The implications of generative artificial intelligence in academic research and higher education in tourism and hospitality

Abstract

The purpose of this study is to critically review the effect of generative artificial intelligence (GAI) tools on higher education and research in the tourism and hospitality (TH) field. This manuscript identifies capabilities and implications of these GAI applications through a theoretical lens. GAI adoption in TH education can facilitate personalized learning experiences, enhance the technological competence of students, and foster a more diverse and inclusive learning environment. For academic research, GAI-enabled technologies may revolutionize data collection, analysis, and writing in a myriad of ways. However, there are multiple ethical and legal concerns associated with adoption that must be considered. At the end of this paper, we propose ten discussion questions aimed at stimulating conversation about adoption of GAI tools in TH education and research.

Keywords

education ethics generative artificial intelligence publishing research

Introduction

Generative artificial intelligence (GAI) has recently been the subject of much academic inquiry. Unlike traditional artificial intelligence approaches, GAI harnesses the power of deep learning algorithms of natural language processing and neural networks to autonomously generate original output (Koleva, 2023; Ozdemir et al., 2023). Accordingly, GAI is used to simulate human-like intelligence and creativity to produce a wide range of original content including images, text, audio, video, and computer code.

Because of its creative abilities, GAI is considered to be a disruptive innovation in many areas, from business to education (Lee et al., 2023). According to disruptive innovation theory, disruptive technology is defined as any technology that alters the fundamental elements of competition by modifying the criteria for measuring performance (Bower and Christenson, 1995). As a disruptor, GAI introduces both opportunities and challenges in the field of tourism and hospitality (TH) and beyond (Dogru et al., 2023; Dwivedi et al., 2023).

Previous research on the implications of GAI as a disruptor in the TH space has often focused on practical management (e.g., Dogru et al., 2023). However, in addition to affecting the business of tourism and hospitality, GAI technology has already altered conventional approaches to higher education, research, and teaching in several important ways. As just one example, ChatGPT’s ability to write summaries and essays and even complete theses (Ivanov and Soliman, 2023) has challenged conventional scholarly research endeavors and higher education practices in the TH domain.

Considering its potential to enhance academic creativity, the emergence of GAI-enabled technology signals the dawn of a new era in academia, empowering both researchers and students to explore uncharted territories. However, while there are many beneficial applications of GAI in higher education, the use of GAI also brings new challenges. Among them, ethical and legal issues pertaining to privacy, misinformation, and biases (Dwivedi et al., 2023; Mhlanga, 2023; Zhai, 2022) present unique problems that must be solved. Accordingly, the challenge for higher education in this new era will be to harness the transformative power of GAI while simultaneously minimizing the potential negative effects that can be brought about by the misuse of technology.

The purpose of this paper is to assist hospitality educators, researchers, and administrators in meeting this challenge. In pursuit of this goal, this study discusses the implications of GAI technology in the field of tourism and hospitality education and research. Based on the framework of disruptive innovation theory, this study presents a detailed account of the applications of GAI in education and academic research, then discusses the ethical and legal concerns associated with the use of GAI in higher education. Based on this discussion, the paper concludes with a set of discussion questions that represent some of the most pressing issues faced in the contemporary educational environment, in the hopes that these questions may stimulate conversations on this topic among TH researchers and educators.

Disruptive innovation theory and GAI

Disruptive technology refers to “a technology that changes the bases of competition by changing the performance metrics along which firms compete” (Lee et al., 2023, 820). Innovations rapidly transform industries, and similarly, the theory of disruptive innovation is constantly advancing. Christensen et al. (2018) broadly describe disruptive innovation as a competitive reaction to fresh innovation. Moreover, if an emerging technology appears sustainable, new innovative challenges will seek to displace it. When disruptive innovation emerges, newcomers ensure its continued momentum (Denning, 2016). Disruptive technologies are those that can instigate significant shifts, causing unparalleled effects both on large societal scales and at individual levels, leading to upheavals in industry structures and spawning a wide range of service innovations. This concept has traditionally been studied in the context of business management. From a company's viewpoint, disruptive technologies, such as blockchain technology, metaverse, AI or GAI enhance business management, offering improved verification, knowledge handling, innovation potential, streamlined work procedures, and increased revenue avenues (Kizildag et al., 2019; Önder and Gunter, 2022). For consumers, technological advancements elevate their experiences by enabling them to check online reviews, assess consumer contentment or grievances, gauge technology adoption, experience real-time interactions, preview hotels prior to visiting, and refine their observational abilities as tourists.

