025, p. 1). Rather than viewing awareness as simple knowledge of AI, Zhu and Hussain argue that awareness encompasses a tripartite framework consisting of (1) cognitive knowledge, (2) behavioral intention and attitude, and (3) ethical issues that influence how individuals perceive and respond to AI technologies. Currently, these dimensions provide the most comprehensive concept of AI awareness available and serve as the foundation for much of the emerging AI awareness literature.

Although Zhu and Hussain provide a comprehensive conceptualization of AI awareness, the construct definition journey has just begun. Their framework has yet to be empirically examined across different organizational contexts, leaving important questions regarding its applicability outside the environments in which the definition was constructed.

Foundational Theories

Zhu and Hussain's framework of AI awareness draws upon three established foundational theories that collectively explain how individuals understand, evaluate, and respond to emerging technologies. Rather than viewing awareness as a single dimension, Zhu and Hussain (2025) argue that these theories explain behavior within AI awareness.

Cognitive Knowledge and Dual-Process Theory

The cognitive dimension of AI awareness is grounded in Dual-Process Theory (DPT), which proposes that human cognition occurs through two complementary modes of reasoning. Evans and Stanovich (2013) describe System 1 processing as rapid, intuitive, and largely autonomous, while System 2 processing involves deliberate, analytical reasoning that requires conscious cognitive effort and working memory.

Zhu and Hussain (2025) argue that individuals with greater exposure to AI are more likely to engage in deliberate evaluation of AI technologies rather than relying on intuition. In highly technical environments, frequent interaction with AI may normalize the technology, allowing individuals to apply System 2 processing and become more analytical towards AI's capabilities, limitations, and applications. Conversely, those with limited exposure to AI, such as those in rural communities or traditional blue-collar low-technology environments, may rely more on System 1 intuition shaped by uncertainty, media narratives, or fear. Through the lens of DPT, differences in AI awareness may therefore reflect differences in both exposure to AI and the cognitive processes used to interpret that exposure.

Behavioral Intention and Attitude, and the Technology Acceptance Model (TAM)

The behavioral dimension of AI awareness draws upon the Technology Acceptance Model (TAM), which proposes that an individual's willingness to adopt a technology is primarily influenced by perceived usefulness and perceived ease of use (Davis, 1989). TAM has served as one of the most widely applied frameworks for explaining technology adoption and provides an important theoretical foundation for understanding how awareness may influence behavior (Sohn & Kwon, 2020).

Recent AI awareness research suggests that awareness may involve additional behavioral and attitudinal mechanisms beyond those originally proposed by TAM. Zhu and Hussain (2025) showed that increased AI awareness can introduce negative consequences around job insecurity, indicating that greater awareness does not necessarily translate to more favorable attitudes towards AI. Liang et al. (2022) also notes that AI awareness can produce both positive and negative behavioral outcomes: individuals with strong intrinsic motivation may engage in innovative AI behaviors, while those experiencing greater job insecurity may experience emotional exhaustion. Zhou et al. (2024) further found that fear of AI replacement may contribute to negative emotional states and behaviors that diminish individual and organizational performance, such as poorer customer interactions. Meanwhile, due to the rapid diffusion of generative AI, curiosity and experimentation may be occurring prior to an individual's evaluation of AI's usefulness or ease of use (Sohn & Kwon, 2020). Collectively, these findings suggest that AI awareness may shape behavioral intentions through mechanisms that extend beyond the traditional TAM model.

Ethical Issues and Moral Foundations Theory (MFT)

The ethical dimension of AI awareness is supported by Moral Foundations Theory (MFT), which proposes that moral reasoning is shaped by multiple intuitive foundations rather than a single ethical principle. Graham et al. (2012) argue that morality reflects four central propositions: individuals possess innate moral intuitions, these intuitions are influenced by cultural development, intuitive judgements often precede conscious reasoning, and moral decision-making is guided by multiple moral foundations rather than a singular ethical framework.

