Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 437 https://internationalpubls.com Adoption of Smart Technologies in MICE Tourism: Understanding Consumer Challenges and Experience in Delhi NCR Aviral Saxena 1*, Dr. Sanjay Jamwal2 1Research Scholar, Department of Tourism Management, CT University, Ludhiana, Punjab, India 2Associate Professor, School of Hotel management, Airlines and Tourism, CT University, Ludhiana, Punjab, India Corresponding author’s email address: aviral86_saxena@yahoo.co.in Article History: Received: 12-12-2024 Revised: 25-01-2025 Accepted: 05-02-2025 Abstract: This research paper, "Adoption of Smart Technologies in MICE Tourism: Understanding Consumer Challenges and Experience in Delhi-NCR," throws light on the challenges involved in adopting smart technologies among consumers and the experience of them in the MICE (Meetings, Incentives, Conferences, and Exhibitions) segment in Delhi-NCR. The main research objectives were the identification of major challenges encountered by the consumers in using smart technologies during MICE events, and an evaluation of these experiences. For this research, a structured questionnaire was created and distributed using Google Forms to respondents who had been to MICE events within the region. A total of 220 valid responses were received based on convenience sampling. The questionnaire was structured into three parts, targeting demographic information, barriers of implementing smart technologies, and customer experiences of various smart technologies applied in MICE events. Acceptable Cronbach's Alpha values emerged in reliability analysis. Challenges were assessed using factor analysis and chi-square tests, whereas descriptive statistics were used in evaluating consumer experience. Three prominent challenges were found: issues with affordability and integration; concerns on connectivity and privacy; and security challenges. In addition, consumer experiences vary; for instance, some of the technologies like Beacon Technology and Wearable Technology had the highest satisfaction, while those such as Event Management Platforms and VR/AR technologies show low satisfaction. Therefore, the study concludes that just as some technologies are welcome, others need further refinement to enhance adoption and consumer satisfaction. It's through these challenges that their integration in MICE tourism will be optimized, along with consumer experiences. Keywords: Smart technology, MICE Tourism, Consumers Challenges, Experience, Delhi-NCR. 1. INTRODUCTION The integration of smart technologies into MICE tourism in the hospitality and tourism industry has emerged as an important trend in the urbanizing Delhi NCR region. Essentially, smart technology adoption aims to improve service operations by streamlining these, customer experiences, and delivering insights to stakeholders through data analysis. As indicated by Buhalis and Amaranggana (2015), smart tourism harnesses the potential of digital ecosystems to interconnect devices, people, and services, thus offering a seamless and enriching experience. Such an interaction has become very Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 438 https://internationalpubls.com popular in MICE tourism, in which efficiency and personalization are important considerations (Haven-Tang & Jones, 2012). Delhi NCR, being a major business tourism destination, acts as a fertile ground for the deployment of such technologies that range from virtual reality and augmented reality in event presentations to AI-driven chatbots in customer support (Gretzel et al., 2015). While promising to redefine service delivery and engagement in the MICE sector, these technological advancements do pose challenges, particularly on the consumer's end. Consumers in the MICE tourism sector may be both corporate delegates and event participants. In a study done by Neuhofer, Buhalis, and Ladkin, (2014), while smart technologies improve personalization, the same technology makes it impossible for users who lack familiarity with digital interfaces to access the service or information intended. This is especially relevant in a diverse demographic landscape like Delhi NCR, where digital literacy varies significantly across consumer groups (Sundbo et al., 2018). In addition, the issue of data security arises as a major challenge. As smart devices and platforms collect vast amounts of consumer data to tailor experiences, concerns over data privacy and misuse have become increasingly pertinent (Xiang et al., 2015). For example, consumers may be hesitant to share personal information, even if it is necessary for creating a customized service experience. These issues require a more subtle understanding of consumer behavior, which is often influenced by trust, perceived value, and ease of technology adoption (Davis, 1989). Despite these challenges, the role of smart technologies cannot be overemphasized in enhancing consumer experiences in MICE tourism. Technologies such as Internet of Things (IoT)-enabled devices and AI-driven analytics allow for real-time feedback and service adjustments to foster higher levels of satisfaction among attendees (Buhalis et al., 2019). For instance, facial recognition systems for hassle-free check-ins and automatic scheduling tools can greatly help decrease the waiting time and thereby efficiency of operations (Sigala, 2018). However, that the system is only viable with the removal of the consumer-centric barriers: it is accessible and is accessible and inclusive (Amaro & Duarte, 2015). Studies have demonstrated how consumer acceptance of smart technologies can be moderated through its ease of use and its perceived utility (Venkatesh et al., 2003). Therefore, it is essential to gain a deep understanding of these moderation factors to promote widespread adoption of smart solutions in MICE tourism. Delhi NCR's socio-economic and cultural landscape further increases the complexity of adopting smart technologies in MICE tourism. The dual identity of this region as a historical and modern hub creates a backdrop where the traditional hospitality of the past intersects with cutting-edge innovation (Buhalis & Sinarta, 2019). Consumers in this region are also expected to meet high standards of quality, which means that smart technologies should be seamlessly integrated with the traditional