Academica Turistica, Year 18, No. 1, April 2025 | 57 Original Scientific Article Insights into Slovenian Hospitality SME Managers' Attitudes toward AI Saša Planinc University of Primorska, Slovenia sasa.planinc@fts.upr.si Marko Kukanja University of Primorska, Slovenia marko.kukanja@fts.upr.si This study explores the attitudes of Slovenian hospitality SME managers toward arti- ficial intelligence ( AI), with a focus on how their demographic characteristics (DC ) and the physical characteristics (PC ) of SME s influence these attitudes. The study used a structured questionnaire and convenience sampling. Using data from 288 managers, it identifies both positive and negative perspectives on AI within a sector undergoing digital transformation. The findings reveal quite balanced attitudes, with both positive and negative experiences being recognized, though there is a slight tendency towards a more negative perspective. Managers’ DC play a more significant role in shaping attitudes than SMEs’ PC. Younger and less experienced managers tend to be more optimistic and enthusiastic about AI adoption, while older and more experienced managers are generally more sceptical. Family-owned businesses, which represent 61% of the sample, recognize some of AI’s potential benefits but primarily express more con- cerns about its use compared to non-family-owned businesses. SME s with more employees and those operating in more competitive environments demonstrate a stronger propensity to adopt AI. This study highlights key barriers to AI adoption in hospitality SME s, empha- sizing the need for targeted education and training programmes, particularly for older managers and those with limited exposure to digital ( AI) tools. Promoting awareness of AI’s benefits through practical demonstrations and best practice examples can reduce resistance and foster more positive attitudes. By addressing these challenges, the hospitality sector can enhance its digital transformation in an increasingly technology-driven environment. Keywords: artificial intelligence, attitudes, hospitality, managers, SME s, Slovenia https://doi.org/10.26493/2335-4194.18.57-72 Introduction Tourism plays a vital role in the European Union’s (EU) economy, contributing 10% to its GDP (Pernice & Kuzhym, 2024). Notably, over 99% of businesses in the EU tourism sector are small and medium-si- zed enterprises (SME s) (European Court of Auditors, 2021). Similarly, in Slovenia, tourism accounted for 9.2% of the country’s GDP in 2023, with SME s repre - senting 99.8% of all companies (Republic of Slovenia, 2024). Recognizing the critical role of SMEs in driving economic growth, the EU Commission has prioritized the development of artificial intelligence ( AI) skil - ls among these enterprises (European Commission, 2024; Ulrich et al., 2021). As technology advances, AI is transforming indu- stries, positioning the hospitality sector at the crossro- 58 | Academica Turistica, Year 18, No. 1, April 2025 Saša Planinc and Marko Kukanja Insights into Slovenian Hospitality … ads of tradition and innovation. AI refers to a broad range of techniques and tools that enable intelligent systems to perceive their environments and make informed decisions (Artificial Intelligence Act, 2024; Gimpel et al., 2023). While these advancements open doors to innovation, collaboration, and efficiency, they also bring ethical concerns and highlight the need for responsible governance to ensure equitable benefits (Abaddi, 2023; Soudi & Bauters, 2024). Despite chal- lenges, AI is set to drive significant economic and societal progress, offering businesses opportunities to enhance efficiency, foster innovation, and address complex problems through data-driven solutions (Kelly et al., 2023). For hospitality SME s, AI presents substantial potential to improve service delivery. The- se businesses, often characterized by flat organizati- onal structures and limited financial resources, can leverage AI to automate tasks such as room bookings, self-check-ins/outs, complaint management, and per - sonalized recommendations (Cai et al., 2022; Citak et al., 2021). Restaurants, for instance, can use AI to manage table reservations, provide menu details, take orders, and process payments, ultimately reducing wait times and enhancing guest satisfaction (Tan & Netessine, 2020; Blöcher & Alt, 2021). AI also aids ope- rational efficiency, inventory management, and guest experience enhancement (Bettoni et al., 2021; Ragazou et al., 2023; García-Madurga & Grilló-Méndez, 2023). However, its adoption is not without challenges, including fears of job displacement, loss of control, and cybersecurity concerns (Saydam et al., 2022). Numerous studies have examined the barriers to AI adoption among SME s, citing issues such as limited knowledge and awareness (Soudi & Bauters, 2024), inadequate skills (Nannelli et al., 2023), high costs and infrastructure limitations (Oldemeyer et al., 2024), and organizational unpreparedness (Lada et al., 2023). Ethical and data security concerns further complicate the AI adoption process (García-Madurga & Grilló- -Méndez, 2023). Understanding these challenges is es- sential for fostering entrepreneurship and economic growth (Abaddi, 2023). From a theoretical perspective, attitudes play a critical role in shaping intentions to adopt techno- logy, as highlighted in frameworks like the Techno- logy Acceptance Model (TAM ), the Theory of Planned Behaviour (TPB ) model, and the Unified Theory of Acceptance and Use of Technology (UTAUT ) model (see also the subsection Theoretical Frameworks for Technology Adoption). These models emphasize how different factors influence attitudes toward techno- logy adoption. Recent research has stressed the im- portance of understanding determinants for effecti- ve AI implementation strategies (Kelly et al., 2023). Factors such as psychological needs (Bergdahl et al., 2023), personality traits (Schepman & Rodway, 2023), and perceived benefits (Ragab & Ezzat, 2021) have been identified as significant. However, Filieri et al. (2021) note a lack of empirical research predicting the specific factors influencing AI adoption in hospitality SMEs (see also Table 1). Research Gap While prior studies have explored AI adoption in large tourism enterprises (Chen et al., 2023; Ivanov & Webster, 2024; Ozdemir et al., 2023) and general (non-hospitality) SMEs, a critical unanswered questi- on remains: How do the demographic characteristi- cs (DC ) of managers and the