As individuals become more aware of AI, they also begin evaluating its broader societal implications, including fairness, accountability, privacy, and the balance between human and machine decision-making. Zhu and Hussain (2025) note that approximately 83% of AI awareness studies overlook issues related to algorithmic bias, despite the growing importance of ethical AI practices. OOSBs are not immune to the ethical concerns of AI, as Harris et al. (2025) found that concerns around data privacy frequently discourage AI adoption. At a broader societal level, Zhou et al. (2024) highlight continuing ethical tensions surrounding the appropriate roles of humans and AI within organizations. These tensions have become increasingly relevant as AI agents assume greater levels of autonomy in organizational decision-making. AI awareness encompasses not only an understanding of AI technologies, but also the moral judgements individuals make regarding their AI use.

Current State of AI Awareness Research

The release of ChatGPT in late 2022 dramatically accelerated scholarly interest in AI awareness. Zhu and Hussain (2025) reported that AI awareness publications increased from an average of 4.3 peer-reviewed articles per year prior to ChatGPT's release to approximately 26 articles per year afterward. Yet despite this rapid expansion, much of the literature assumes AI awareness rather than investigating it directly. While AI awareness is frequently treated as an antecedent to AI adoption, trust, and technology acceptance, comparatively few studies seek to explain how AI awareness develops. Zhu and Hussain's (2025) systematic review represents one of the first comprehensive efforts to consolidate and conceptualize the emerging literature, highlighting both the rapid growth of the field and the limited attention devoted to understanding awareness itself.

AI Awareness Varies Across Contexts

Although AI awareness is often discussed broadly, existing research suggests that awareness differs substantially across contexts. Rather than developing uniformly, awareness appears to be influenced by environmental, economic, organizational, and social conditions that shape an individual's exposure to AI technologies.

Goel and Nelson (2025) found that AI awareness searches were significantly higher in wealthier and more urbanized states, while age demonstrated relatively little influence. Similarly, Diversity Institute (2025) reported substantially higher AI adoption rates in urban communities than in rural regions. Together, these findings suggest that access to technology-rich environments may influence how individuals become aware of AI.

Research also suggests that awareness may develop independently of formal organizational AI adoption. Harris et al. (2025) found that although fewer than half of surveyed South African SMEs had integrated AI into their business operations, more than sixty percent of employees reported using generative AI tools such as ChatGPT in their daily work. This finding indicates that awareness may emerge through informal exposure and experimentation in addition to formal organizational initiatives. Mila et al. (2025) claim that AI's low technical adoption barrier plays a role in the informal exposure of current AI tools.

Collectively, these studies suggest that AI awareness is shaped by considerably more than technical knowledge alone. Instead, awareness appears to vary according to differences in exposure, organizational context, and the environments in which individuals encounter AI technologies.

Current Limitations of AI Awareness Research

Despite the rapid growth of AI awareness research, important limitations remain. Zhu and Hussain (2025) found that AI awareness research has been concentrated primarily within business, engineering, and computer science disciplines, with comparatively limited contributions from psychology, the arts and humanities, and environmental sciences. They further identified significant ethical shortcomings within the literature, reporting that 83% of studies fail to consider algorithmic bias despite increasing public concern regarding responsible AI (Zhu & Hussain, 2025, p. 11).

Beyond disciplinary limitations, the literature also reflects what Zhu and Hussain (2025) describe as a techno-deterministic bias, where AI is frequently treated as an inherently beneficial technology and research focuses primarily on adoption outcomes rather than how individuals cognitively and emotionally make sense of AI. Consequently, much of the existing literature emphasizes whether AI should be adopted while devoting comparatively less attention to understanding how AI awareness develops.

Perhaps the most significant limitation, however, is the context in which AI awareness has been studied. Existing research has focused predominantly on employees, consumers, students, and organizations broadly, while owner-operated businesses have received comparatively little attention. Given the unique decision-making responsibilities, resource constraints, and organizational characteristics of OOSBs, it remains unclear whether existing conceptualizations of AI awareness adequately explain how awareness develops within this context. This limitation provides the foundation for the present study.

Owner-operated Small Businesses

Owner-operated businesses differ fundamentally from larger organizations in how strategic and technological decisions are made (Thong, 1999). Whereas large organizations often distribute decision-making across specialized departments and functional experts, owner-operated businesses frequently rely on a single individual to simultaneously serve as strategist, operator, marketer, financial manager, salesperson, and technology decision maker (Bollweg et al., 2021; Thong, 1999). Consequently, decisions regarding emerging technologies such as artificial intelligence are shaped by organizational conditions that differ substantially from those found in larger firms.

