INTENTION TO USE SMART APPS FOR VISITING

Tekspenuh

(1)MUSEUM AMONG YOUTH IN MALAYSIA By. FAMELLA VANESSA ANAK UYONG (H18A0112) FARAH NADIA BINTI SUHIMI (H18A0113) NURFARISHA BINTI FAUZI (H18A0444) SOLEHAH BINTI ISMAIL (H18A0612). Bachelor of Entrepreneurship (Tourism Entrepreneurship). A report submitted in partial fulfilment of the requirements for the degree of Bachelor of Entrepreneurship (Tourism Entrepreneurship). Faculty of Hospitality, Tourism and Wellness UNIVERSITY MALAYSIA KELANTAN 2021. FYP FHPK. INTENTION TO USE SMART APPS FOR VISITING.

(2) I hereby certify that the work embodied in this report is the result of the original research and has not been submitted for a higher degree to any other University or Institution. OPEN ACCESS. I agree that my report is to be made immediately available as hardcopy or on-line open access (full text). CONFIDENTIAL (Contains confidential information under the Official Secret Act 1972) * RESTRICTED. (Contains restricted information as specified by the organization where research was done)*. I acknowledge that Universiti Malaysia Kelantan reserves the right as follow. The report is the property of Universiti Malaysia Kelantan The library of Universiti Malaysia Kelantan has the right to make copies for the purpose of research only The library has the right to make copies of the report for academic exchange. Certified by. Signature of Supervisor. Signature. Name:TS DR SITI SALINA SAIDIN Date: 20 June 2021. Group Representative: Farah Nadia binti Suhimi Date: 20 June 2021. Note: * If the report is CONFIDENTIAL OR RESTRICTED, please attach the letter from the organization stating the period and reasons for confidentially and restriction. ii. FYP FHPK. DECLARATION.

(3) First of all, the success and final outcome of this research required a lot of guidance and assistance from many people helping us to complete this research study. Their participation had given a lot of helps and supports to us so we can do efficiently in this research study. We would like to express our gratefulness and appreciation to our supervisor, Dr Siti Salina Binti Saidin for supervising us to complete this project. Without her helps and the knowledge she had been shared with us, we cannot complete this project in time and efficiently. Moreover, we would like to reveal our thanks to our group members who giving full commitment and passion while doing this research study. Group member’s Farah, Farisha, Famella, and Solehah. Without each other’s cooperation, understanding and tolerate, this project could not be finished on time. We would also like to thank to our family who gave full support to our study in Universiti Malaysia Kelantan (UMK). Last but not least, we are very grateful to Universiti Malaysia Kelantan (UMK) for giving us this opportunity to run this research project.. iii. FYP FHPK. ACKNOWLEDGEMENTS.

(4) Page i. TITLE PAGE DECLARATION. ii. ACKNOWLEDGEMENTS. iii. TABLE OF CONTENTS. iv. LIST OF TABLES. v. LIST OF FGURES. vi. LIST OF SYMBOL & ABBREVIATIONS. vii. ABSTRACT. viii. ABSTRAK. ix. CHAPTER 1: INTRODUCTION 1.1. Background of the Study. 1-2. 1.2. Problem Statement. 2-4. 1.3. Research Objectives. 5. 1.4. Research Questions. 5-6. 1.5. Scopes of Study. 1.6. Significant of Study. 6-7. 1.7. Definition of Terms. 7-8. 1.8. Summary. 6. 9. CHAPTER 2: LITERATURE REVIEW 2.1. Introduction. 10. 2.2. The Unified Theory of Acceptance and Use of Technology. 10-12. (UTAUT) 2.3. Intention to Use Smart Apps. 12-14. 2.4. Performance Expectancy. 15-16. 2.5. Effort Expectancy. 16-17. 2.6. Social Influence. 17. 2.7. Facilitating Conditions. 18. 2.8. Conceptual Framework. 19. 2.9. Hypothesis. 20. 2.10 Summary. 20-21. iv. FYP FHPK. TABLE OF CONTENTS.