physical characteristics (PC) of SME s influence managerial attitudes toward AI in hospitality SME s? Hospitality SME s operate within unique ‘guest-oriented’ ecosystems, making it difficult to generalize findings from larger tourism enterprises (Lada et al., 2023; Oldemeyer et al., 2024). Ozdemir et al. (2023) describe AI adoption in hospitality SME s as being in its ‘infancy stage’ , noting that models for AI adoption in these businesses are still underdeveloped. Similarly, Gupta (2024) underscores the importance of identifying key factors that facilitate successful AI integration. Compared to broader research on AI adoption, studies specifically addressing hospitality SMEs are sparse. To the best of our knowledge, no research has comprehensively examined hospitality managers’ atti- tudes toward AI nor the impact of managers’ DC and SMEs’ PC on these attitudes. This study seeks to ad- dress this gap by: (1) evaluating the level of managers’ attitudes toward AI; (2) investigating how managers’ DC influence their AI attitudes; and (3) examining the effect of SMEs’ PC on managerial AI attitudes. Accor - Academica Turistica, Year 18, No. 1, April 2025 | 59 Saša Planinc and Marko Kukanja Insights into Slovenian Hospitality … dingly, we aim to answer the following Research Qu- estions (RQ s): RQ1 What is the level of hospitality SME mana- gers’ attitudes toward AI? RQ2 How do managers’ DC influence their attitu- des toward AI? RQ 3 H o w d o SMEs’ PC impact managers’ attitudes toward AI? This research contributes to the growing body of li- terature on AI adoption in hospitality SME s by empha- sizing the influence of DC and PC on managerial atti- tudes toward AI in the case of Slovenia. Theoretically, it integrates DC and PC to offer a nuanced perspecti- ve on AI adoption. Practically, it provides actionable insights for policymakers and industry stakeholders, advocating for targeted educational initiatives to cul- tivate positive managerial attitudes (see also the Dis- cussion and Conclusion sections). Such interventions are critical to overcoming adoption barriers and acce- lerating the digital transformation of hospitality SME s. Theoretical Background: AI (R)Evolution In Tourism Research In the past decade, tourism research has experienced a significant surge in studies exploring AI. Much of this work has focused on Robots, AI, and Service Auto- mation (also referred to as RAISA), particularly within the hotel and travel sectors, examining perspectives of both guests and service providers (Ivanov & Webster, 2019; Lukanova & Ilieva, 2019; Saydam et al., 2022). Kırtıl and Aşkun (2021) reported an impressive annu- al growth rate of 8.36% in AI-related tourism research since 2017. This growing interest has spurred systema- tic reviews and bibliometric analyses on AI in tourism (e.g. García-Madurga & Grilló-Méndez, 2023; Kırtıl & Aşkun, 2021; Knani et al., 2022; Law et al., 2023; Nannelli et al., 2023; Saydam et al., 2022). AI has been defined through various lenses, of- ten emphasizing two primary dimensions: cognition (behaviour) and human performance (rationality) (Kelly et al., 2023). The EU Artificial Intelligence Act, implemented in 2024, defines AI as a ‘machine-ba- sed system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments’ (Artificial Intelligence Act, 2024). This study adopts this definition as its conceptual framework. Although AI research in tourism remains somew- hat fragmented (Nannelli et al., 2023), five major AI applications have been identified in the hospitality sector: search and booking engines, virtual assistants and chatbots, robots and autonomous vehicles, kio- sks and self-service screens, and augmented/virtual reality ( AR/VR) devices (Huang et al., 2021). These applications are used to address key objectives such as forecasting, operational efficiency, enhancing guest experiences, and promoting sustainability (García- -Madurga & Grilló-Méndez, 2023). Research on AI adoption has predominantly high- lighted its positive impacts. AI empowers tourism businesses to analyse process-generated data, derive actionable insights, and make data-driven decisions, leading to improved operational efficiency (Doğan & Niyet, 2024; Gupta, 2024). By automating repetitive tasks, AI minimizes human errors and boosts produ- ctivity. From a business perspective, it drives growth by increasing sales, expanding market share, and boo- sting revenue (Liu, 2024; Traversa, 2024). At the guest level, AI enhances satisfaction by optimizing experi- ences and reducing wait times. For example, smart re- staurant technologies streamline the dining process, minimizing human interactions and eliminating que- ues (Talukder et al., 2023). However, alongside its benefits, AI adoption also presents ethical, legal, social, and economic challen- ges. These include concerns about job displacement and the transformation of traditional roles as routine tasks become automated. This shift disproportiona- tely impacts guest service and operational positions, increasing unemployment risks (Du, 2024; Tabba- ssum et al., 2024). Despite their critical role in the tourism sector, SMEs demonstrate relatively low rates of AI adoption. SMEs face unique challenges in leveraging AI tech - nologies. Blöcher and Alt (2021) studied AI adoption in the EU restaurant sector, revealing a disconnect between academic enthusiasm and practical applica- tion, as managers expressed a need for clearer guidan- ce on harnessing AI’s potential. Similarly, Ulrich et al. 60 | Academica Turistica, Year 18, No. 1, April 2025 Saša Planinc and Marko Kukanja Insights into Slovenian Hospitality … (2021) found that German SME s preferred traditional technologies and exhibited limited engagement with AI. These findings highlight the challenges SME s en- counter in translating AI’s theoretical advantages into tangible business outcomes. Given their distinct characteristics, SME s requi- re focused attention when examining AI adoption. Unlike larger enterprises, SME s often operate with constrained resources, flat organizational structures, and limited technological expertise. These factors collectively slow AI adoption rates within the sector. Subsequent sections of this study will delve deeper into the specific factors influencing AI adoption in SMEs, emphasizing opportunities to overcome these challenges and unlock AI’s transformative potential for the hospitality industry. Theoretical Frameworks for Technology Adoption Numerous theoretical models have