However, the same concepts hold true when we look at the disruptive effects of GAI on TH teaching and research. In this paper, we examine how GAI is poised to impact TH students, professors, and researchers in higher education.

Applications of GAI in higher education

The use of GAI has the potential to greatly influence nearly every facet of TH education and research. Although the full extent of its capabilities remains unknown, there are numerous potential applications of GAI across the TH academic landscape. These applications have the potential to create value for all stakeholders involved, including but not limited to scholars, educators, students, administrators, and publishers. As follows, the implications of GAI for each of these stakeholders are considered.

Education and teaching

Personalized learning

In the context of higher education and teaching, GAI can enhance the quality of the student experience by providing a more personalized learning experience. For example, GAI-enabled chatbots can answer students' questions and provide personalized support, adapting to individual student needs, abilities, and learning styles (Ouyang et al., 2022). This personalization can improve student engagement, motivation, and academic performance. GAI-powered chatbots, adaptive learning platforms, and virtual reality experiences can enrich the educational experience by providing personalized support, engaging students, and simulating real-world situations. Enhancing teaching via GAI-enabled chatbots and simulations likewise broadens teaching spaces and allows for modalities to adapt to evolving learning dynamics and varying student abilities (Iskender, 2023).

Online learning

The widespread adoption of online learning in higher education has paved the way for GAI to revolutionize online instructional methods and learning experiences. For example, GAI has been used to accomplish a wide variety of online classroom outcomes, including enhancing students’ wellbeing, improving elastic teaching and learning methods, and promoting discovery through the use of virtual reality (Dwivedi et al., 2023; Ivanov and Soliman, 2023). Likewise, GAI can deliver pre-recorded solutions to students in online spaces without a presence of an instructor. In other words, it provides offline learning experiences in online environments.

Career preparation

The use of GAI in the classroom can also prepare students for an increasingly technology-driven tourism industry. In the future, hospitality professionals at any level will be expected to be knowledgeable about how to apply technology to provide hospitality services. In the TH industry, firms are expected to adopt GAI as a means of reducing labor costs, and many of the tasks that are currently completed by humans will be assigned to GAI. Thus, future hospitality managers will need to understand how to supervise and manage blended teams made up of both humans and GAI-driven platforms. Creating teaching environments that prepare students for managing “blended” teams will be essential to meeting the needs of the future TH industry (Dwivedi et al., 2023; Iskender, 2023).

To ensure that students are prepared for blended environments, GAI will need to be embedded into curricula at all levels. For example, management courses may need to shift to an emphasis on how to manage teams that include significant GAI contributors, while marketing classes will need to include discussions focused on how to facilitate appropriate GAI interactions with guests. In courses that feature accounting and finance content, producing and interpreting financial statements can also be enhanced using GAI tools. As another example, GAI can be used to assess economic strategies and examine the validity of financial models, assisting students in the evaluation of the financial health of a company. In accounting courses, GAI can be used to enhance students’ understanding of forecasting techniques, enabling them to generate data to estimate future business trends (Ivanov and Soliman, 2023). GAI can also be used in courses focused on inventory management to enhance students’ understanding of supply-demand planning and sales forecast calculations.

Academic research

Data collection

In addition to affecting classroom dynamics, GAI has the ability to significantly disrupt TH research systems. GAI-powered research tools offer TH scholars opportunities to collect, organize, and analyze vast volumes of data. For example, at the pre-analysis stage of research, GAI can be used to suggest the most convenient data collection methods to fit the research goal (Ampountolas et al., 2023). Then, at the organizational level, GAI can be used to assist researchers by cleaning (i.e., detecting and managing inconsistencies, values, and outliers), clustering, sorting, and organizing data sets.