mechanisms for service delivery (Gretzel et al., 2020). Additionally, the COVID-19 pandemic hastened the process of digital transformation in the domain of MICE tourism, as virtual and hybrid events become more the norm than the exception (Sigala, 2021). Although this transition has facilitated wider access and flexibility among those in attendance, it has also focused on the digital divide and the need for inclusive strategies that consider different technological proficiencies among the consumers. To summarize, smart technology in MICE tourism would work as a double-edged sword for stakeholders operating in Delhi NCR. This technology has huge opportunities in the improvement of efficiency and enhancing consumer satisfaction, but they come with many challenges Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 439 https://internationalpubls.com in areas related to consumer readiness and data security. Therefore, it would be crucial to maintain an appropriate consumer-centric approach and, in the midst of developing technological solutions, foster a culture of inclusivity and trust. Future studies should delve deeper into the behavioral dimensions of technology adoption in MICE tourism, which captures consumer expectations, technological capabilities, and socio-cultural factors interplay. Addressing these dimensions can help the MICE sector in Delhi NCR tap into smart technologies to support sustainable growth and create meaningful experiences for its diverse clientele. 2. REVIEW OF LITERAURE 2.1 Concept of MICE Events MICE events- some concepts and meaning MICE is a specific tourism and hospitality segment which falls upon business-oriented traveling and gatherings (Korir, 2013). The MICE events have been designed towards the purpose of unifying multiple professionals, businesses, or organizations into a singular platform so that they might be involved in networking and knowledge exchange, strategic planning and promotion of brands or else so that award-winning achievements might be acquired for them (Anas et al., 2020). Such events have great planning, resource management, and attention to detail not without the collaboration of many different stakeholders: the event planner and organizer, venue providers, service providers, and technology partners (Bowdin et al., 2012). Meetings are usually referred to the formal gatherings of a group of people who have a common objective, like corporate board meetings and training sessions (Allen, 2008), whereas incentives are travel programs meant to motivate and reward employees or partners (Graan et al., 2018). Conferences are a more massive meeting based on discussions, learning, and collective processes that often take place about a specific industry or vocation (Rogers & Wynn-Moylan, 2022), but exhibitions serve as platforms on which businesses can showcase products and services to an accurately targeted audience (Guoqing, 2023). MICE events are much more than a shot in the arm for the global economy, as cities and countries compete to host such events and gain the rights to do so, helping revitalize the local economies and improve their international reputation (McCartney, 2008). MICE events will thrive only if state-of-the-art infrastructure with connectivity, technological support, and a skilled workforce are adequate to provide seamless experience quality and memory creation for attendees (Lekgau & Tichaawa, 2022). 2.2 Concept of Smart Technologies This is the sum technologies with innovations in the direction of optimized efficiency, interconnectivity, and decision-making towards the optimal application of intelligent systems, data analytics, and automation of the device (Bellini et al., 2022). In simple words, smart technology works on IoT, AI, big data, and cloud computing that allow devices and systems to communicate effectively (Ghosh et al., 2018). Such technologies are applied transformatively across and within sectors such as smart cities, where IoT-driven solutions improve urban living by creating smart transportation, energy management, and resource real-time monitoring (Gupta et al., 2023; Syed et al., 2021). This encompasses wearables that improve the monitoring and diagnosing of patients, whereas on the manufacturing side, Industry 4.0 makes use of automation and machine learning in bringing efficiency to operations (Li et al., 2020). The critical stages of the problems are related to data privacy, Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 440 https://internationalpubls.com interoperability, and cybersecurity issues. This actually means that the further development of smart technologies is a sign toward moving from systems that are able to handle enormous quantities of data yet are unable to evolve according to their environments to those capable of learning and adapting according to their environments, thus changing modern infrastructural and lifestyle paradigms (Kose et al., 2023; Singh et al., 2020). 2.3 Application of Smart Technologies in MICE Events Smart technologies have almost revolutionized this industry with the application to MICE events as the planning, execution, and evaluation processes become easy and are done with lesser complexity. The inquiry and registration processes at events have been made efficient by artificial intelligence- powered chatbots and virtual assistants. Another dimension where smart technologies enhance efficiency as well as engagement with the participants entails creating immersive experiences for attendees through applications of AR and VR. Tools applied in running the events include event management software and mobile applications, where there is enhancement of facilitation of events in real time with networking and customized experiences for the participants (Davidson & Cope, 2020). IoT devices, such as smart badges, RFID, facilitate the effective tracking of guests' movements and thus facilitate better space and resource management (Wang et al., 2021). Big data analytics also help in the collection of insights from attendee preferences to enable personalization of the events in the future (Li & Li, 2019). Hybrid and virtual event platforms supported through advanced streaming and interactive technologies have also expanded the scope of MICE events in first reaching global audiences (Richards et al., 2022). Sustainability for MICE events also extends to smart ideas such as energy-efficient venue and digitalizing the documentation processes in its efforts to lessen the carbon footprint associated with the occurrence of the event (Kim et al., 2020). However, smartness in technologies imposes challenges within data privacy, and awareness of the users of that technology, thereby increasing cost (Chen et al., 2023). Therefore, the smart technologies in MICE events are making the tourism industry more innovative and efficient along with being inclusive to the extent of fulfilling the required participant needs and industry demands. 