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Hospitality Industry

1. " Impact of IoT Integration on Customer Satisfaction in the Hospitality Industry " - Dr . Deepti Masand Sharma - Dr . Nitu Singh Sisodia Assistant Professor’S , Prestige Institute of Management and Research Indore

2. Abstract This research paper probes the transformational role of the Internet of Things (IoT) in enhancing customer engagement within the hospitality industry. This amalgamation of for enhancing guest engagement as a customer moreover as a consumer for engagement and upgrading services to meet customer satisfaction and expectation. As customer expectations continue to evolve, hotels are increasingly adopting Internet of Things (IoT) technologies—including smart room features such as smart lighting, climate control, mobile-controlled amenities, voice-activated assistants, and real-time service automation—to provide personalized and seamless guest experiences.

3. Abstract The absence or less integration of technology may not draw up day to day operations however it impacts significantly optimization of resources customization and responsiveness The hospitality enterprise is undergoing a first-rate transformation fuelled by using the fast A d vancement of digital technology. amongst those, the net of things (IoT) has emerged as a pivotal innovation this is reshaping the way inns interact with their guests . This observe explores how IoT complements patron engagement through turning in personalized, seamless, and green offerings within hotel environments.  

4. Literature Review Several studies have explored the application of IoT in enhancing purchaser engagement inside the hospitality quarter. Kumar and Jaiswal (2024) examined the impact of IoT-supported services along with sensible lights and voice-managed assistants on premium Indian motels, finding that personalized guest reviews brought about a 35% increase in pleasure. assisting this, Lopez and Velez (2024) performed research in EU smart accommodations and concluded that IoT -pushed emotional personalization notably stimulated repeat reservations . Wang (2023) argued that carrier automation through IoT enhances consistency and lets in motels to offer predictive offerings tailored to visitor wishes. From a loyalty angle, Natarajan and Mehta (2022) determined a significant upward thrust in loyalty software enrollments in hotels the usage of cellular-enabled rooms and digital reception technology. meanwhile, Huang and Lin (2022) evolved a pride index model linking IoT adoption to logo loyalty, confirming that technologically superior offerings foster more potent emotional connections.

5. RESEARCH FRAME Research Questions How does IoT affect guests' satisfaction during their stay? How does IoT affect the personalization of guests' experience in hotels? Research Objectives 1. Analyzing the Impact of IoT Offerings on Client Engagement in the Hospitality Industry 2. Assessing How Personalization Enabled by IoT Enhances Guest Satisfaction and Ensures Safety 3. Exploring the Relationship Between IoT Features, Guest Behavioral Intentions, Loyalty, and Repeat Visits

6. Hypotheses of the Study Research Question 1: How does IoT impacts guests' satisfaction during their stay? 🔸 Hypothesis 1 (H1): H0 ₁ (Null): IoT services have no significant impact on guest satisfaction during their stay . H1 ₁ (Alternate): IoT services significantly enhance guest satisfaction during their stay Research Question 2: How does IoT affect the personalization of guests' experience in hotels? 🔸 Hypothesis 2 (H2): H0 ₂: There is no significant difference in perception of IoT -enabled personalization based on education level. H1 ₂: There is a significant difference in perception of IoT -enabled personalization based on education level.

7. Hypotheses of the Study Research Objective 3: Explore the relationship between IoT functions and behavioural intentions (loyalty, repeat visits) 🔸 Hypothesis 3 (H3): H0 ₃: IoT services have no significant effect on customers’ behavioural intentions such as loyalty and revisit intention. H1 ₃: IoT services significantly influence behavioural intentions like loyalty and repeat visits. Research Objective 4: Designing ways to increase customer connection with smart technologies 🔸 Hypothesis 4 (H4): H0 ₄: Customizing IoT offerings based on guest segments (e.g., education level) does not impact customer engagement. H1 ₄: Tailoring IoT offerings to guest segments increases customer engagement.