Thong (1999) argued that technology adoption within small businesses cannot be understood simply as a scaled-down version of large organizations. The study found that owner characteristics, organizational resources, and environmental conditions exert a substantially greater influence over technology-related decisions because authority is concentrated in a single decision maker. Unlike larger organizations that can distribute technology evaluation across specialized personnel and dedicated resources, owner-operated businesses must prioritize emerging technologies alongside competing operational demands and broader business needs (Bollweg et al., 2021; Kozlov, 2026).

These tensions extend beyond financial resources. Oldemeyer et al. (2025) found that limited technical knowledge, digital maturity, organizational capabilities, and internal resources act as the primary barriers to AI implementation within small and medium-sized enterprises. Within owner-operated businesses, these limitations are magnified as technology-related decision authority is often concentrated in the owner, who must evaluate emerging technologies alongside ongoing operational and strategic responsibilities (Thong, 1999). Unlike larger organizations, where technology evaluation may be distributed across multiple functions, owners frequently balance AI decisions against the immediate demands of serving customers, managing employees, and operating the business (Bollweg et al., 2021). Therefore, the time required to learn about, evaluate, and experiment with AI competes directly with day-to-day priorities. Even when AI tools and learnings are inexpensive and readily accessible, the organizational capacity required to evaluate and integrate AI may remain a significant barrier to engagement.

Recent research suggests that these challenges continue to shape AI adoption within small businesses. Harris et al. (2025) found that owner-operated businesses frequently encounter barriers including limited technical expertise, financial constraints, infrastructure limitations, and uncertainty regarding AI implementation. Despite these challenges, the authors also found that the majority of owners and employees reported using generative AI tools informally within their daily work, suggesting that AI engagement may occur outside formal organizational implementation efforts.

Collectively, the owner-operated business literature suggests that these organizations represent a distinct decision-making environment in which technology awareness and adoption occur under fundamentally different organizational conditions than those found in larger firms. Yet while previous research has examined technology adoption within owner-operated businesses, comparatively little attention has been devoted to understanding the awareness that precedes those decisions. Consequently, owner-operated businesses provide an important context for examining whether existing conceptualizations of AI awareness adequately explain how business owners become aware of, interpret, and evaluate emerging AI technologies. Thus, the research question that emerges:

Conceptual Foundation

A Developmental Perspective of AI Awareness

Existing literature establishes AI awareness as a single step prior to AI decision making. Zhu and Hussain (2025) define AI awareness through cognitive knowledge, behavioral intention and attitude, and ethical issues, providing this study's dominant framework towards understanding the construct. While this framework explains the working dimensions of AI awareness, even less attention has been devoted to understanding how awareness develops or whether the construct evolves throughout one's AI journey.

Several complementary theories provide reason to consider a developmental perspective of AI awareness. Rogers (2003) suggests that awareness of innovations begins through communication within social systems. Bandura (1977) theorizes that individuals often learn by observing prior to engaging in behavior themselves. Weick et al. (2005) proposes that understanding develops through action and interpretation. Combining the modern AI awareness definition with these foundational perspectives suggests that AI awareness may begin through social exposure and continue to develop through experience and sensemaking.

Owner-operated Small Business Context

Owner-operated small businesses provide a unique context for examining AI awareness because decisions are concentrated with a single individual. Unlike larger organizations that distribute responsibilities across specialized departments, owners frequently serve simultaneously as strategist, operator, marketer, financial manager, and technology decision maker (Thong, 1999).

These conditions differ substantially from larger organizations, where employees often have greater access to organizational technology resources, formal learning opportunities, and dedicated technology specialists (Bollweg et al., 2021; Harris et al., 2025). Instead, owners must independently evaluate AI while balancing operational demands, financial constraints, customer expectations, and long-term business objectives.

Business decisions are also deeply personal. Entrepreneurial identity frequently becomes intertwined with business ownership, meaning technology decisions may affect not only business performance but also owners' livelihoods, family responsibilities, and professional identities (Shepherd & Haynie, 2009). Initial AI awareness is likely to emerge through informal sources, including competitors, customers, professional networks, family members, and media. Collectively, these characteristics make OOSBs an appropriate context for examining how AI awareness develops and manifests.