2.4 Challenges Faced by Visitors with the Usage of Smart technologies in MICE Events The challenges to visitors using smart technologies in MICE events are very diverse in nature and are usually more about the fast-paced evolution of such technologies and their integration in a complex event ecosystem. One big challenge is accessibility as well as usability of these technologies for the less tech-savvy participants or those with any disability. The steeper learning curve for new tools, such as event-specific apps or augmented reality systems, might annoy the users and hence decrease their satisfaction (González-Reverté, 2019). Data privacy has also become a challenge with AI-driven analytics and tracking systems because the attendees might feel uneasy about their personal information being collected and used (Ahmad et al., 2023). Interoperability issues also lead to seamless experiences since different smart technologies do not integrate effectively, causing disconnections during events (Kim et al., 2020). Poor digital infrastructure in some venues increases connectivity problems, especially in areas with poor internet services (Day, 2018). Visits may also face issues on the excessive use of smart technologies with the reduction in human interfaces and lack to respond to non-standard or emergency questions (Yoo & Lee, 2020). The cost associated with developing and Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 441 https://internationalpubls.com interacting with expensive smart technologies may become very costly for small events and participants from lower-income levels, leading to inequality within the MICE ecosystem (IEEE, 2021). All these barriers together impact the user experience and make it a requirement to balance innovation with inclusivity, simplicity, and robust support systems. 2.5 Consumers Experience with the Use of Smart Technologies in MICE Events The adoption of smart technologies in MICE events has deeply transformed the consumer experience for efficiency, personalization, and engagement. AI-driven chatbots, event management applications, virtual and augmented reality (VR/AR), and real-time data analytics have streamlined operations and created immersive experiences for attendees (Smith et al., 2021). For example, AI-based tools facilitate customized communication and networking, while AR and VR allow virtual tours of event venues, thereby enhancing pre-event engagement (Jones & Roberts, 2020). Event apps, with live polling and real-time feedback features, have increased participant interaction and enhanced overall satisfaction (Lee et al., 2019). Contactless technologies have improved both safety and convenience during, before, and even after COVID-19 in the context of check-ins, payment processes, and accessing content related to the event (Brown et al., 2022). Still, problems with user adaptability and concerns for data privacy may cause minor complications in seamless adaptation and usage (Nguyen & Tran, 2023). However, despite these barriers, consumers are still reporting better experiences overall, citing improved connectivity, better time management, and the overall value added by smart technologies (Patel, 2021). Therefore, through the adoption of smart technologies, the experience at MICE events is changing, becoming more dynamic, interactive, and satisfying to a new standard for the industry. 3. OBJECTIVES OF THE STUDY • To identify and evaluate the key challenges faced by consumers in adopting smart technologies during MICE events in Delhi-NCR. • To analyze consumer experiences with smart technologies in MICE tourism. 4. RESEARCH METHODOLOGY The research methodology for the study "Adoption of Smart Technologies in MICE Tourism: Understanding Consumer Challenges and Experience in Delhi-NCR" was designed to achieve the two main objectives of the study: (1) to identify and evaluate the key challenges faced by consumers in adopting smart technologies during MICE events in Delhi-NCR, and (2) to analyze consumer experiences with smart technologies in MICE tourism. For the purpose of primary data collection, a structured questionnaire was designed and was administered via Google Forms, targeting individuals who have attended MICE events in the Delhi-NCR region. The questionnaire was circulated via email and other social networking sites using a convenience sampling method that allowed the researchers to collect responses from a willing population. A total of 290 respondents were approached, and out of these, 224 completed the questionnaire, with 220 responses deemed valid and considered for analysis. The questionnaire had been divided into three parts-the first part had elicited the demographic profile of respondents, the second had asked questions to measure challenges arising in the process of consumption regarding the adoption of smart technologies during MICE events, and the third assessed consumer experiences with different smart technologies used in MICE events. The challenge section of the questionnaire asked 12 questions that respondents answered using a 5-point Likert scale ranging Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 442 https://internationalpubls.com from "Strongly Disagree" to "Strongly Agree." In the third section, there were 12 questions on various smart technologies in MICE events and their impact on consumer experiences. The same 5-point Likert scale was used to measure responses. Data for the research was gathered in December 2024. To assure the reliability of the data, Cronbach's Alpha reliability statistics were computed, giving an overall value of 0.824 for the 34 questions, 0.816 for the 12 challenge-related questions, and 0.759 for the 12 questions about consumer experiences with smart technologies, showing acceptable reliability. In order to obtain the first objective, three main factors from adopting smart technologies were obtained using factor analysis. Chi-square was also applied to examine how MICE event types attend their relations with four variables presented in the first factor. This further helped in assessing challenges on an event- by-event basis. For the second objective, descriptive statistics, especially central tendency techniques, were used to calculate the weighted mean for different types of smart technologies applied in MICE events to assess consumer experiences with those technologies. This elaborate research design ensured that the experiences of consumers and challenges they face in adopting smart technologies in MICE tourism in Delhi-NCR were analyzed thoroughly. 