Data analysis

At the analytical level, researchers can use GAI to directly analyze data. GAI-enabled analytical tools have the capacity to process vast amounts of data quickly and accurately, enabling researchers to gain new insights and quickly identify complicated patterns. This enhanced efficiency can accelerate the research process, reduce costs, and allow researchers to focus on more complex questions or innovative approaches. Accordingly, such GAI-powered tools can help researchers forecast trends, assess the impact of policies or marketing campaigns, and model potential scenarios, leading to more informed decision-making and strategic planning in the TH sector. For example, Xu et al. (2021) emphasize GAI's potential to revolutionize research by offering novel perspectives on established topics, arguing that GAI's ability to analyze high-throughput data, categorize information, and make predictions can significantly enhance research efficiency and foster evidence-based decision-making.

GAI's analytical applications are particularly well-suited to problem-solving, optimization, and modeling tasks, enabling researchers to generate more accurate and efficient computations. By automating data acquisition, optimizing resources in research laboratories, and facilitating the synthesis and analysis of complex datasets, GAI can unlock new possibilities for understanding and predicting tourist behavior, preferences, and trends. For example, GAI can quickly process extensive textual data from diverse sources such as news articles, financial reports, and social media. Smart algorithms can then be used to uncover pertinent information, gauge sentiment, and discern emerging trends from these data. These insights can, in turn, shape marketing strategies and inform tailored offerings for tourists. Consequently, these applications may lead to more informed decision-making and drive innovation in the industry. Additionally, when performing secondary data analysis, GAI can be used to identify whether similar data were used in preceding studies and, if so, to compare findings. Such applications would also help to avoid research repetition and resource waste associated with the research, such as time and funding.

Ethical and legal considerations

Biases, privacy, and transparency

Despite its potential merits, however, the application of GAI in the TH research field is not without challenges and ethical considerations. Zhai (2022) notes several critical ethical issues that need to be addressed. First, GAI may reflect biases present in the data it was trained on, potentially leading to unfair treatment based on gender, race, ethnicity, or disability. Privacy is another issue, as the collection and processing of data for personalized responses can pose risks of data leaks and exposure to personal information. Finally, the lack of transparency regarding the sources of information generated by GAI creates doubts about the reliability of its output, making it difficult to assess bias and/or the rationale behind recommendations. Navigating these issues is crucial in ensuring responsible and balanced integration of GAI in educational environments (Dwivedi et al., 2023; Ivanov and Soliman, 2023; Lund and Wang, 2023; Mhlanga, 2023; Mondal et al., 2023; Zhai, 2022).

Authorship and intellectual property

The use of GAI to create academic content also challenges traditional notions of authorship and intellectual property. Because GAI tools can produce content based on existing information, it becomes difficult to determine the originality of an idea or concept. This raises questions about who should be credited for the work: the GAI tool, the person who provided input to the tool, or the individual(s) whose work inspired the GAI-generated content.

Additionally, as GAI-generated content becomes more sophisticated, it is becoming more challenging to detect plagiarism using traditional methods. Traditional plagiarism detection software may not be able to recognize GAI-generated content as plagiarized because it can produce unique text based on the input data. This could lead to a rise in undetected plagiarism, which may undermine the integrity of academic work and the value of academic degrees.

To address this issue, proper attribution is essential. Academic institutions and publishers should establish guidelines for citing GAI-generated content to ensure that original authors receive appropriate credit for their work. Furthermore, users of GAI tools should be transparent about their use in the writing process, including a clear indication of which parts were generated by GAI and which were written by a human author. Similarly, to preserve the integrity of academic work, educational institutions should establish clear policies regarding the use of GAI tools in academic writing. These policies should outline the acceptable use of GAI-generated content, such as for drafting, brainstorming, or proofreading, while prohibiting its use for generating entire assignments or significant portions of research papers.

Legal concerns

In addition to ethical concerns, the proliferation of GAI in higher education and academic research has resulted in legal concerns. Complex issues concerning intellectual property rights have come to the forefront of the legal discussion. The primary issue centers on determining who owns the copyright for content (text, audio, video, code, etc.) created by the algorithm. Currently, copyright laws mainly recognize humans as the rightful owners. However, whether programmers or algorithms should be considered authors and copyright holders remains unresolved due to the lack of specific legislation.

At the academic level, these copyright issues necessitate a careful consideration of the relationship between professors and universities and intellectual property rights. Unauthorized use of copyrighted materials during GAI training can lead to copyright infringement and potential legal consequences. To minimize these risks, obtaining proper permissions or working with open-source and public-domain materials is crucial. A comprehensive understanding of these issues is vital for all stakeholders integrating GAI into their creative work, but it is especially relevant to researchers who seek to effectively navigate the changing legal landscape (Agostino et al., 2021; Samuelson, 2023).