Initial Conceptual Perspective

The literature reviewed throughout this proposal provides several complementary perspectives for understanding AI awareness. Rogers (2003) explains how awareness of innovation spreads through social systems. Bandura (1977) describes learning through observations. Weick et al. (2005) suggests that understanding may continue developing through action and interpretation. Taken together, these findings provide an initial lens through which AI awareness should be examined within OOSBs. Rather than taking Zhu and Hussain's (2025) pre-behavior definition at face value, this study considers whether AI awareness may emerge through social exposure, experimentation, and ongoing interpretation. Consistent with analytic induction, this perspective serves as an initial explanation to be examined and refined through empirical inquiry directed towards OOSBs.

Contextual Influences

Although this study sees AI awareness as a potentially developmental phenomenon, development is unlikely to occur uniformly across OOSBs. Existing literature suggests that the environment in which owners operate, the characteristics of their businesses, and their unique individual experiences may all shape how AI awareness develops and manifests.

External environmental factors may influence how owners first encounter AI technologies. Rogers (2003) argues that innovations spread through communication within social systems, suggesting that awareness often begins through customer interactions, competitors, professional networks, and media, rather than through deliberate organizational initiatives. Geographic location and regional exposure may also influence awareness. Goel and Nelson (2025) found that AI awareness was greater in wealthier and more urbanized states, suggesting that owners operating in different environments may experience substantially different opportunities for AI exposure.

Business characteristics may also shape the development of AI awareness. Technology decisions within OOSBs are influenced by organizational resources, operational demands, and business needs (Thong, 1999). Likewise, Bollweg et al. (2021) found that organizational and environmental conditions influence the use of digital technologies within OOSBs. Seemingly, differences in industry, digital dependence, organizational maturity, and available resources may influence not only opportunities to experiment with AI, but the relevance of AI within their business.

Owner characteristics may also influence how AI awareness develops following initial exposure. Bandura's (1977) social learning theory suggests that individuals acquire knowledge through observing others. This implies that professional networks, competitors, family members, and peers may all contribute to AI awareness. An owner's previous technology experience, willingness to learn, confidence with technologies, and available time may further influence how they interpret perceptions around AI. Entrepreneurial identity may also influence how owners interpret emerging technologies. Shepherd and Haynie (2009) suggest that entrepreneurs' identities become closely intertwined with their businesses, potentially shaping how AI is perceived and evaluated.

These contextual influences are presented to provide an initial framework for understanding why AI awareness may develop and manifest differently across owner-operated businesses. Consistent with analytic induction, the study remains open to identifying additional contextual influences that emerge through participant experiences.

Closing Conceptual Statement

The explored literature establishes AI awareness as an emerging construct while diffusion of innovations, social learning theory, and organizational sensemaking suggest that awareness may extend beyond initial exposure to AI. Within OOSBs, single individuals experience awareness, interpretation, and decision making, thus providing an appropriate research setting. This study seeks to explore how AI awareness develops and manifests within OOSBs and whether existing theories adequately explain owner experience, or whether AI awareness within OOSBs requires unique explanation.

Research Methodology

As AI awareness remains a relatively new construct and has yet to be empirically studied within local OOSBs, a qualitative research design has been chosen to explore owner experiences prior to the development of hypotheses or quantitative measures. Instead of measuring AI awareness using predetermined and non-validated variables, this study aims to understand how owners experience, interpret, and describe AI awareness within their businesses.

As previously mentioned, this study will employ analytic induction as its primary research approach. Analytic induction begins with an initial theoretical explanation and is continuously compared against empirical evaluation, allowing for constant refinement (Ragin, 1994). Rather than seeking to verify a predetermined model, the approach remains open to modifying existing dimensions of AI awareness as new evidence emerges (Timmermans & Tavory, 2012). By using analytical induction, this study uses Zhu and Hussain's (2025) definition of AI awareness to serve as a core theoretical perspective, while remaining open to refinement based upon owner empirical gatherings.

Participants and Data Collection

Participants will consist of owner-operated small businesses employing fewer than twenty employees. Eligible participants will be owners who maintain primary responsibility for strategic business decisions while operating the business as their primary source of income. Businesses may operate through physical locations, online, or hybrid, provided that the owner retains primary decision-making authority.