5. DATA ANALYSIS AND INTERPRETATION 5.1 Demographic Profile of Respondents Table 1: Demographic profile of respondents Parameter Variable Frequency Percentage Gender Male 121 55 Female 99 45 Age 18-25 47 21.4 26-35 71 32.3 36-45 50 22.7 46-55 22 10 Above 55 Years 30 13.6 Marital Status Single 118 53.6 Married 102 46.4 Prefer not to say -- -- Educational Qualification Intermediate 11 5 Graduate 83 37.7 Postgraduate 56 25.5 Doctorate 36 16.4 Others 34 15.5 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 443 https://internationalpubls.com Occupation Student 8 3.6 Private Job 54 35.5 Government Job 78 24.5 Self-Business 72 32.7 Others 8 3.6 Annual Income Not Earning 17 7.7 Up to 4 Lakhs 6 2.7 4-8 Lakhs 75 34.1 8-12 Lakhs 63 28.6 Above 12 Lakhs 59 26.8 Are you resident of Delhi NCR? Yes 110 50 No 110 50 Where have you attended MICE event? Delhi 104 47.3 Noida/Ghaziabad 56 25.5 Gurugram/Faridabad 60 27.3 Type of MICE event attended Meetings 60 27.3 Incentives 62 28.2 Conferences 44 20 Exhibition 54 24.5 Frequency of attending MICE event Once a year 99 45 2-3 times a year 75 34.1 More than 3 times a year 46 20.9 As shown in Table 1, the gender distribution is relatively balanced, with 55% of the respondents being male (n=121) and 45% female (n=99), reflecting a well-rounded representation of perspectives. The age composition reveals that a significant proportion of respondents fall within the 26-35 age group (32.3%, n=71), followed by those aged 36-45 (22.7%, n=50). Younger participants aged 18-25 constitute 21.4% (n=47), while older groups, including those aged 46-55 and above 55 years, represent 10% (n=22) and 13.6% (n=30), respectively, suggesting a diverse age distribution with a focus on middle-aged individuals. In terms of marital status, the majority of respondents are single (53.6%, n=118), while 46.4% (n=102) are married. Educational qualifications indicate that most respondents are graduates (37.7%, n=83), followed by postgraduates (25.5%, n=56), and a notable number have Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 444 https://internationalpubls.com attained doctorate degrees (16.4%, n=36). Interestingly, 15.5% (n=34) of respondents fall into the "Others" category, potentially including diplomas or professional certifications. Occupation-wise, private job holders comprise the largest segment (35.5%, n=54), closely followed by those engaged in self-business (32.7%, n=72) and government jobs (24.5%, n=78). Students and other occupations are less represented, both accounting for 3.6% (n=8 each). Regarding annual income, a considerable portion of respondents earn between ₹4-8 lakhs (34.1%, n=75), with significant representation from the ₹8-12 lakhs (28.6%, n=63) and above ₹12 lakhs (26.8%, n=59) income brackets. Non-earning individuals and those earning up to ₹4 lakhs constitute 7.7% (n=17) and 2.7% (n=6), respectively, indicating a majority of middle to higher-income respondents. The geographic distribution shows an equal split between Delhi-NCR residents and non-residents, with 50% (n=110) in each category. Among locations for MICE events, Delhi dominates with 47.3% (n=104) of respondents having attended events there, followed by Gurugram/Faridabad (27.3%, n=60) and Noida/Ghaziabad (25.5%, n=56). The type of MICE events attended is evenly distributed, with "Incentives" (28.2%, n=62) and "Meetings" (27.3%, n=60) being the most attended, followed by "Exhibitions" (24.5%, n=54) and "Conferences" (20%, n=44). In terms of frequency, the largest group of respondents (45%, n=99) attends MICE events once a year, while 34.1% (n=75) participate 2-3 times annually, and 20.9% (n=46) engage more than three times a year. This diverse demographic profile highlights the study's inclusiveness, ensuring a broad spectrum of insights into the adoption of smart technologies and associated challenges in MICE tourism within the Delhi NCR region. 5.2 Key Challenges Faced by Consumers in Adopting Smart Technologies during MICE Events in Delhi-NCR The first objective of the study is to identify and evaluate the key challenges faced by consumers in adopting smart technologies during MICE events in Delhi-NCR. Questions to visitors who visited MICE events in Delhi-NCR were asked on 12 parameters of key challenges faced by consumers with the use of smart technologies during MICE events in Delhi-NCR on a five-point likert scale of strongly disagree to strongly agree. Factor analysis is performed to find out the most important dimensions of challenges faced by consumers with the use of smart technologies during MICE events in Delhi-NCR. Table 2: KMO and Bartlett's Test for sampling adequacy and sphericity in analyzing key of challenges faced by consumers with the use of smart technologies during MICE events KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .751 Bartlett's Test of Sphericity Approx. Chi-Square 784.954 df 66 Sig. .000 From the results in Table 2: KMO and Bartlett's Test for sampling adequacy and sphericity, it can be concluded that the data obtained can be used for factor analysis. The Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy value is 0.751, which indicates it is "good" and suggests that the Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 445 https://internationalpubls.com sample can be used to carry out factor analysis. A KMO value above 0.7 is acceptable, and the variables used in the study have adequate correlations for meaningful factor extraction. Furthermore, Bartlett's Test of Sphericity has a Chi-Square value of 784.954 with