In light of these ethical and legal issues, it is important to carefully consider the implications and limitations of using GAI in hospitality and tourism education. It is also essential to note that ethical use of GAI is not only the responsibility of the developers and policymakers. A range of stakeholders, including industry leaders, managers, users, and customers, should collaborate to develop guidelines and regulations for deploying ethical GAI (Dwivedi et al., 2023). Educators and administrators also have a critical role to play, as they need proper training to utilize GAI in an ethical and responsible manner (Zhai, 2022). Ultimately, a collaborative effort is required to ensure that GAI is deployed fairly, justly, and beneficially for all stakeholders involved.

Discussion and conclusions

GAI is poised to revolutionize higher education, research, teaching, and academic publications. Its impact on the tourism and hospitality industry and beyond is already noticeable, with tools like ChatGPT being integrated into educational institutions for tasks such as writing essays and providing personalized feedback to students. However, while GAI presents exciting opportunities, it raises important concerns about ethics, legality, privacy, and bias, which must be carefully addressed. Achieving a balance between harnessing the transformative potential of GAI and ensuring responsible and fair usage is essential for shaping the future of knowledge creation and dissemination. Thus, it is crucial to evaluate the implications of implementing GAI in academic settings and to thoroughly examine the associated challenges and opportunities.

On the positive side, GAI tools, such as ChatGPT, can facilitate learning by responding to individual students' needs and learning styles. This personalized approach enhances engagement, motivation, and academic performance and reinvents teaching methodologies in TH education. GAI can also offer comprehensively integrated curricula across disciplines, fostering skills to manage hybrid teams, facilitate GAI interactions, and harness AI tools for enhanced decision-making. In parallel, GAI has a huge impact on the research community, enhancing such things as data collection, organization, and analysis.

Along with positive opportunities, however, integrating GAI into higher education and teaching presents ethical challenges, as well. GAI’s ability to exacerbate biases and violate privacy demands careful consideration. Moreover, GAI-generated content disrupts conventional notions of authorship and intellectual property, posing questions about credit and plagiarism detection. Legal complexities concerning copyright ownership further complicate the situation. Addressing these challenges requires transparent attribution, clear usage policies, and a clear understanding of the legal implications. Effective and collaborative efforts among stakeholders, including educators and industry leaders, are fundamental to maintaining ethical and responsible GAI integration for TH education.

In short, GAI-enabled technology holds the promise of an era of enhanced academic exploration for researchers and students in the rapidly evolving academic realm. A variety of challenges, however, impede the potential for transformation. It is imperative that higher education maximizes GAI's constructive potential while vigilantly minimizing any adverse consequences resulting from its misuse. This study serves as a helpful guide for hospitality educators, researchers, and administrators in this endeavor.

Implications

While GAI applications are still in their formative stages (Dwivedi et al., 2023), they are expected to revolutionize research and education in the tourism and hospitality fields. This paper serves as a call for uniform structural changes across all educational disciplines including tourism and hospitality. Iskender et al. (2022) suggest the need to restructure TH curricula across three pillars. Specifically, TH programs should seek to: (i) educate students as technologists who know how to apply technology for hospitality services; (ii) help students develop a mindset that technology will not replace them, but rather assist them in doing their jobs better; and (iii) emphasize the importance of developing soft skills (empathy, compassion, reception, etc.) in an accelerated tech-savvy world. Students who can utilize these tools will have a competitive advantage in the job market. The implementation of these pillars will change the nature of both on ground and online learning at fundamental levels (Mollick, 2023).

For research, it is suggested that GAI may be utilized by authors for certain tasks such as data collection, analysis, and reference management, as long as it is disclosed in the acknowledgment section of a manuscript (Ivanov and Soliman, 2023). Microsoft 365 Copilot has already integrated the GAI language models into office programs. These applications may help researchers to be more efficient and use their resources (e.g., time and mental energy) to produce manuscripts. However, ChatGPT or other GAI language models are not suggested as coauthors since these applications cannot be accountable, and at the current time are not able to think creatively or conceptually.