been developed to explore and explain user acceptance of emerging technologies. Key frameworks include the TPB, the TAM (Davis, 1989), the UTAUT (Venkatesh, 2022), and the Diffusion of Innovations Theory. More re- cently, AI-specific frameworks such as the AI Device Use Acceptance ( AIDUA) model (Gursoy et al., 2019) and the Task-Oriented AI Acceptance (T-AIA) model (Yang et al., 2022) have also been proposed. These models provide diverse perspectives on how and why technologies are adopted across various contexts, of- ten emphasizing the interplay of technological, orga- nizational, and environmental factors. A consistent theme across these frameworks is the pivotal role of attitudes in shaping users’ behaviou- ral intentions and subsequent adoption behaviours. For instance, the TPB highlights ‘attitudes toward the behaviour’ as a crucial factor influencing intentions, which ultimately drives actual behaviour. Similarly, the TAM links attitudes to perceptions of usefulness and ease of use, both of which play a significant role in determining an individual’s intention to adopt new technologies (Kelly et al., 2023). Measuring Attitudes Towards AI: Tools and Scales Attitudes are considered a crucial precursor in the technology adoption process across various theore- tical models (as presented above). Given that imple- mentation models are still evolving, recent state-of- -the-art research instruments have been developed to specifically measure attitudes toward AI. These instruments aim to capture the nuances of how indi- viduals and organizations perceive AI and its potenti- al across different contexts, offering valuable insights into the factors that influence AI adoption. By focu- sing on attitudes, researchers can better understand the psychological and emotional barriers affecting decision-making, ultimately helping to develop more effective strategies for integrating AI into various in- dustries. As AI adoption models continue to evolve, these tools will play a key role in shaping both theory and practice in the field. These measurement scales assess attitudes toward AI in diverse contexts and populations, aiming to cap- ture the multifaceted perceptions individuals hold and thereby facilitating a deeper understanding of AI’s acceptance and integration. For example, the ATTARI -WHE scale was develo- ped to assess attitudes toward AI in the workplace, he- althcare, and education (Gnambs et al., 2025). Simi- larly, the ATTARI -12, introduced by Stein et al. (2024), is a psychologically grounded questionnaire that examines attitudes toward AI as a unified construct, independent of specific contexts or applications. The AI Attitude Scale ( AIAS-4) is a concise instrument consisting of four items, focusing on general attitudes toward AI and evaluating its perceived utility and so- cietal impact (Grassini, 2023). Additionally, the MAL- L:AI Scale was developed to measure attitudes toward AI in language learning (Yıldız, 2023). Finally, the General Attitudes towards Artifi- cial Intelligence Scale (GAAIS) is a valuable tool for analysing attitudes toward AI, due to its robust psychometric properties and ability to capture the complexity of public sentiment. This 20-item scale (Schepman & Rodway, 2023) effectively differentiates between positive and negative attitudes, enabling re- searchers to explore various factors influencing these attitudes (Şahin & Yıldırım, 2024). The GAAIS has also been adapted for use in different cultures, confirming its cross-cultural applicability and relevance in diverse research contexts (Kaya et al., 2024). Academica Turistica, Year 18, No. 1, April 2025 | 61 Saša Planinc and Marko Kukanja Insights into Slovenian Hospitality … The Spectrum of Attitudes Towards AI: Insights from Diverse Research Contexts Understanding attitudes towards AI is complex, influ- enced by various factors such as demographics, emo- tional responses, and individual personality traits. Recent research highlights a wide range of emotions towards AI, with both optimism and scepticism often shaped by personal experiences and perceptions of AI’s impact on different aspects of life. For example, Stein et al. (2024) analysed data from U.S. panel participants and German social science students, focusing on the predictive role of persona- lity traits, such as the Big Five, the Dark Triad, and conspiracy mentality. Their findings indicated that individuals who are more agreeable and younger tend to have more favourable attitudes towards AI, while those with a propensity for conspiracy beliefs tend to view AI more negatively. This underscores the signifi- cant influence personality traits have on perceptions of AI. In addition, Park et al. (2024) investigated the role of perceived human-likeness and concerns about job se- curity. Their study, which surveyed 2,841 participants from various work environments, found that feelin- gs of personal utility and adaptability were crucial in shaping attitudes towards AI in professional settings. These studies collectively highlight the complex rela- tionship between individual differences and broader socio-economic factors in shaping attitudes toward AI, stressing the importance of understanding these dynamics to foster positive engagement with emer - ging technologies. Cultural and gender dimensions also emerge as key factors. An extensive survey by Méndez-Suárez et al. (2024) of 20,671 European consumers revealed that men generally hold more favourable views of AI than women. Furthermore, respondents from East Asian countries expressed greater trust in AI management systems compared to those from Western nations, il- lustrating the influence of cultural contexts on AI per - ceptions. Managerial attitudes are also crucial for AI adoption within organizations. Majrashi (2024), in a survey of 330 public sector managers in the United States, fou- nd that perceptions of AI’s usefulness and ethical con- cerns, such as transparency and privacy, were pivotal in shaping their intentions to adopt AI technologies. This emphasizes the need to address ethical concerns to build trust in AI systems. Similarly, Brink et al. (2023) examined managerial attitudes across sectors in the Netherlands and identified four key factors influencing AI adoption: demographics, familiari- ty, psychological traits, and personality. Their study highlighted the importance of transparent communi- cation, tailored training, and user involvement in the design process to enhance AI acceptance. Addressing