df of 66 and a Sig. of 0.000. This is a high significant outcome, p < 0.05, implying that the correlation matrix is not an identity matrix, and it means that the variables significantly correlate with each other. In other words, factor analysis is appropriate for the given dataset. In general, conclusion results of the KMO and Bartlett's Test are valid in conducting the factor analysis for exploring major issues faced by consumers to use smart technologies while attending MICE events. Table 3: Total Variance Explained in Factor Analysis of Challenges Faced by Consumers with the Use of Smart Technologies during MICE Events Total Variance Explained Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % 1 4.057 33.810 33.810 4.057 33.810 33.810 2.392 19.932 19.932 2 1.562 13.013 46.823 1.562 13.013 46.823 2.257 18.812 38.744 3 1.176 9.802 56.626 1.176 9.802 56.626 2.146 17.882 56.626 4 .958 7.980 64.606 5 .836 6.963 71.569 6 .774 6.454 78.022 7 .613 5.110 83.132 8 .546 4.550 87.683 9 .465 3.871 91.554 10 .449 3.739 95.293 11 .301 2.505 97.798 12 .264 2.202 100.000 Extraction Method: Principal Component Analysis. Table 3 reports the total variance explained through the factor analysis carried out on the problems consumers face with the help of smart technologies in MICE events. The table is divided into three principal columns: Initial Eigenvalues, Extraction Sums of Squared Loadings, and Rotation Sums of Squared Loadings. These columns help to understand the distribution of variance among different components after the analysis. The column of Initial Eigenvalues shows that the three components Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 446 https://internationalpubls.com explain the maximum amount of variance, as seen in the following: Component 1 explained 33.81%, Component 2 accounted for 13.01%, and Component 3 covered 9.80%. In total, three components explained 56.63% of the variance; therefore, these factors must be significant enough to account for most of the difficulties consumers are facing. The cumulative percentage for the first three components is 56.63%, meaning that after rotation, the remaining components explain less of the variance. Overall, the table above reveals that only a few components were able to define the key challenges identified in the study since the first three components explained more than half of the variance in consumer challenges regarding the use of smart technologies during MICE events. The above insights will help to concentrate on the most significant factors in addressing the challenges faced by consumers while adopting smart technologies in MICE tourism. Table 4: Rotated component matrix of challenges faced by consumers with the use of smart technologies in mice events Rotated Component Matrixa Component 1 2 3 Affordability for small/medium event organizers .818 Attendees' difficulty adapting to smart technologies .662 Integration challenges of smart technologies .613 Trust and reliability concerns with smart technologies .580 Internet connectivity issues in MICE events .740 High cost of implementing smart technologies .641 Privacy concerns with personal data use .624 Accessibility challenges for attendees with disabilities .583 Risk of data breaches in MICE events .764 Technical glitches with smart technologies .737 Language barriers in smart technology use .569 Lack of staff training on smart technologies -- -- -- Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 6 iterations. The table 4 presents the results of a factor analysis conducted to understand the challenges faced by consumers in the adoption of smart technologies during MICE (Meetings, Incentives, Conferences, Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 447 https://internationalpubls.com and Exhibitions) events. The factor analysis resulted in the identification of three key components, which represent distinct dimensions of challenges. The interpretation of each factor is as follows: Factor 1: Affordability and Integration Challenges: The first one can be titled "Affordability and Integration Challenges". This factor encompasses the kinds of challenges faced by organizers and attendees of MICE events in embracing smart technologies, including the financial, adaptability, and reliability kind of challenges. The greatest loading variable was affordability to small/medium event organizers (.818), which signified that financial constraints pose a significant obstacle to these smaller event organizers in embracing smart technologies. With scarce funds, such organizations find it challenging to invest in and implement new technologies. It was challenging for attendees to adjust to smart technologies (.662) indicating that even though they embraced the technologies, they would find it difficult to effectively utilize them, mainly when they were not exposed to the tech. This is further exacerbated by challenges in integration (.613). The existing event infrastructure can be incompatible with new technologies to make seamless adoption difficult. Finally, trust and reliability concerns (.580) signify skepticism about the consistency of these technologies and their dependability, which may prevent full embracing by organizers and audience alike. Collectively, these factors underscore some of the complex issues regarding making smart technologies accessible, effective, and reliable in an MICE event. Factor 2: Connectivity, Cost, and Privacy Concerns: The second factor can be named "Connectivity, Cost, and Privacy Concerns". This factor has brought out the technological, financial, and privacy-related issues that affect the efficient application of smart technologies in MICE events. The highest loading variable, internet connectivity issues in MICE events (.740), reflects how critical a stable and fast internet connection is for the successful deployment of smart technologies. Without secure connectivity, even the best technological solutions will fail to achieve their potential. High cost for the implementation of smart technologies (.641) also suggests that the required infrastructure may not be within the reach of event organizers and will deter adoption, especially for smaller organizations or organizations with limited budgets. Furthermore, the privacy issues when personal data is used (.624) highlight the rise in the awareness and