Future research directions

Similar to all revolutionary technologies, GAI presents ethical and privacy limitations and potential capabilities that demand extensive reflection (Dogru et al., 2023). Failing to approach GAI with prudence could have detrimental consequences for educators, researchers, and students in the TH domain. Hence, a wide range of questions are still open, calling for further investigation to identify GAI's potential for providing value for the TH education industry.

Discussion questions

A protocol should be developed with consensus to determine how to utilize GAI tools in education and research, while still considering ethical concerns. Therefore, we propose ten relevant questions (“The Big Ten”) that we hope will serve as a springboard to stimulate conversations in the academy on this important and timely issue.

Overarching questions

DQ1: What are the capabilities and limitations of GAI tools in producing outputs that resemble human intelligence?

DQ2: How does the heavy reliance on GAI tools impede critical thinking in research and education in TH context?

DQ3: What are the best practices for utilizing GAI tools in TH education and research to ensuring maximum benefits without compromising the core purposes of education and research?

Questions for education and teaching

DQ4: How can we implement structural modifications in TH education to take advantage of GAI tools while maintaining academic integrity?

DQ5: What are the ethical and academic integrity implications of using GAI tools extensively in education?

DQ6: How does the integration of GAI tools in classrooms impact the learning environment and teaching methods in TH context?

DQ7: In what capacity can adoption of GAI applications in hospitality and tourism education play a role to achieve diversity and inclusion?

Questions for research

DQ8: How can researchers ethically and legally use GAI tools in their research endeavors?

DQ 9: How do we, as researchers, design a protocol that determines the acceptance and proper use of GAI tools in academic research?

DQ10: How do we maintain an environment of critical thinking, creativity, and conceptual innovation in research as GAI proliferates the research milieu?

Footnotes

Acknowledgment

The leading four authors reviewed, sorted, aggregated, and interpreted the contribution of the 13 other authors, extended the content, and developed the manuscript. The rest of the 13 authors made equal contributions to this study and their order of authorship is in alphabetical order based on last name following the leading four authors. We would like to thank Tourism Economics editors Albert Assaf and Raffaele Scuderi for their valuable feedback, guidance, and contribution.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Tarik Dogru

Murat Kizildag

Irem Onder

Ozgur Ozdemir

Author biographies

Tarik Dogru is an associate professor of hospitality management in the Dedman College of Hospitality at Florida State University. Prior to joining Florida State University, Dr. True worked at Boston University as an assistant professor for 2 years. Dr. True’s research interests span topics in sharing economy (e.g., Airbnb), hospitality finance, corporate finance, tourism economics, employment and wages, climate change, and blockchain technology.

Nathan Line is the Cecil B. Day Distinguished Professor in the Dedman College of Hospitality at Florida State University. Dr. Line’s research interest is hospitality and tourism consumer behavior. His primary streams of interest include EWOM behavior and consumer reactions to the social servicescape.

Lydia Hanks is the J.W Marriott Sr Professor at The Dedman College of Hospitality Management at Florida State University. Her research interests include the social servicescape, corporate social responsibility, service failure and recovery, and consumer behavior. Dr. Hanks earned her Ph.D. in Hospitality Management from Penn State in 2012. She also holds an MBA from the University of Denver and a B. S. from the University of Louisiana.

Fulya Acikgoz is a Ph.D. candidate at University of Bristol, UK. Her research includes but is not limited to technology marketing, information management, and social media. Her research has been published in journals including Journal of Business Research, Psychology & Marketing, Technovation, International Journal of Consumer Studies, International Journal of Contemporary Hospitality Management, Journal of Business & Industrial Marketing, Behavior & Information Technology, International Journal of Human-Computer Interaction, International Journal of Technology Marketing.

Je’Anna Abbott holds the Spec’s Charitable Foundation Endowed Professorship in Social Responsibility at the University of Houston. She is a professor at the Hilton College of Global Hospitality Leadership, the C.T. Bauer College of Business, the University of Houston Law Center, and Texas A&M Mays College of Business. She teaches hospitality business law, conflict management, negotiations, and digital strategy. She has written multiple refereed articles in these areas. She has advanced degrees from the University of Houston, Texas A&M University, University of Chicago, Wharton, and Harvard University.