anxieties about AI is also essential for improving attitudes. Kaya et al. (2024) found in their study of Turkish respondents that increased familia- rity with AI technologies and reduced anxiety signi- ficantly predicted more favourable attitudes. These findings suggest that targeted educational initiatives and ethical implementation practices are critical for building trust and acceptance. T ogether, these studies illustrate how attitudes towards AI are shaped by a combination of demographic, cul- tural, and psychological factors. Factors Influencing AI Attitudes in SMEs: Insights from Recent Studies The table below presents state-of-the-art research stu- dies examining the primary factors influencing attitu- des towards AI in SME s. As demonstrated by the table above, the reviewed studies highlight various factors influencing attitudes toward AI adoption in SMEs, but there is limited emp- hasis on DC and PC . Interestingly, the literature reve- als a gap in research focusing specifically on hospitali- ty SMEs, indicating the need for more targeted studies in this area. In terms of PC , the studies identify an interplay of various factors, including technical, organizational, and environmental challenges. Technical challenges, such as inadequate infrastructure, are frequently cited as barriers to AI adoption (e.g. Oldemeyer et al., 2024; Vogel et al., 2023). Firm size is another important fa- ctor influencing managerial attitudes toward AI ado- ption. Larger firms often face more complex operati- onal challenges, making AI solutions more attractive for enhancing efficiency and maintaining a compe- 62 | Academica Turistica, Year 18, No. 1, April 2025 Saša Planinc and Marko Kukanja Insights into Slovenian Hospitality … titive advantage (Agrawal et al., 2024). On the other hand, smaller firms tend to exhibit more scepticism toward AI, primarily due to perceived complexity and resource constraints, which inhibit technological advancement and the adoption of new technologies (Ivanov & Webster, 2024). As a result, smaller firms may lag behind larger firms in utilizing AI for operati- onal improvements. Despite the rich body of research on factors in- fluencing AI attitudes, the role of managers’ DC and SMEs’ PC, especially in hospitality SME s, remains un- derexplored. Schwaeke et al. (2024) noted that the Table 1 Factors Influencing AI Attitudes Author(s) Sample Main findings (influencing factors) Iyelolu et al., 2024 Literature review study Resistance to change, lack of technical expertise, and data security concerns, which hinder adoption and innovation. Wong & Y ap, 2024 Respondents from Malaysian MSMEs (n = 196) Compatibility, top management support, alignment with business strategy, organizational resources, competitive pressure, and government regulations. Schwaeke et al., 2024 Literature review study A complex interplay of cultural factors, knowledge factors, and competitive pressures. Badghish & Soomro, 2024 Managers from six different sectors in Saudi Arabia (n = 220) Relative advantage, compatibility, sustainable human capital, market and customer demand, and government support. Almashawreh et al., 2024 SME owner-managers in Jordan (n = 364) Relative advantage, complexity, top management commitment, and organizational preparedness. Agrawal et al., 2024 Indonesian SMEs (n = 292) Technological, organizational, and environmental factors primarily influence attitudes, shaping their decision-making processes and competitive advantage in the market. Oldemeyer et al., 2024 Literature review study Lack of knowledge, costs, and inadequate infrastructure, encompassing social, economic, and technological challenges. Bąk et al., 2024 Literature review study Strategy and business model, culture and attitude, resources, support, entrepreneurship and innovation, competitive position, and environmental conditions. Charllo, 2024 SME representatives (n = 498) in the USA. Study results presentation using secondary data. Lack of expertise, funding constraints, and data privacy concerns hinder. Lada et al., 2023 Owners or managers of different SMEs in Sabah, Malaysia (n = 196) Top management commitment and organization readiness significantly influences attitudes. In contrast, competitive pressure, employee adaptability, and external support show an insignificant impact. Rawashdeh et al., 2023 SME owners and managers in the United States (n = 353) The study identifies technological factors influencing AI adoption, highlighting the mediating role of accounting automation. Key variables include time-saving and efficiency improvements, which significantly impact attitudes. Vogel et al., 2023 Literature review study Fear of job loss, lack of AI experience, insufficient infrastructure, and the need for increased understanding of AI contribute to negative attitudes. Note Summarized by authors from listed sources. Academica Turistica, Year 18, No. 1, April 2025 | 63 Saša Planinc and Marko Kukanja Insights into Slovenian Hospitality … current literature on SME s presents a fragmented un- derstanding of how these enterprises engage with AI technologies. This gap needs to be addressed in future studies to gain a clearer understanding of the specific barriers faced by hospitality SME s and to identify ways to overcome them. Methods Research Process, Instrument Design, and Data Analyses The variables included in the research instrument were carefully selected and adapted from prior studies. Considering the topic’s novelty and evolving nature, an extensive review of recent literature was conducted using major academic databases. The review focused on tourism and SME -related research over the past five years, employing keywords such as attitudes , AI, digitalization, hospitality, and SMEs. Attitudes toward AI were measured using the 20-item GAAIS scale (Schepman & Rodway, 2023). The positive attitudes subscale includes 12 items, and the negative attitudes subscale includes 8 items (statements). Responses were recorded on a five-point Likert-type ordinal scale, ran- ging from 1 (Strongly Disagree ) to 5 (Strongly Agree ), with reverse scoring applied to negative subscale items to ensure consistency in analysis (see Table 2). To provide insights into AI adoption, managers’ demographic characteristics were collected using va- riables such as age, gender, education, years of experi- ence in the industry, and managerial function (Kuka- nja et al., 2023). Additionally, physical characteristics of SMEs were collected using