(5) 3.1 Introduction. 22. 3.2 Research Design. 22-23. 3.3 Population. 23. 3.4 Sample Size. 24-25. 3.5 Sampling Method. 26. 3.6 Data Collection Method. 26-27. 3.7 Research Instruments. 27-29. 3.7.1 Questions Used in Section A of the Questionnaire. 29-30. 3.7.2 Questions Used in Section B of the Questionnaire. 30. 3.7.3 Questions Used in Section C of the Questionnaire. 31. 3.7.4 Questions Used in Section D of the Questionnaire. 31-32. 3.7.5 Questions Used in Section E of the Questionnaire. 32. 3.7.6 Questions Used in Section F of the Questionnaire. 32-33. 3.8 Data Analysis. 34-37. 3.9 Summary. 37. CHAPTER 4: FINDINGS AND DISCUSSION 4.1 Introduction. 38. 4.2 Reliability Analysis. 38-40. 4.3 Demographics Characteristics of Respondents. 40-45. 4.4 Descriptive Analysis. 46. 4.4.1 Independent and Dependent Variables. 46. 4.4.2 Performance Expectancy. 47-48. 4.4.3 Effort Expectancy. 48-49. 4.4.4 Social Iflluence. 49-50. 4.4.5 Facilitating Condition. 50-51. 4.4.6 Intention to Use Smart Apps. 51-52. 4.5 Pearson Correlation Coeffiiecient. 52-56. 4.6 Framework Analysis. 57-58. 4.7 Summary. 58. v. FYP FHPK. CHAPTER 3: METHODOLOGY.

(6) 5.1 Introduction. 59. 5.2 Recapitulation of Study. 59-61. 5.2.1 Research Question 1. 61-62. 5.2.2 Research Question 2. 62-63. 5.2.3 Research Question 3. 63. 5.2.4 Research Question 4. 63-64. 5.3 Findings and Discussion. 64-65. 5.4 Limitation. 66. 5.5 Recommendation. 67-68. 5.6 Conclusion. 68-69. REFERENCES. 70-73. APPENDICES. 74-79. vi. FYP FHPK. CHAPTER 5: CONCLUSION.

(7) Tables. Title. Table 3.1. Table for Determining Sample Size From a Given. Page 25. Population Table 3.2. Measurement of 5-Point Likert Scale. 28. Table 3.3. Questionnaire Composition. 29. Table 3.4. Questions Used in Section A of the Questionnaire-. 30. Demographic Table 3.5. Questions Used in Section B of the Questionnaire-. 31. Intention to Use Smart Apps Table 3.6. Questions Used in Section C of the Questionnaire-. 32. Performance Expectancy Table 3.7. Questions Used in Section D of the Questionnaire-Effort. 33. Expectancy Table 3.8. Questions Used in Section E of the Questionnaire-Social. 34. Influence Table 3.9. Questions Used in Section F of the Questionnaire-. 34-35. Facilitating Condition Table 3.10. Rule of Thumb for Interpreting the Size of a Correlation of Correlation Coefficient. Table 4.1. Rules of Thumb of Crobach’s Alpha Coefficient Size. Table 4.2. Result of Reliability Coeffiecient Alpha for the Independent Variables and Dependent Variables. Table 4.3. Number of Resopondents by Gender. Table 4.4. Number of Resopondents by Age. Table 4.5. Number of Resopondents by Maritul Status. Table 4.6. Number of Resopondents by Occupation. Table 4.7. Descriptive Statistics. 37. FYP FHPK. LIST OF TABLES.

(8) Descriptive Statistics for Performance Expectancy. Table 4.9. Descriptive Statistics for Effort Expectancy. Table 4.10. Descriptive Statistics for Social Influence. Table 4.11. Descriptive Statistics for Facilitating Condition. Table 4.12. Descriptive Statistics for Intention to Use Smart Apps. Table 4.13. Rule of Thumb for Interpreting the Size of a Correlation of Correlation Coefficient. Table 4.14. Correlation Coefficient for the Performance Expectancy Factor and Intention to Use Smart Apps. Table 4.15. Correlation Coefficient for the Effort Expectancy Factor and Intention to Use Smart Apps. Table 4.16. Correlation Coefficient for the Social Influence Factor and Intention to Use Smart Apps. Table 4.17. Correlation Coefficient for the Facilitating Condition Factor and Intention to Use Smart Apps. Table 5.1. Summary of Correlation Analysis. vii. FYP FHPK. Table 4.8.