8. The conceptual model of the study Here is the structure of the conceptual model- Independent variable: Internet of Things ( IoT ) (Includes: Intelligent room controls, check-in/check-in/check-in IoT , mobile room service, occupancy sensors, etc.) Mediating variables: The personalization of experience Efficiency Dependent variables: Involvement of customers

9. The conceptual model of the study 1: IOT implementation (frame 1 - independent variable) : This is the default model of the model. IoT refers to the integration of intelligent internet -connected devices into hotel operations. They can include: Intelligent room technology (e.g. automated lighting, temperature, voice assistants) Check-in/check-out-out IOT Smart locks and access to the keyless room Personalized room settings via guests' profiles These innovations help hotels to offer a modern, technically controlled guest experience.

10. 2.Enhancement of Customer Experience (Frame 2 - Mediating variables): IoT implementation increases two key intermediary aspects: Personalization of experience IoT allows hotels to adapt services for guests, for example: Remembering previous preferences Editing room settings automatically Tailor -made offer and room upgrades This level of personalization contributes directly to the deeper satisfaction of guests and the emotional connection with the brand. Operating efficiency IoT facility makes the hotel operations from: Power consumption automation Acceleration of maintenance and maintenance warnings Shortening waiting time for services Improved operational performance indirectly increases the perception and satisfaction of customers. 3. Results of customer wiring (frame 3 - dependent variables): The third field is the final results resulting from the IoT -controlled improvements: A . Customer involvement Guests feel more interconnected and involved because of technologically supported services, leading to increased interaction, feedback and loyalty behaviour. B. Customer satisfaction Increased speed, comfort and personalization significantly increases the overall satisfaction of guests. C. Customer loyalty Satisfied and engaged guests will return more often, recommend the hotel and publish positive reviews-which increases long-term profitability.

11. SAMPLING CHARACTERISTICS Population : guests and customers who experienced services supporting IoT in hotels. Target group : Individuals who stayed in intelligent hotels or individual IoT services such as intelligent check-in, intelligent rooms, voice assistants, automated lighting and mobile applications. Sampling technique: The comfort sample was used for easy availability and time restrictions. Sample size: A total of 150 respondents were examined via the Google online form.

12. Methodology : The present observe adopts a quantitative studies layout aimed toward analyzing the effect of IoT on patron engagement inside the hospitality industry. Facts turned into amassed through a based questionnaire administered on-line through Google forms to a sample of 120 motel visitors who had skilled IoT-enabled offerings in Smart city motels . A comfort sampling method turned into used, considering accessibility and time constraints. The questionnaire consisted of both demographic gadgets and 5-point Likert-scale statements assessing delight, personalization, and engagement ranges. The number one fact was supplemented by means of secondary sources together with peer- reviewed journals, industry reviews, and published articles. Statistics evaluation changed into conducted the use of Microsoft Excel and SPSS, applying descriptive data and chi-square assessments to decide the connection among IoT adoption and purchaser engagement variables. Ethical suggestions were accompanied, with respondents collaborating voluntarily and anonymously. The method changed into designed to provide empirical proof supporting the look at its targets and to draw significant insights into the function of IoT in enhancing personalized reviews and patron loyalty in hospitality settings

13. DATA COLLECTION Primary facts for the observe turned into gathered via a based online questionnaire designed the use of Google paperwork. The survey focused folks that had these days stayed at IoT-enabled hotels in Smart Cities. A total of a hundred and twenty legitimate responses were acquired the use of convenience sampling, ensuring a applicable and accessible respondent pool. The questionnaire blanketed demographic info and Likert-scale gadgets centered on IoT features, guest satisfaction, personalization, and engagement. Secondary statistics was sourced from scholarly journals, industry whitepapers, and former research studies to support and contextualize the number one finding . Primary data: Reaction of the survey of hotel guests. Secondary data: Academic magazines, articles, hospitality reports and case studies.