variables such as years of business activity, number of employees, family bu- siness status, number of competitors, capacity (num- ber of seats/beds), and potential rent payments. These variables were introduced from previous studies (Pla- ninc et al., 2022; Kukanja et al., 2023). The data were analysed using IBM SPSS 29.0, with descriptive statistics (M – mean value, and SD – stan - dard deviation) employed to summarize the key cha- racteristics of the sample and variables, and bivariate analysis conducted to explore the impact of DC and PC on AI attitudes. Based on the type of variables and the data distribution, we applied appropriate statisti- cal tests: Spearman’s rank correlation coefficient to assess the relationships between two ordinal variables or a combination of ordinal and numerical variables, the Kruskal-Wallis test (H test statistic) to compare differences in ordinal data across more than two in- dependent groups, and the Mann-Whitney test (U test statistic) to compare differences in ordinal data between two independent groups. This comprehensi- ve approach ensured a robust statistical analysis of the relationships between attitudes, managers’ DC , and SMEs’ PC. Sample Description and Data Collection Process The sample for this study comprised SME s operating in the Republic of Slovenia. These were specifically classified under the EU’s standard NACE categories I55 (accommodation) and I56 (food and beverage service activities). According to the official business register (AJPES, n.d.), there were 8,303 businesses in these ca- tegories as of 2023. Given the diverse nature of SME s, which often en- gage in multiple business activities and span various subcategories, direct comparisons can be challenging. To address this, the study focused on SME s whose operational revenue was exclusively derived from I55 and I56 activities. The selected sample emphasized businesses with similar operational characteristics, such as those providing ‘traditional’ bed accommo- dations (e.g. hotels, motels, and bed & breakfasts) and table service facilities (e.g. restaurants, inns, and snack bars). This approach ensured a more uniform sample, enabling more accurate comparisons within the targeted sector. Due to the absence of detailed official data on the characteristics of hospitality SME s, a convenience sampling method was employed, as explained later in the study. Data collection took place between January and July 2023. The process began by pre-screening pu- blic records to identify eligible SME s, excluding those that did not meet the inclusion criteria of I55 and I56 classifications. As in previous studies (e.g. Lada et al., 2023; Pla- ninc et al., 2022; Kukanja et al., 2023), the respondents selected were managers or owner-managers, as they are the primary decision-makers regarding techno- logy adoption. Respondents were required to confirm that their businesses primarily operate in the food 64 | Academica Turistica, Year 18, No. 1, April 2025 Saša Planinc and Marko Kukanja Insights into Slovenian Hospitality … (I55) or accommodation (I56) service sectors and ge- nerate the majority of their operating revenue from these activities. If a facility failed to meet the inclusion criteria or if a manager declined to participate, inter - viewers moved on to the next eligible facility. By the conclusion of the data collection period, the study had sampled 288 SME s, representing 3.46% of the total population in the I55–56 classifications. While this sample size offers a solid foundation for analysis, it may limit the generalizability of the findin- gs to the broader population of hospitality SME s (see also the Conclusion section). Results Sample Characteristics The demographic data reveal that 66% of respondents (Slovenian hospitality SME managers) were men, and the majority had completed at least secondary educa- tion (56%), with an additional 42% having attained an even higher level of education. The average age of the respondents was 44.53 years (SD = 10.31). In terms of experience in the hospitality industry, respondents had an average of 21.06 years of experience (SD = 10.86). Regarding SMEs’ PC, the average duration of busi- ness activity was 23.37 years (SD = 27 .78). A significant proportion of SME s (70%) are managed by managers who are also their owners, indicating a strong entrepre- neurial spirit. Additionally, 61% of all SME s are family- -owned businesses. The average number of employees was 14.10 (SD = 31.33), the average number of competi- tors was 3.96 (SD = 4.74), the average number of seats/ beds was 101.39 (SD = 116.14), and 43% of respondents reported paying rent, while the remaining 57% did not. Table 2 Mean Values for GAAIS Items Item code Attitude towards AI M SD A14 There are many useful applications of AI (+) 3.36 1.142 A03 Organizations use AI unethically (-) 3.17 1.141 A19 People like me will suffer if AI use increases (-) 3.16 1.314 A08 AI is sinister (-) 3.15 1.219 A05 I am excited about what AI can do (+) 3.06 1.318 A15 I get chills thinking about AI use in the future (-) 3.03 1.313 A17 Society will benefit from AI in the future (+) 2.98 1.148 A20 AI is used for spying on people (-) 2.98 1.331 A12 AI is exciting (+) 2.97 1.157 A02 AI can provide new economic opportunities (+) 2.94 1.237 A09 AI could take control over people (-) 2.94 1.418 A10 I think AI is dangerous (-) 2.91 1.288 A06 AI systems make many mistakes (-) 2.90 1.113 A11 AI can positively impact people‘s well-being (+) 2.88 1.054 A07 Interest in using AI in daily life (+) 2.55 1.232 A16 AI systems can perform better than humans (+) 2.48 1.226 A04 AI systems can help people feel happier (+) 2.43 1.209 A13 AI would be better than employees (+) 2.43 1.320 A18 I would like to use AI at work (+) 2.35 1.249 A01 I prefer using AI systems over humans (+) 1.98 1.265 Average 2.83 1.281 Note Positive and negative items are marked with the positive (+) or negative (-) sign. Prior to processing, the negative GAAIS items were reverse-scored (1 = Strongly agree; 5 = Strongly disagree). Thus, higher scores on each subscale represent more positive attitudes. Items are sorted by mean values in descending order. Academica Turistica, Year 18, No. 1, April 2025 | 65 Saša Planinc and Marko Kukanja Insights into Slovenian Hospitality … Statistical Analyses to Answer Research Questions The results presented in Table 2 provide the answer to RQ1. The study evaluated the values for GAAIS items by calculating mean values and standard