Selim Bakir is a Ph.D. Student at Auburn University's Horst Schulze School of Hospitality Management. He received his M.Sc in Hospitality Management from Auburn University. He also holds an MBA from Istanbul Gelisim University. Before joining Auburn University, he worked in the hotel and tourism industries for about two decades. His research interests include hospitality and tourism employee behavior, emphasizing a better understanding of the effect of AI and robotics.

Adiyukh Berbekova is an Assistant Professor at the Travel Industry Management School, Shidler College of Business, University of Hawai'i at Mānoa. Her research interests include crisis management, quality of life in tourism and hospitality, and destination performance.

Anil Bilgihan is an associate professor at Florida Atlantic University's College of Business and a Fulbright senior scholar. Specializing in e-commerce, digital marketing, and Hospitality Information Systems, he has over 100 peer-reviewed publications. Dr. Bilgihan has taught internationally in Spain, France, Mexico, and Turkey. He holds editorial roles in Psychology & Marketing and the Journal of International Hospitality Management. His accolades include the FAU Scholar of the Year and Cisco Extensive Research Award.

Ali Iskender is an assistant professor in the School of Marketing, Entrepreneurship, Sport Management, and Hospitality and Tourism Management at Western Carolina University.

Murat Kizildag is an associate professor in UCF’s Rosen College of Hospitality Management. Dr. Kizildag is intrigued by the depth of financial research. His research expertise spans the areas of Security Valuation, Risk & Investment Analytics, Financial Modeling & Forecasting, Portfolio Management & Optimization, and Time-Series Analysis. Dr. Kizildag produced more than 50 academic articles and attended numerous academic and industry conferences. His recent articles appeared in business finance and hospitality business/management journals. Further, Dr. Kizildag worked on several different grant projects and contracts.

Minwoo Lee, is an Associate Professor at Conrad N. Hilton College, University of Houston.

Woojin Lee, is an Associate Professor at the School of Community Resources and Development.

Sean McGinley is an associate professor at the Florida State University's Dedman College of Hospitality. He earned a bachelor's, master's, and doctorate degree from the School of Hospitality Management at the Pennsylvania State University. His research interests include career change, turnover, organizational recruitment, job applicant attraction, and career development in the hotel industry.

Makarand Mody, an Associate Professor of Hospitality Marketing at Boston University’s School of Hospitality Administration, graduated with his Ph.D. from Purdue University. He received his M.Sc in Human Resource Management for Tourism and Hospitality from the University of Strathclyde, and a Higher Diploma in Hospitality Management from IMI University Centre, Switzerland. Dr. Mody has worked in the hotel and airlines industries in the areas of learning and development and quality control. His research focuses on issues pertaining to the supply and demand of responsible tourism, the sharing economy, and the modeling of consumer behavioral pathways.

Irem Onder is an Associate Professor and Ph.D. coordinator at the Department of Hospitality and Tourism Management at University of Massachusetts Amherst. She obtained her PhD from Clemson University, South Carolina and master’s degree in Information Systems Management from Ferris State University, Michigan. Her main research interests include information technology and tourism economics, specifically big data analysis, smart destinations, decision support systems, blockchain, and tourism demand forecasting. She serves in the editorial boards of Journal of Travel Research, Tourism Management, Tourism Economics, and Journal of Information Technology and Tourism.

Ozgur Ozdemir, is an associate professor in the William F. Harrah College of Hospitality at University of Nevada Las Vegas. He obtained his PhD in Hospitality Management with a concentration in financial management from the Pennsylvania State University. Dr. Ozdemir's main research areas include corporate finance, capital markets, corporate governance and operations management. His work has been published in prestigious journals including Tourism Management, International Journal of Hospitality Management, International Journal of Contemporary Hospitality Management, Journal of Hospitality & Tourism Research, Hospitality Financial Management, and International Business Review.

Courtney Suess, Ph.D., is an Associate Professor in the Department of Recreation, Park and Tourism Sciences and a Fellow in the Center for Health Systems Design at the College of Architecture at Texas A & M University. She holds a Master’s degree and Doctor of Philosophy from the University of Nevada, Las Vegas, in Hospitality Administration and holds a second Master’s of Science degree in Architecture from the University of Texas at Austin. Her research focuses on the planning and development of tourism, hospitality facilities design, and environmental psychology.

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