apprehension that both attendees and organizers develop regarding how people's data is collected and stored within smart technology-based applications as a barrier in trust and acceptance. Finally, accessibility challenges for the attendees with disabilities (.583) indicate that even with the advancements in technology, there are gaps in making these technologies all-inclusive for all the attendees, especially those with disabilities. These factors together illustrate multifaceted challenges involving the deployment of technology, privacy of users, and the need for inclusivity in the context of MICE events. Factor 3: Security and Technical Challenges: The third factor can be named "Security and Technical Challenges". This factor summarizes all the concerns about the security and reliability of smart technologies in MICE events in terms of risks, which include data breaches, technical glitches, and language barriers. When this factor is interpreted, it is clear that issues relating to security and operations have a tremendous impact on the adoption and seamless operation of smart technologies during MICE events. The variable "Risk of data breaches in MICE events" had the highest factor loading of 0.764, meaning that this is the largest concern for the fear of hacking of sensitive data by organizers and attendees; it may impact their trust in the technology and decline its use or shy away Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 448 https://internationalpubls.com from using it. The second most important issue is "Technical glitches with smart technologies" (0.737), which reflects concerns about the reliability and performance of smart systems during events. Such glitches can disrupt the event flow, leading to frustrations and dissatisfaction. Lastly, the variable "Language barriers in smart technology use" (0.569) highlights the challenges faced by non-native speakers or attendees unfamiliar with the technological tools used in MICE events. This indicates that due to language incompatibility problems, there will be inadequate communication and difficulties in getting the most from technologies implemented, especially for large and diversified international events. All of these seem to indicate that solving problems related to security, reliability, and languages should be solved to successfully incorporate smart technologies into MICE events. The variable "Lack of staff training on smart technologies" does not have a factor loading value in the rotated component matrix, which means that it was not strongly related to any of the factors identified. This zero-factor loading of the variable "Lack of staff training on smart technologies" may suggest that staff training is not perceived as a significant factor or challenge in the implementation and use of smart technologies for MICE events during this dataset. 5.2.1 Chi Square Analysis between Type of MICE Event Attended and Variables of First factor (Affordability and Integration Challenges) Applying the Chi-square test between the type of MICE event attended (meetings, incentives, conferences, and exhibitions) and the variables of the first factor-which are affordability for small/medium event organizers, the difficulty of attendees to cope with smart technologies, the challenge of integrating smart technologies, and trust and reliability with smart technologies-because they are most relevant to organizational and attendee-related challenges influencing the adoption and use of smart technologies in different types of MICE events. Null Hypothesis: There is no significant relationship between the type of MICE event attended and the challenge of affordability for small/medium event organizers. Table 5: Crosstabulation of Type of MICE Event Attended and Affordability for Small/Medium Event Organizers Crosstabulation Count Affordability for small/medium event organizers Total Strongly Disagree Disagree Neutral Agree Strongly Agree Type of MICE Event Attended Meetings 11 21 4 8 16 60 Incentives 12 18 10 6 16 62 Conferences 8 9 8 4 15 44 Exhibition 6 24 7 7 10 54 Total 37 72 29 25 57 220 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 449 https://internationalpubls.com Table 6: Chi-Square Test Results for Type of MICE Event Attended and Affordability for Small/Medium Event Organizers Chi-Square Tests Value df Asymptotic Significance (2-sided) Pearson Chi-Square 12.205a 12 .429 Likelihood Ratio 12.772 12 .386 Linear-by-Linear Association .016 1 .901 N of Valid Cases 220 a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 5.00. Crosstabulation Table 5 shows the respondents' perception about affordability in terms of percentage for small/medium event organizers for the four categories of MICE events (Meetings, Incentives, Conferences, and Exhibition). For instance, in "Meetings," the maximum number of respondents agreed to the question that affordability is a problem with 16 respondents who strongly agreed and 8 agreed. In "Exhibition," likewise, more respondents disagreed that the challenge of affordability exists because 24 respondents disagreed and 6 strongly disagreed. Chi-square test results give Pearson Chi- Square as 12.205 with a degree of freedom of 12, and p-value (Asymptotic Significance) as 0.429. As the value of p-value is above 0.05 significance level, it accepts the null hypothesis. This shows that there is no statistical significance between the type of MICE event attended and the perception of affordability for small/medium event organizers. Null Hypothesis: There is no significant relationship between the type of MICE event attended and the challenge of attendees' difficulty adapting to smart technologies. Table 7: Crosstabulation of Type of MICE Event Attended and Attendees' difficulty adapting to smart technologies Crosstabulation Count Attendees' difficulty adapting to smart technologies Total Strongly Disagree Disagree Neutral Agree Strongly Agree Type of MICE Event Attended Meetings 15 11 19 12 3 60 Incentives 11 12 19 14 6 62 Conferences 6 8 7 16 7 44 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 450 https://internationalpubls.com Exhibition 4 14 13 19 4 54 Total 36 45 58 61 20 220 Table 8: Chi-Square Test Results for Type of MICE Event Attended and Attendees' difficulty adapting to smart technologies Chi-Square Tests Value df Asymptotic Significance (2-sided) Pearson