deviations. Negative attitudes were reverse scored. The overall mean score for attitudes indicated a slightly negative, yet close to neutral managerial attitude towards AI (M = 2.83), with quite a few differences between ma- nagers’ opinions (SD = 1.28). The Slovenian hospitality SME managers mostly agreed (M = 3.36) that there are many useful appli- cations of AI (A14). On average, they agreed slightly less that organizations use AI ethically (A03), that pe- ople like them will not suffer if AI use increases (A19), and that AI is not sinister (A08). On the other hand, they least agreed (M = 1.98) that they prefer using AI systems over humans (A01) and slightly more (M = 2.35) that they would like to use AI at work (A18). In general, negative items (reverse scored) achie- ved slightly higher average ratings (M = 3.03, SD = 1.27) than positive items (M = 2.70, SD = 1.21). The highes- t-rated positive item was A14 (‘There are many use- ful applications of AI’) and the highest-rated negative item was A03 (reversefd statement: ‘Organizations use AI ethically’). In contrast, the lowest-rated posi- tive item was A01 (‘I prefer using AI systems over hu- mans’) and the lowest negative item was A06 (rever - sed statement: ‘AI systems make few mistakes’). Next, statistical relationships between managers’ DC and AI attitudes were calculated to answer RQ 2. The results presented in Table 3 demonstrate that managers’ attitudes towards AI are significantly influ- Table 3 Statistical relationships between managers’ demographic characteristics and their AI attitudes Item Age Gender Education Y ears of exp. Managerial function r s Sig. U Sig. H Sig. r s Sig. U Sig. A01 −0.117 0.048 8776.5 0.519 2.028 0.363 −0.165 0.005 7809.5 0.170 A02 −0.092 0.122 7538.0 0.011 1.452 0.484 −0.110 0.064 6823.5 0.004 A03 −0.024 0.682 8507.5 0.367 0.049 0.976 0.024 0.687 8021.0 0.455 A04 −0.100 0.094 7980.5 0.085 6.445 0.040 −0.131 0.028 7821.0 0.250 A05 −0.196 0.001 8187.0 0.130 4.479 0.107 −0.232 0.000 6315.5 0.001 A06 0.020 0.734 8730.0 0.583 1.491 0.474 0.021 0.724 7265.5 0.036 A07 −0.101 0.088 8038.0 0.080 8.080 0.018 −0.165 0.005 6634.0 0.001 A08 −0.050 0.403 9055.0 0.919 3.932 0.140 −0.037 0.537 8427.0 0.817 A09 −0.142 0.016 9131.5 0.955 3.209 0.201 −0.134 0.024 8353.0 0.679 A10 −0.132 0.026 8732.0 0.597 1.935 0.380 −0.111 0.062 8212.5 0.613 A11 −0.094 0.112 7333.5 0.005 5.039 0.081 −0.137 0.022 7245.0 0.033 A12 −0.066 0.266 8562.0 0.381 0.597 0.742 −0.080 0.179 6777.5 0.004 A13 −0.084 0.158 7865.5 0.050 4.819 0.090 −0.072 0.230 8331.5 0.767 A14 −0.142 0.017 8493.0 0.400 4.063 0.131 −0.144 0.015 7225.5 0.041 A15 −0.156 0.008 8271.0 0.164 2.858 0.240 −0.120 0.043 7767.0 0.174 A16 −0.100 0.093 6856.5 0.001 4.349 0.114 −0.047 0.437 8265.0 0.789 A17 −0.028 0.635 8575.0 0.388 6.316 0.043 −0.077 0.194 8035.0 0.381 A18 −0.139 0.019 8398.5 0.230 13.596 0.001 −0.158 0.007 7682.5 0.132 A19 −0.086 0.147 9154.0 0.983 0.169 0.919 −0.040 0.502 7382.0 0.048 A20 −0.125 0.034 8116.5 0.117 1.879 0.391 −0.085 0.153 7551.0 0.099 Note Statistically significant relationships (p ≤ 0.05) are marked in bold. 66 | Academica Turistica, Year 18, No. 1, April 2025 Saša Planinc and Marko Kukanja Insights into Slovenian Hospitality … enced by DC . Nearly all items are affected by at least one DC. Some DC, such as age, years of experience, and managerial function, have a more pronounced impact. On the other hand, each DC influences only certain items, but not more than half of them. Negative correlations (r s ) across all eight statisti- cally significant items related to age, clearly indicate that attitudes towards AI are inversely proportional to experience. Results reveal that younger managers express greater enthusiasm, willingness and optimism regarding the use of AI (A01, A05, A09, A10, A14, A15, A18, A20), while older respondents are more scepti- cal about its benefits. However, the strength of these correlations is relatively weak, although they remain statistically significant. Regarding years of experience, all statistically si- gnificant correlations are also negative, indicating that managers with shorter tenure are more positive towards AI (A01, A04, A05, A07, A09, A11, A14, A15, A18). The strength of these correlations is, again, rela- tively weak. Regarding managerial function, the tests reveal statistically significant differences for certain items. Additional analysis of the average ranges across grou- ps (detailed data are omitted due to space constraints) shows that managers who are also SME owners exhibit a somewhat more conservative approach towards the use of AI (A02, A05, A06, A07, A11, A12, A14, A19) compared to managers hired as external professio- nals. Table 4 Statistical relationships between SME s’ physical characteristics and managers’ AI attitudes Item Ye ar s of busin. activ. No. of employees Family business No. of competitors Capacity Rent r s Sig. r s Sig. U Sig. r s Sig. r s Sig. U Sig. A01 −0.036 0.545 0.167 0.005 8777.0 0.122 0.020 0.744 −0.066 0.270 9969.0 0.906 A02 −0.063 0.292 0.115 0.055 7947.5 0.007 0.037 0.544 −0.001 0.984 9304.0 0.270 A03 0.032 0.592 −0.004 0.949 9245.5 0.613 0.086 0.154 0.037 0.535 9732.5 0.774 A04 −0.065 0.280 0.006 0.923 8629.5 0.136 0.065 0.278 −0.125 0.037 9456.5 0.484 A05 −0.058 0.329 0.167 0.005 8427.5 0.048 0.043 0.476 0.022 0.717 8945.0 0.105 A06 0.008 0.888 0.021 0.724 8489.5 0.083 0.161 0.007 −0.011 0.850 9849.5 0.936 A07 −0.036 0.549 0.162 0.007 8108.0 0.014 0.118 0.050 0.056 0.350 9206.5 0.214 A08 0.042 0.481 0.132 0.028 9097.0 0.392 0.125 0.038 0.071 0.240 8959.0 0.124 A09 0.011 0.853 0.082 0.174 9327.0 0.533 0.121 0.044 −0.056 0.351 9581.0 0.495 A10 0.004 0.946 0.087 0.149 8444.0 0.079 0.155 0.010 0.010 0.864 9405.5 0.443 A11 −0.088 0.142 −0.069 0.255 8817.0 0.219 0.027 0.655 −0.036 0.551 8706.0 0.061 A12 −0.112 0.060 0.003 0.955 9475.5 0.781 0.045 0.457 −0.023 0.704 8785.0 0.072 A13 −0.068 0.255 0.092 0.127 8469.5 0.070 0.056 0.352 0.003 0.965 9335.0 0.332 A14 −0.011 0.850 0.079 0.190 8537.5 0.102 −0.036 0.549 0.040 0.503 7319.5 0.000 A15 −0.009 0.875 0.166 0.006 8096.0 0.013 0.098 0.103 0.015 0.804 8468.0 0.019 A16 −0.080 0.179 −0.028 0.646 8796.0 0.249 −0.071 0.241 −0.014 0.812 9088.5 0.244 A17 −0.039 0.510 −0.026 0.671 8671.5 0.129 0.034 0.576 −0.023 0.698 9226.5 0.252 A18 −0.041 0.487 0.127 0.034 8052.5 0.010 0.102 0.088 −0.050 0.404 9509.5 0.424 A19 0.060 0.311 0.125 0.038 8419.5 0.046 0.124 0.038 −0.026 0.665 9471.5 0.396 A20 0.043 0.467 0.125 0.038 8390.5 0.054 0.117 0.053 −0.016 0.788 