(9) Figures. Title. Page. Figure 2.1. UTAUT Model. 12. Figure 2.2. The Conceptual Framework of this Study. 19. Figure 4.1. Percentage of Repondents by Gender. Figure 4.2. Percentage of Respondents by Age. Figure 4.3. Percentage of Respondents by Maritul Status. Figure 4.4. Percentage of Respondents by Occupation. Figure 4.5. Correlation between Performance, Effort, Social Influence, Facilitating Condition and Intention to use Smart Apps. viii. FYP FHPK. LIST OF FIGURES.

(10) Abbreviations AR. Augmented Reality. VR. Virtual Reality. MCO. Movement Control Order. SOP. Standard Operating Behaviour. PE. Performance Expectancy. EE. Effort Expectancy. SI. Social Influence. FC. Facilitating Condition. vii. FYP FHPK. LIST OF SYMBOLS & ABBREVIATIONS.

(11) This research investigates the intention to use Smart Apps for visiting museum among youth in Malaysia. Due to the rapidly growing and advanced tourism industry, there are various advanced technologies created to meet the needs and demands of the tourism industry. Among them are smart application technology (Smart Apps), which include Augmented Reality (AR) and Virtual Reality (VR). This Smart Apps was created to make it easier for users or tourists to travel locally or abroad. By using this Smart Apps, users can interact or obtain information and data in the form of a 3D world. This research examines factors such as performance expectancy, effort expectancy, social influence, facilitating condition in order to gain data, seeking and determine either the variables collected could be linked between intentions to use Smart Apps among youth in Malaysia. To reach the analysis, a quantitative research was carried out. The respondent of this study was conducted with 200 respondents who are youth in Malaysia. For the analysis of data, reliability test and Pearson correlation would be used. As a result, researcher analysis shows moderate positive correlation between variable factors and intention to use Smart Apps among youth. Through the knowledge of this research, it might be useful for the tourism industry especially museum in increasing and upgrade the quality of the museum systems in Malaysia. Keywords : Smart Apps, augmented reality, virtual reality, mobile apps, intention to use Smart Apps.. viii. FYP FHPK. ABSTRACT.

(12) Penyelidikan ini dijalankan adalah untuk mengkaji berkenaan kecenderungan untuk menggunakan Aplikasi Pintar dalam melawat muzium di kalangan belia di Malaysia. Oleh kerana industri pelancongan yang makin berkembang pesat dan maju, terdapat pelbagai teknologi canggih telah dicipta untuk memenuhi keperluan dan permintaan dalam industri pelancongan. Antaranya adalah teknologi aplikasi pintar (Smart apps), yang termasuk ‘Augmented Reality’ (AR) dan ‘Virtual Reality’ (VR). Aplikasi Pintar ini dicipta bagi memudahkan pengguna atau pelancong untuk mengembara di dalam atau di luar negara. Selain itu, menggunakan ‘Smart apps’ ini, pengguna dapat berinteraksi atau memperoleh maklumat dan data dalam bentuk dunia 3D. Penyelidikan ini mengkaji faktor-faktor seperti jangkaan prestasi, jangkaan usaha, pengaruh sosial, memudahkan keadaan, untuk mendapatkan hasil data penyelidikan ini, mencari dan menentukan sama ada pembolehubah yang dikumpulkan boleh dikaitkan antara dan niat untuk menggunakan Aplikasi Pintar di kalangan belia di Malaysia. Untuk mencapai analisis, penyelidikan kuantitatif telah dijalankan. Kajian ini dijalankan dengan 200 responden yang merupakan belia di Malaysia. Untuk analisis data, ujian kebolehpercayaan dan korelasi Pearson akan digunakan. Hasilnya, analisis penyelidik menunjukkan hubung kait positif yang sederhana antara faktor pembolehubah dan niat untuk menggunakan Aplikasi Pintar dalam kalangan belia. Melalui pengetahuan, kajian yang dijalankan ini mungkin berguna untuk industri pelancongan terutamanya muzium dalam meningkatkan dan meningkatkan kualiti sistem muzium di Malaysia. Kata Kunci : Smart Apps, realiti berperantara, realiti maya, aplikasi mudah alih, hasrat menggunakan Smart Apps.. ix x. FYP FHPK. ABSTRAK.