Chi-Square 17.424a 12 .134 Likelihood Ratio 17.731 12 .124 Linear-by-Linear Association 5.841 1 .016 N of Valid Cases 220 a. 2 cells (10.0%) have expected count less than 5. The minimum expected count is 4.00. Table 7 shows the crosstabulation of the distribution of responses in terms of the difficulty of attendees to adapt with smart technologies by type of MICE events, which consist of meetings, incentives, conferences, and exhibitions. The frequencies are spread along five levels of agreement, with "Neutral" for all types of events at the top. Table 8 displays the results from the chi-square test. The p-value of 0.134 is greater than the significance level of 0.05, indicating that there is no significant relationship between the type of MICE event attended and the challenge of attendees' difficulty in adapting to smart technologies. Although the linear-by-linear association test shows a p-value of 0.016, suggesting some relationship, the overall chi-square test fails to show statistical significance. Hence, the null hypothesis is accepted, stating that the inability of participants to adapt to smart technologies is not significantly different depending upon which type of MICE event was attended. Null Hypothesis: There is no significant relationship between the type of MICE event attended and the Integration challenges of smart technologies. Table 9: Crosstabulation of Type of MICE Event Attended and Integration challenges of smart technologies Crosstabulation Count Integration challenges of smart technologies Total Strongly Disagree Disagree Neutral Agree Strongly Agree Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 451 https://internationalpubls.com Type of MICE Event Attended Meetings 8 15 29 8 0 60 Incentives 16 16 28 2 0 62 Conferences 8 6 23 2 5 44 Exhibition 14 17 12 7 4 54 Total 46 54 92 19 9 220 Table 10: Chi-Square Test Results for Type of MICE Event Attended and Integration challenges of smart technologies Chi-Square Tests Value df Asymptotic Significance (2-sided) Pearson Chi-Square 31.074a 12 .002 Likelihood Ratio 35.772 12 .000 Linear-by-Linear Association .007 1 .936 N of Valid Cases 220 a. 6 cells (30.0%) have expected count less than 5. The minimum expected count is 1.80. Table 9 indicates the distribution of responses with respect to the challenges related to integration of smart technologies across different types of MICE events, which are meeting, incentives, conferences, and exhibitions. Frequencies are different, and majority fall under "Neutral" and "Disagree." Thus, there is mixed response towards the event types. The results of chi-square test are provided in Table 10. The p-value of 0.002 is less than the p-value level of 0.05, and this denotes a statistically significant relationship between the type of MICE event attended and the integration challenges of smart technologies. More support for the conclusion arises with the likelihood ratio test having a p-value of 0.000. Because the null hypothesis would say there is no significant relationship, and the alternative hypothesis would be accepted, the statement would be that there is a significant relationship between the type of MICE event attended and the integration challenges with smart technologies. Null Hypothesis: There is no significant relationship between the type of MICE event attended and the Trust and reliability concerns with smart technologies. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 452 https://internationalpubls.com Table 11: Crosstabulation of Type of MICE Event Attended and Trust and reliability concerns with smart technologies Crosstabulation Count Trust and reliability concerns with smart technologies Total Strongly Disagree Disagree Neutral Agree Strongly Agree Type of MICE Event Attended Meetings 8 18 16 13 5 60 Incentives 15 16 12 17 2 62 Conferences 2 12 11 10 9 44 Exhibition 15 8 14 17 0 54 Total 40 54 53 57 16 220 Table 12: Chi-Square Test Results for Type of MICE Event Attended and Trust and reliability concerns with smart technologies Chi-Square Tests Value df Asymptotic Significance (2-sided) Pearson Chi-Square 30.279a 12 .003 Likelihood Ratio 32.924 12 .001 Linear-by-Linear Association .000 1 .993 N of Valid Cases 220 a. 4 cells (20.0%) have expected count less than 5. The minimum expected count is 3.20. According to the table 11, visitors from events of different kinds have expressed their opinions that had different points on trust and reliability with the smart technology issues. Take a closer look to "meetings" who reported a more significant part of respondents, who responded agreeing or agreeing strictly at 18 in total; another, similar category as that would be for "exhibitions", with a smaller number that disagree or are totally against their arguments at 23. This indicates that there may be different levels of concern toward the trustworthiness and reliability of smart technologies depending on the type of events. Table 12 shows the chi-square test results. The Pearson Chi-Square value is 30.279 with a p-value of 0.003, less than the standard significance level of 0.05. This means that the type of MICE Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 453 https://internationalpubls.com event attended is statistically significant to trust and reliability concerns with smart technologies. Therefore, the null hypothesis stating that there is no significant relationship between the type of MICE event attended and trust and reliability concerns with smart technologies is rejected. The results suggest that the type of event does indeed influence the level of concern regarding the trustworthiness and reliability of smart technologies. Finally, the factor analysis helps in identifying and classifying the key challenges involved in the adoption of smart technologies. The chi-square tests are used to highlight how these challenges vary among different types of MICE events. Together, these help in effectively fulfilling the first objective by providing an all-rounded understanding of the challenges faced by consumers while adopting smart technologies during MICE events in Delhi-NCR. 5.3 Consumer Experiences with Smart Technologies in Mice Tourism The second objective of the study is to examine consumer experiences with smart technologies in MICE tourism in Delhi-NCR. 12 questions on various smart technologies used in MICE events