Participants will be recruited using purposive sampling through professional networks, digital outreach, cold outreach, and participant referrals. The goal is to recruit owners that represent a variety of industries, business models, geographic locations, and various levels of AI experience in order to maximize variation across the study. Recruitment will continue until additional interviews no longer contribute meaningful conceptual refinement of the developing explanation within the studied context, and theoretical saturation is reached (Ragin, 1994).

Data will be collected through semi-structured interviews lasting approximately one hour. Interviews performed online will be audio and video recorded, while in-person interviews will be audio recorded. Participant consent of recording will be gathered and recordings will be transcribed using modern transcription software. Transcripts will be reviewed for accuracy prior to analysis and identification removed to protect participant confidentiality.

Data Analysis

Each interview will be examined both individually and collectively. Initial coding will begin using Zhu and Hussain's (2025) dimensions of AI awareness as sensitizing concepts while remaining open to new concepts emerging from participant experiences (Thomas, 2006). Throughout the analysis, evidence that challenges the concept of AI awareness will receive particular attention, allowing an abduction process able to produce new theories and hypotheses (Ragin, 1994; Timmermans & Tavory, 2012).

Following first-cycle coding, memos will be developed to summarize each interview, document emerging concepts, and identify evidence that supports, extends, or challenges the developing explanation of AI awareness.

Cases will then be continuously compared to one another to identify recurring patterns, contextual differences, and alternative explanations. Throughout the analysis, contextual factors identified within the conceptual foundation, including external environment, business characteristics, and owner characteristics, will serve as initial areas of attention while remaining open to revision or expansion as additional concepts emerge. The final explanation will then emerge through iterative comparison and refinement.

Research Quality and IRB Considerations

Several procedures will be employed to enhance the trustworthiness of the study. An audit trail documenting coding decisions, analytic memos, conceptual revisions, and sampling decisions will be maintained throughout the research process. Reflexive journaling will also be used following each interview to document researcher observations, assumptions, and potential sources of bias, particularly given the researcher's previous experience as the owner of a small business and current experience within the technical AI field. Together, providing transparency throughout the development of the study strengthens the trustworthiness of the analytical process (Nowell et al., 2017). To further enhance credibility, selected transcripts and coding decisions will be reviewed through peer debriefing to strengthen interpretive rigor and challenge emerging interpretations (Nowell et al., 2017).

Because the study involves interviews with human participants, Institutional Review Board (IRB) review will be completed prior to participant recruitment. The researcher will seek the appropriate level of IRB review, including exemption if applicable. Participation will be voluntary, informed consent will be obtained from all participants, and interview recordings and transcripts will be securely stored and de-identified to protect participant confidentiality.

Expected Research Contributions

This study is expected to contribute to both AI awareness literature and the broader understanding of technology adoption within owner-operated small businesses. From a theoretical perspective, the study seeks to extend the emerging AI awareness literature by examining how AI awareness develops and manifests within a context that has received comparatively little scholarly attention. By exploring owner-operated businesses through analytic induction, the study may provide insight into whether existing conceptualizations of AI awareness adequately explain owner experiences or whether contextual refinements are warranted.

From a practical perspective, the findings may help owner-operated businesses better understand their own AI awareness and identify potential blind spots as they evaluate emerging AI technologies. Consultants, AI educators, and software providers may also benefit from a richer understanding of how owners become aware of, interpret, and evaluate AI, allowing AI education, implementation strategies, and communication to better align with the realities of owner-operated businesses. The findings may also encourage practitioners to treat AI awareness as a developmental process that precedes AI capability. Rather than assuming organizations require additional AI tools or technical training, the findings may suggest that increasing awareness represents an important first step toward effective AI adoption.

Finally, this study is expected to provide a foundation for future research. By exploring AI awareness qualitatively before attempting quantitative measurement, the study may support the future development of context-specific measurement instruments, hypothesis testing, longitudinal studies, intervention research, and comparisons across industries, geographic regions, organizational contexts, and countries.

Declaration of Generative AI in the Writing Process

During the preparation of this work, I used Google Gemini to identify additional scholarly literature related to AI awareness and owner-operated small businesses. I used Google NotebookLM to organize the literature, summarize sources, and develop study materials to support the literature review. I also used ChatGPT to assist with improving the clarity, organization, readability, and flow of the manuscript, as well as to provide feedback on the logical organization of ideas. Following the use of these tools, I reviewed and edited the content as needed and take full responsibility for the content of the manuscript.