(13) INTRODUCTION. 1.1 BACKGROUND OF STUDY. Tourism is one of the quickest growing economic sectors in the world. There are two billion millennials that represent 25% of the world’s population, redefining consumerisation. Growth from Knowledge (GfK), a market research company studied that 59% of 20 to 29 years old and 57% of 30-39 years old agreed with the statement that experiences are more important than possessions. Millennials or youth nowadays prefer to socialise in group engagement at a fraction of the cost instead of investing in personal highly of Smart Apps such as Virtual Reality (VR) applications and Augmented Reality (AR) because it is also a part of a technology that the youth has been possessed. The goal of augmented reality is to make the user's life easier by introducing virtual information not just to his immediate surroundings, but also to any indirect view of the real-world environment, such as a live-video feed. The user's perspective of and interaction with the real environment is improved through augmented reality. Carmini, J, Furht, B., Anisetti, M, Ceravolo, P., Damiani, E., & Ivkovic. (2010) AR let us see the real-life scene right in fronts of us like a tree swaying in the park, dogs chasing balls and other things. Over recent years, augmented reality has increasingly become popular in the travel industry. This is because it facilitates hotels and other businesses operating in this field to enhance the physical environments, they are trying to assist customers with, including local sights and hotel rooms. Hobson and Williams (1995) asserted that travel. 1. FYP FHPK. CHAPTER 1.

(14) Tussyadiah, Jung, & Tom Dieck (2018), AR creates an enhanced user experience. It is widely recognised as a useful tool to improve interaction with and perception of the realworld environment. Previous research has also examined AR’s perceived value within the cultural heritage context (Tom Dieck & Jung, 2017). Virtual reality (VR) is being informed as to the next big step in technological innovation. Bruno, Bruno, De Sensi, Luchi, Mancuso, & Muzzupappa (2010) stated that VR had been actively adopted in cultural tourism because some of its features help achieve the tourism industry’s goal of providing tourists with unique and enhanced experiences. It also reduces the distance between potential tourists and a definition by giving information and magnifying their understanding of a destination before their actual visit. The role of VR is becoming increasingly important in the museum context because it is helping the museums overcome some major issue they faced such as to enhance the visitors' knowledge by providing edutainment (governance of education and entertainment). However, studies on using Smart Apps within the tourism context are limited, and in previous research, museums are still at an early stage.. 1.2 PROBLEM STATEMENT. The Warsaw Tourism Institute has researched the number of young people visiting the museum, and it confirms that the number of those visiting the museum is slight. According to Bożena Alejziak (2011), the young people or youth of the present day argue that the museum is considered a "temple" that stores many national artefacts or treasures related to cultural, customary and legal history. The writer also pointed out that. 2. FYP FHPK. itself is in no small extent a secondary reality, which the tourist escapes into briefly..

(15) the museum. Most youth and children visit the museum simply because they have to disarm the school trip’s obligatory schedule. For youth, museums are places to take a picture and a noisy place during weekends (Mutia, 2020). Most youth nowadays more appreciate the memory of a photograph than knowledge. They intend to take more pictures other than learning. Due to the museum’s noisy atmosphere, people will also feel disturbed and the result influenced them not to go to the museum. In early 2020, Malaysia and almost all over the world had experienced a situation that made it difficult for each individual to freely move due to the covid-19 pandemic. This caused many tourism industries to be affected, such as airlines, hotels, travel agencies and especially museums. The pandemic in Malaysia has forced the museum to be closed following Movement Control Order (MCO) conducted in March which it was issued by Malaysian Prime Minister, Tan Sri Muhyiddin Yassin (Xavier Kong, 2020) and started operating after a few months. Still, the museum must comply with Standard Operation Procedures (SOP) and forced to take care of the distance between visitors to stop mass gathering among people and prevent infection from occurring. This situation caused an increasing number of individuals who did not want to visit the museum. According to Les Shu (2015) British Museum, London already created an education program by inventing Augmented Reality to let people explore and wander through the museum in a virtual world. Unlike other countries, in Malaysia, smart applications such as AR and VR have not been used by any museum. The researcher also examined that the absence of Smart Apps’ uses would make it difficult for the museum to get more visitors.. 3. FYP FHPK. youth are now not interested in visiting the museum because it is a durable place inside.