14. Tools and techniques for data analysis   The data collected has been analysed using Microsoft Excel and SPSS for descriptive statistical techniques such as: Frequency distribution, Percentage analysis, Average and standard deviation, Cross -capacity Correlation and regression analysis, (if necessary for advanced interpretation ). Methodology restrictions The use of comfort sampling can reduce the generalization of the results. The size of the sample does not have to reflect the whole demographic diversity of consumers of hospitality. Some respondents may have limited understanding of IoT services, which leads to biased answers. The data reported separately are subject to personal interpretation and distortion.

15. Results Analysis and Discussion Descriptive analysis : Chart 1

16. Interpretation of Findings:   Engagement developments: High way for Q6 (four. Fifty-one) and Q8 (four.64) endorse that respondents view the factors associated with those questions definitely. If Q6 and Q8 are related to particular functions of IoT in hospitality, it can mean sturdy client engagement with these technology or offerings. Variability in Responses: Questions like Q8 show great variability (as evidenced via both the excessive well-known deviation and variety). this may indicate differing stages of cognizance, enjoy, or expectancies amongst respondents concerning the IoT functions inside the hospitality enterprise. Impact of Outliers: Severe outliers in a few responses (e.g., Q8) can skew the overall results, creating great variations among the imply and the median. It’s vital to observe those outliers to apprehend if they represent precise instances (e.g., a small organization with very different experiences).

17. Correlation Matrix:

18. Interpretation: Age and Technology Engagement IoT Adoption (r = 0.12) and Customer Engagement (r = 0.25) both show a weak positive correlation with age. This suggests that older individuals may adopt IoT technologies and engage with services slightly more , but the influence is not substantial . Gender and Behavioral Influence Negligible correlation between Gender and IoT Adoption ( r = -0.05 ) indicates no meaningful relationship . A mild positive correlation with Customer Engagement ( r = 0.18 ) implies some gender-based differences in service interaction , though not dominant

19. Key Findings : Occupation’s Role Moderate positive correlation with IoT Adoption ( r = 0.34 ) and Customer Engagement ( r = 0.42 ). Indicates that certain professions may be more comfortable with or reliant on technology , thus interacting more with IoT -driven services . Income as a Driving Factor Income shows a stronger relationship with both IoT Adoption ( r = 0.46 ) and Customer Engagement ( r = 0.50 ). Suggests that higher-income individuals are more likely to adopt IoT technology and engage meaningfully with it, possibly due to greater access, awareness, and expectations Strong Link Between IoT Adoption & Customer Engagement A high positive correlation (r = 0.62) exists between IoT Adoption and Customer Engagement . This reinforces the idea that as IoT tools are adopted, customer interaction and satisfaction significantly increase , emphasizing the strategic importance of investing in IoT infrastructure .

20. STRONG OBSERVATIONS The statistical and correlation analysis reveals meaningful insights into the dynamics between IoT adoption and customer engagement within the hospitality industry: IoT adoption is strongly correlated with customer engagement (r = 0.62), validating the strategic value of IoT investments for enhancing guest experience. Among demographic variables, income (r = 0.50) and occupation (r = 0.42) demonstrate moderate positive correlations with customer engagement, suggesting that economic and professional background influence technology interaction. Age shows only a weak positive correlation , and gender shows negligible correlation , indicating minimal influence on general adoption or engagement. However, gender differences were observed specifically in the perception of personalization , with females perceiving higher value in tailored services — a key insight for UI/UX and service design teams. High variance and skewness in some responses (especially Q8) imply the presence of outliers , possibly reflecting differing digital maturity across user segments.

21. Interpretation: The correlation analysis reveals several key insights into the relationships between variables. Age shows a weak positive correlation with both IoT adoption (r = 0.12) and customer engagement (r = 0.25), indicating that as age increases, there is a slight increase in both IoT adoption and engagement, but the relationship is not strong. Gender shows no significant correlation with IoT adoption (r = -0.05) and a small positive correlation with customer engagement (r = 0.18), suggesting minimal impact. Occupation has a moderate positive correlation with both IoT adoption (r = 0.34) and customer engagement (r = 0.42), indicating that certain occupations are more likely to adopt IoT technologies and have higher engagement. Income shows a moderate positive correlation with both IoT adoption (r = 0.46) and customer engagement (r = 0.50), suggesting that individuals with higher income are more likely to adopt IoT and engage with services. Lastly, IoT adoption and customer engagement have a strong positive correlation (r = 0.62), indicating that as IoT adoption increases, customer engagement tends to increase significantly, highlighting the strong link between these two factors in the hospitality industry