9075.5 0.175 Note Statistically significant relationships (p ≤ 0.05) are marked in bold. Academica Turistica, Year 18, No. 1, April 2025 | 67 Saša Planinc and Marko Kukanja Insights into Slovenian Hospitality … Regarding gender and education, statistically si- gnificant differences are less pronounced. However, some differences are still present, and in those cases, further analysis of the average ranges across groups reveals that women express less agreement regarding the positive effects of AI usage than men (A02, A11, A13, A16), and higher education is associated with greater confidence in the potential of AI and recogni- tion of its benefits (A04, A07, A17, A18). In the last step, to answer RQ 3, statistical relation- ships between attitudes and PC of hospitality SMEs were calculated. From Table 4, it is evident that PC variables ge- nerally have a less pronounced influence on shaping managers’ attitudes compared to DC variables. Some PC variables have no impact at all (years of business activity), others affect only one or two items (capacity and rent), while some do exhibit influence, but not on more than half of the items. Positive correlations (r s ) for statistically significant items related to the number of employees and the number of competitors clearly indicate that mana- gers in companies with a larger number of employe- es are more willing to adopt AI (A01, A05, A07, A08, A15, A18, A19, A20), and similarly, managers in SME s operating in more competitive environments are also more willing to adopt AI (A06, A07, A08, A09, A10, A19). However, the strength of these statistically signi- ficant correlations is relatively weak. Regarding company ownership, the tests reveal statistically significant differences for certain items (A02, A05, A07 , A15, A18, A19). Further analysis of the average ranges across groups (detailed data are omi- tted due to space constraints) shows that managers from family-owned businesses exhibit a more con- servative approach towards AI adoption compared to those from non-family-owned businesses. Discussion Our findings provide valuable insights into the adop- tion of AI within hospitality SME s, a sector undergo- ing rapid digital transformation. Despite the potential of AI to enhance guest experiences and streamline operational processes, adoption rates among these businesses remain notably low, highlighting persistent challenges in integrating AI technologies. The analysis of Slovenian hospitality SME s mana - gers’ attitudes toward AI (see Table 1) revealed a fra- gmented understanding and insufficient theoretical frameworks tailored to hospitality SME s. The scarcity of research focusing on hospitality SME s limits the applicability of broader SME studies’ results to this unique ecosystem. Accordingly, this study aimed to examine hospitality managers’ attitudes toward AI, exploring how DC and PC influence these attitudes. Our research results show that managers’ attitudes toward AI are slightly negative, yet close to neutral. This highlights the pressing need for industry-spe- cific education and capacity-building efforts. Our results contrast with Schepman and Rodway (2023), who reported more favourable attitudes toward AI in a broader SME context. While direct comparisons are limited by the lack of existing studies specific to hospitality SMEs, our findings emphasize the critical role of attitudes in AI adoption (see also the subsecti- on Theoretical Frameworks for Technology Adopti- on). The observed negative attitudes highlight a need for targeted interventions, such as education, best- -practice showcases, and emphasizing AI’s benefits, to foster more positive perceptions (see also the Conclu- sion section). DC emerged as a more important factor, having a greater statistically significant influence on the items related to attitudes toward AI. Y ounger managers tend to be more receptive to AI than older, more experien- ced counterparts, especially those who do not own the business. This hesitancy among older managers may stem from entrenched management practices and va- lues, such as the mindset of ‘we have always done it this way’. Cultural and managerial factors, including a prioritization of personalized guest service over technological innovation, might further exacerbate this resistance. Our findings also suggest that women are less likely to agree on AI’s benefits. Additionally, industry-specific challenges – such as the labour-in- tensive nature of hospitality, reliance on a seasonal and less-educated workforce, and operational com- plexities – may amplify these negative attitudes. Our research shows that higher education levels seem to 68 | Academica Turistica, Year 18, No. 1, April 2025 Saša Planinc and Marko Kukanja Insights into Slovenian Hospitality … contribute to more favourable attitudes toward AI. Educational initiatives could promote a more positive stance toward AI. The influence of PC is less pronounced than that of DC. Nonetheless, larger SME s and those operating in more competitive environments show greater awa- reness of AI’s potential. On the other hand, managers of family-owned businesses, which comprised 61% of our sample, exhibit caution in adopting AI, potentially reverting to traditional hospitality approaches. AI, however, does not need to disrupt the provi- der-guest relationships. Instead, it can enhance them through tools like Customer Relationship Manage- ment (CRM), which personalize guest experiences, improve efficiency, and enable data-driven decision- -making (Dwivedi et al., 2023; Ozdemir et al., 2023). AI’s role in fostering a ‘hybrid intelligence’ ecosystem – where humans and AI collaborate – offers a promi- sing pathway for the hospitality sector (García-Mad- urga & Grilló-Méndez, 2023; Kırtıl & Aşkun, 2021). However, achieving such an ecosystem will require greater investments in employee training and strate- gic alignment of AI tools with hospitality goals. As Nannelli et al. (2023) note, AI presents vast opportuni- ties, but training is essential to avoid falling behind industry trends as the technology evolves. Our findings diverge from Ivanov and Webster (2024), who examined attitudes toward AI in the Bul- garian hotel industry. They concluded that demograp- hic and property characteristics did not significantly influence preferences for AI in decision-making, emphasizing instead that general attitudes toward AI were the strongest predictors of adoption. This con- trast underscores the complexity of factors