(16) physical harm. There is still a possibility that this application is harmful to itself due to the lack of attention to the application. The app can distract individuals who use it from the real world. This situation may pose a danger to the user. For example, the Pokemon Go game has raised some news about the accident that occurred to its users as a result of the individual's complacent or negligence when playing this game. It is also stated that the individual who has received a lot of an accident is the youth. According to Anna Johansson (2018), there will be social effects and user isolation while using virtual reality apps. This is because technology is capable of forming an addiction within a person. Some individuals are so obsessed with social media and video games until they isolate themselves from society to an unhealthy level. Despite all the problems, researchers, discussing the acceptance of Malaysian society mostly the young, are willing and agreeing if the museum uses smart application platforms such as AR and VR used, on visits to museums and also what are the behavioural results when implementing the smart application in the tourism industry. The recent technological advancements, especially involving mobile apps, have enabled people to do things work efficiently. However, to ensure that the new implementation is successful, technological development in the visitor industry, that is, smart applications are essential to understand the needs of consumers from the point of view. Therefore, this study investigates the needs and impact of smart apps, especially AR and VR, to attract more youth visitors to visit museums in Malaysia.. 4. FYP FHPK. Other than that, Volodymyr Bilyk (2018) also states, there are possibilities of.

(17) The research question is consequently derived from the problem statement, which is formulated to guide the research issues and to identify the concern addressed by the research study. In simple terms, a research question is essentially a question that specifically states what the researcher will attempt to answer. The research questions are:. i.. What is the relationship between performance expectancy and intention to use Smart Apps for visiting museum among youth in Malaysia?. ii.. What is the relationship between effort expectancy and intention to use Smart Apps for visiting museum among youth in Malaysia?. iii.. What is the relationship between social influence and intention to use Smart Apps for visiting museum among youth in Malaysia?. iv.. What is the relationship between facilitating condition and intention to use Smart Apps for visiting museum among youth in Malaysia?. 1.4 RESEARCH OBJECTIVES. The aim of this research study is: i.. To examine the relationship between performance expectancy and intention to use Smart Apps for visiting museum among youth in Malaysia.. ii.. To examine the relationship between effort expectancy and intention to use Smart Apps for visiting museum among youth in Malaysia.. 5. FYP FHPK. 1.3 RESEARCH QUESTION.

(18) To examine the relationship between social influence and intention to use Smart Apps for visiting museum among youth in Malaysia.. iv.. To examine the relationship between facilitating condition and intention to use Smart Apps for visiting museum among youth in Malaysia.. 1.5 SCOPES OF STUDY. This study focuses on the intention to use Smart Apps for visiting museum among youth in Malaysia. The purpose of this research is to investigate the relationship between performance expectancy and examine the relationship between effort expectancy. The intention of using smart apps such as Augmented reality (AR) or Virtual Reality (VR) Apps among youth issue that crucial to make marketing and promotion to improve. In this study, we can understand the focus of using Smart Apps for visiting museums among youth to use AR and VR technologies involving the number of scientific applications. This study focuses on youth in Malaysia as respondents. This study will analyse the relationship between social influence and intention to use Smart Apps for visiting museums.. 1.6 SIGNIFICANT OF STUDY. This feasibility study is an earlier part of the research project to utilise AR. It will involve prototype testing and experiments (By Chan e al, 2019). The advent of AR. 6. FYP FHPK. iii..

(19) and consumers even though both are different technologies. This study aims to determine the intention to use Smart Apps. This study aimed to investigate the relationship between facilitating conditions and intention to use Smart Apps for visiting museum among youth in Malaysia.. 1.7 DEFINITION OF TERMS. In this section are the terms that are being used in this research study. Below is the definition of each of the term:. 1.7.1 Virtual Reality (VR) Virtual Reality (VR) is a three-dimensional environment produced by a computer that a person can experience. The person is ideally part of the virtual world or soaked in a virtual environment and therefore, can manipulate objects or perform a series of actions (Anurag, 2018).. 1.7.2 Augmented Reality (AR) Augmented reality (AR) is a technology that provides digital information in time and environment that manifests its users. It uses a real environment that past users are combined or added with new digital information (Rouse, 2016).. 1.7.3 Technology. 7. FYP FHPK. (Augmented Reality) and even VR (Virtual Reality) is increasingly popular in business.

(20) human life. Technology can also change and be manipulated according to the circulation of times. (Arthur, 2009). 1.7.4 Movement Control Operation (MCO) It is a security measure done by the Malaysian government in response to the covid-19 pandemic to control and prevent it from spreading the covid-19 by controlling society’s movement.. 1.7.5 Standard Operating Procedures (SOP) It is a standard set by the World Health Organization (WHO) and agreed by the Government of Malaysia and implemented for each country to safeguard the public and address or prevent cases of covid-19 infection from occurring.. 1.7.6 Smart Application (Smart Apps) Smart apps are a full feature application created to help broaden its current capabilities. This application provides real-time integration directly into a core processor that helps improve an individual experience.. 8. FYP FHPK. Technology is an application created from scientific knowledge to be used practically in.