22. Independent Samples t-Test:

23. Interpretation: There is a significant difference between males and females only in their perception of IoT enabling better personalization, with females rating it higher. This indicates that gender influences how IoT is perceived in terms of tailored experiences. For other factors like customer satisfaction, service efficiency, communication, and overall engagement, there is no significant gender-based difference, suggesting a generally uniform perception across genders . Uniformity Across Other Service Aspects For other dimensions—such as: Customer satisfaction Operational efficiency Communication quality Overall engagement ...no significant gender-based differences were found. This suggests a largely uniform perception across genders regarding general service outcomes and the role of IoT in enhancing them.

24. One-Way ANOVA Test: One-Way ANOVA Test:

25. RESULTS Research Question 1: How does IoT affect guests' satisfaction during their stay? 🔸 Hypothesis 1 (H1): H0₁ (Null): IoT services have no significant impact on guest satisfaction during their stay. H1₁ (Alternate): IoT services significantly enhance guest satisfaction during their stay. ✅ Data Insight: F = 1.78, p = 0.157 → Not significant ✅ Result: Fail to reject H0₁ . ➡ Guests across education levels perceive IoT as similarly impactful on satisfaction; education does not significantly alter this perception.

26. RESULTS Research Question 2: How does IoT affect the personalization of guests' experience in hotels? 🔸 Hypothesis 2 (H2): H0₂: There is no significant difference in perception of IoT -enabled personalization based on education level. H1₂: There is a significant difference in perception of IoT -enabled personalization based on education level. ✅ Data Insight: F = 3.75, p = 0.014 → Significant ✅ Result: Reject H0₂ ➡ Education level does influence how guests perceive personalization features through IoT .

27. RESULTS Research Objective 3: Explore the relationship between IoT functions and behavioural intentions (loyalty, repeat visits) 🔸 Hypothesis 3 (H3): H0₃: IoT services have no significant effect on customers’ behavioural intentions such as loyalty and revisit intention. H1₃: IoT services significantly influence behavioural intentions like loyalty and repeat visits. ✅ Note: No direct ANOVA data was shared for loyalty/repeat intention — you'd need survey responses and regression or correlation analysis to test this hypothesis.

28. RESULTS Research Objective 4: Designing ways to increase customer connection with smart technologies 🔸 Hypothesis 4 (H4): H0₄: Customizing IoT offerings based on guest segments (e.g., education level) does not impact customer engagement. H1₄: Tailoring IoT offerings to guest segments increases customer engagement. ✅ Data Insight (Overall Engagement): F = 1.12, p = 0.349 → Not significant ✅ Result: Fail to reject H0₄ ➡ Overall perceived engagement is not significantly influenced by education level

29. Recommendations for IoT Integration in Hotels 1. Gradual Implementation Strategy Start Small : Begin with foundational IoT applications such as smart lighting, digital room keys, and mobile check-ins. Scalable Approach : A phased rollout allows better management of investment, technical challenges, and staff adaptation 2. Focus on Personalization Guest-Centric Services : Prioritize IoT applications that personalize guest experiences (e.g., profile-based settings, service customization, and AI-driven recommendations). Enhanced Satisfaction : Personalized services improve guest satisfaction and loyalty

30. Recommendations for IoT Integration in Hotels 3. Employee Training and Guest Orientation Staff Empowerment : Train employees thoroughly to operate and support IoT -enabled services. Guest Awareness : Educate guests on using IoT features during the check-in process to increase utilization and satisfaction . 4. Segment-Based Offering Customization Guest Segmentation : Recognize varying comfort levels with technology across guest demographics. Flexible Packages : Offer differentiated options such as "Tech-Light" for traditional guests and "Tech-Savvy" for digital-native travelers

31. THANK YOU