affecting AI adoption and the need for further exploration, as cultural and regional factors may also play a role in shaping AI attitudes. As a relatively under-researched area, further stu- dies are required to deepen our understanding of both attitudes and actual AI implementation in hospitality SMEs. This aligns with Mogaji et al. (2024), who stress the importance of developing nuanced conceptual frameworks in AI research. Importantly, integrating AI into hospitality requires a digitally skilled workfor - ce capable of effectively utilizing and managing these technologies. Managers, therefore, must focus on em- powering their employees with the necessary digital competencies. This study highlights the relatively low and predo- minantly negative attitudes toward AI among hospi - tality SME managers and the significant influence of DC, and partially PC , on these perceptions. Promoting positive attitudes toward AI is crucial for successful adoption. Achieving this will require targeted educa- tion, practical demonstrations of AI benefits, and tai- lored approaches that address DC and PC influences. As the hospitality industry continues to evolve, managers must adapt by embracing digital transfor - mation and equipping themselves and their employe- es with the skills necessary for AI integration. We can assume that the dual focus on AI and traditional skills will be critical for SME s to sustain their compe- titiveness and meet the expectations of increasingly tech-oriented guests. Doing so will not only enhance their competitiveness but also ensure their long-term sustainability in an increasingly digitalized world. Conclusion At the beginning of this paper, we set out to examine managerial attitudes toward AI (RQ 1) and assess the impact of DC and PC of SME s on these attitudes (RQ 2 and RQ3). To achieve these objectives, we conducted a comprehensive literature review to identify key fa- ctors influencing AI attitudes in SMEs. Using data co- llected from 288 respondents (Slovenian hospitality SME managers), we analysed both positive and nega- tive managerial attitudes and tested the relationships between the exogenous variables (DC and PC ) and these attitudes. Our findings indicate that managerial attitudes toward AI are generally slightly negative. We also de- monstrated that DC – particularly age, years of expe- rience, and managerial function – and PC – including the number of employees, number of competitors, and the company ownership (family business) – sig- nificantly influence these attitudes. Given the specific characteristics of the hospitality sector, our study sug- gests that improving managerial attitudes toward AI could, in line with the TAM model, enhance AI ado- ption. Addressing DC and PC factors can help mana- Academica Turistica, Year 18, No. 1, April 2025 | 69 Saša Planinc and Marko Kukanja Insights into Slovenian Hospitality … gers better appreciate the benefits and challenges of AI implementation in SME s. Theoretically, this study contributes to the growing body of research on AI in SMEs. Within the relatively underexplored hospitality sector, it provides insi- ghts into how DC and PC shape managerial attitudes toward AI. By examining the interplay between family business dynamics, ownership-managerial roles, and attitudes, our research also enriches the literature on digital entrepreneurship in emerging hospitality stu- dies. Furthermore, it underscores the need for a nu- anced understanding of how cultural and regional fa- ctors might influence these dynamics, particularly in Slovenia, where specific market conditions may shape AI attitudes differently from broader global trends. Practically, these findings offer actionable recommendations for hospitality managers. Mana- gers must recognize the advantages of digitalization and adopt new technologies to improve both financial and non-financial performance. Addressing the pre- vailing slightly negative attitudes is critical for foste- ring a culture of innovation. For instance, educational programmes tailored to older managers or those with limited exposure to digital tools could facilitate more positive perceptions of AI. Encouraging peer-to-peer learning and sharing success stories from early adop- ters could further reduce resistance to change. This study also highlights implications for go- vernment policy. The significant influence of age, experience, and gender differences on AI attitudes underscores the need for targeted educational initi- atives aimed at specific demographic groups of ma- nagers. Policymakers and industry stakeholders, in collaboration with academia, should address gaps in AI knowledge and develop a supportive ecosystem to accelerate AI adoption within the sector. However, this study has several limitations that future research should address. The sample size may restrict the generalizability of the findings, and the use of convenience sampling could affect the repre- sentativeness of the sample. Furthermore, the demo- graphic profile of respondents – predominantly men, at least secondary-educated, aged 35–55, with a large proportion of family businesses and owner-managers (70%) – may have influenced the results. The relian- ce on self-reported data poses another limitation, as survey responses may not fully capture actual behavi- ours or attitudes. Future research should also explore the potential long-term impact of AI adoption on SME competitiveness, particularly as digital transformati- on accelerates across industries. Future research could benefit from broader, more diverse samples and alternative methodologies, such as mixed-methods approaches or case studies, to pro- vide deeper insights. Studies incorporating triangula- tion among managers, guests, and employees could enhance understanding, especially in a sector where balancing digital and human interactions remains a challenge. Exploring generative AI applications, whi- ch are increasingly accessible online, could further illuminate how managers experiment with and perce- ive AI. Additionally, examining factors that influence AI attitudes across different industries, as identified in prior studies, could help contextualize the unique challenges and opportunities within the hospitality sector. Finally, future research should address industry- -specific factors such as guest orientation and re- sistance to change, which could significantly shape attitudes toward AI in hospitality SME s. 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