were asked to respondents on a 5-point likert scale of strongly disagree to strongly agree. Descriptive statistics especially central tendency technique is used which calculated weighted mean on various types of smart technologies used and consumers experience with these technologies. Table 13 is indicating the consumers experience with various smart technologies used in MICE events of Delhi- NCR. Results are given in the descending order of mean values. From the descriptive statistics, the consumers differ in their experiences of varied smart technologies used in the MICE events in Delhi-NCR. The mean value is the highest with Beacon Technology at 3.02, followed closely by Wearable Technology at 3.00. These smart technologies that improve attendee navigation and interaction are seen rather positively by consumers. Technology for gamification that serves to boost engagement through various interactive features has a mean of 2.90. This shows moderate satisfaction on the part of consumers. Drones, with a mean of 2.89, also provide great experiences, especially for aerial shot capturing or material delivery. IoT devices, such as smart lighting and climate control, are rated at a mean of 2.84, indicating a fair level of consumer satisfaction with their contribution to event management and convenience. Live Streaming and Hybrid Event Solutions average a mean of 2.76, displaying a middle-of-the-pack consumer experience, perhaps influenced by greater dependence on the technologies within post-pandemic event scenarios. Those interacting through Interactive Displays and Digital Signage are at an average of 2.70, AI and ML average at 2.65, signifying lesser acceptance on an even keel. With regard to adoption or effectiveness, mobile event apps come in with a mean value of 2.64 and Big Data Analytics at a mean 2.62. Virtual Reality and Augmented Reality technologies are at a mean of 2.48, which indicates an unsatisfactory experience compared to other technologies. The least favorable consumer experience is seen with Event Management Platforms and Software, with a mean of 1.75, indicating the lowest level of consumer satisfaction, likely because of technical issues or complexities in usage. Conclusion Smart Technologies: The most satisfactory among them are the first four: Beacon Technology, Wearable Technology, Gamification Technology, and Drones. However, Event Management Platforms and Software, Virtual Reality (VR) and Augmented Reality (AR), and Big Data Analytics were considered least satisfactory. These findings indicate that while some smart technologies are well Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 454 https://internationalpubls.com received in enhancing engagement and convenience, others, especially those relying on complex systems or newer technology like VR/AR, are less likely to be satisfying. Table 13: Descriptive Statistics of Consumer Experience with Smart Technologies Used in MICE Events in Delhi-NCR Descriptive Statistics N Minimum Maximum Sum Mean Std. Deviation Beacon Technology like Indoor navigation, proximity marketing, and real-time notifications 220 1 5 664 3.02 1.576 Wearable Technology like Smart badges, wristbands, or smart glasses for instant networking and access control. 220 1 5 659 3.00 1.312 Gamification Technology like Leaderboards, quizzes, scavenger hunts integrated into event apps for Boosting attendee engagement 220 1 5 638 2.90 1.249 Drones for Capturing aerial footage or delivering materials. 220 1 5 636 2.89 1.333 IoT (Internet of Things) Devices like Smart lighting, climate control devices for Improving event management and attendee convenience 220 1 5 625 2.84 1.285 Live Streaming and Hybrid Event Solutions like Zoom, Microsoft Teams, hybrid event platforms. 220 1 5 607 2.76 1.265 Interactive Displays and Digital Signage for Providing dynamic content and real- time updates 220 1 5 595 2.70 1.285 Artificial Intelligence (AI) and Machine Learning for Enhancing personalization, predicting attendee preferences, and automating tasks. 220 1 5 582 2.65 1.198 Mobile Event Apps for Enhancing attendee engagement and interaction. 220 1 5 580 2.64 1.277 Big Data Analytics for Analyzing attendee behavior, preferences, and event performance. 220 1 5 576 2.62 1.257 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. ICMASD (2025) 455 https://internationalpubls.com Virtual Reality (VR) and Augmented Reality (AR) for virtual site inspections, product demonstrations, or interactive sessions. 220 1 5 546 2.48 .953 Event Management Platforms and Software like Eventbrite, Cvent, Hopinlike for streamlining events 220 1 5 385 1.75 1.234 Valid N (listwise) 220 6. CONCLUSIONS The research on adopting smart technologies in MICE tourism in Delhi-NCR provides insight into the challenges and customer experiences of these technologies. The first goal revealed three main challenges identified by consumers in adopting the smart technologies: affordability issues and integration problems, the connectivity and privacy concerns about the smart technologies, and security and technical challenges relating to smart technologies. The findings indicated that financial constraints and inability to adapt to new technologies were major barriers, especially for small or medium-sized event organizers. Concerns about internet connectivity, privacy, and security risks such as data breaches were also prevalent among both event organizers and attendees. Chi-square tests also revealed significant relationships between the type of MICE event attended and certain challenges, such as integration and trust issues, which means that the type of event influences the consumer's experience with smart technologies. The second objective focused on analyzing consumer experiences with smart technologies used in MICE events. The results were mixed, though Beacon Technology and Wearable Technology were found to have the highest satisfaction level, thereby indicating their efficiency in strengthening attendee engagement and interaction. Conversely, the lowest-rated areas for satisfaction were with Event Management Platforms and Software and Virtual Reality and Augmented Reality. Thus, apparently, these technologies still remain somewhat unembraced because of challenges faced in usage and efficiency by end-users. 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