(21) The summary of these chapters, the researcher gives the overview of the study intention to use Smart Apps for visiting museum among youth in Malaysia. Besides the researcher also explains about the topic of research which starts with the background of the study, problem statement, followed by research question and research objectives. Last is the scope of the study also significant of the study, and definition of terms also include.. 9. FYP FHPK. 1.8 SUMMARY.

(22) LITERATURE REVIEW. 2.1 INTRODUCTION. Self-service technologies (SSTs) have been introduced as a part of smart tourism. Among SSTs, augmented Reality (AR) has been developed to provide information about destinations. Because of this, a variety of AR utilisation examples can be found in the field of tourism (e.g., Fritz et al. 2005; Han et al., 2014; Yovcheva et al., 2013). Hence, this study is related to understanding the intention to Use Smart Apps for visiting Museums among Youth in Malaysia.. 2.2 THE UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY (UTAUT). Before the researcher begins to explain the Unified Theory of Acceptance and Use of Technology (UTAUT), the researcher must understand the meaning of ‘theory’ itself. Theory is the precision of the right approach that makes a theory very exacting for all the critical components of a thesis. According to Poole and Van de Ven (1989), a good idea is a limited and relatively precise picture.. 10. FYP FHPK. CHAPTER 2.

(23) technology. Based on the UTAUT model, performance expectancy (PE), effort expectancy (EE), social influence (SI) and facilitating conditions (FC) are the significant predictors towards use behaviour of proposed technology. In the past studies (Mou et al., 2016: Lin et al., 2014: Teo, 2014), it has proved that there are significant direct relationships without passing through behaviour intention variable although the PE, EE, SI and FC in UTAUT model did not show a direct connection towards used behaviour. The researcher will use these four variables throughout this research. Performance expectancy is defined as the mark to which an individual believes that using the approach will help them achieve gains in job performance. The performance expectancy is usually focused on task accomplishment and is likely to be especially relevant to men. The effort expectancy has been defined as the degree of ease linked with the use of the system. Social influence has described as the degree to which individual notices that essential others believe he or she should use the new system and the facilitating condition is defined as to which an individual believes that an organisation and technical infrastructure exists to support the use of the system.. 11. FYP FHPK. UTAUT has come out with a concrete model explaining the use of behaviour of.

(24) FYP FHPK Figure 2.1: UTAUT Model. Thus, the proposed research model for examining the independent variables that might influence the intention to use the smart apps for visiting museums among the youths in Malaysia.. 2.3 INTENTION TO USE SMART APPS. The dependent variable for our research topic is about the intention to use Smart Apps in the tourism industry. Intention to use smart apps in this research is essential because the researcher’s data helps the researcher get the right direction while the research was conducted. It is also to verify whether the objective has been achieved or not. Smart apps is an implementation or expansion and planning of an application that will assist humans in storing, changing, or disseminating any information such as AR and VR. According to Guttentang (2010), VR is one 3D environment that is created by a. 12.

(25) heritage landmarks and destinations, VR gives visitors the ability to visit protected sites as a replacement for actual visits. By using VR, tourists can discover any attraction including the difficult-accessible sites (Guerra et al, 2015). Different with VR, AR lets visitors encounter destinations and attractions naturally with the improvement of digital superimpose content ( Jung et al, 2015). Creation and use of these Smart Apps technically are to facilitate the relationship between humans, society and the environment. In addition, nowadays, smart apps are used for interacting, socialising,e or communicating information among human beings and can be used in the tourism industry such as travel agencies, airlines, and so on. It is intended to provide the best and comfortable travel experience for its clients of the tourism industry. According to Jung. et al (2013), a growing number of academics identify the ability of AR to boost the tourism experience for the tourism industry. The AR mobile app can fulfil the information needs of its users. Enhance the experience of one's use, intention to use and recommendation to others (Jung. et al, 2015). Tourists can obtain immediate knowledge about unknown environments by using markers or AR locationbased applications (Han et al, 2013). Smart apps are one of the trending technologies tools that have been used in a different specific business, including in the tourism industry. Smart apps or more specific Augmented Reality and Virtual Reality bring great potential nowadays and in the future. The tourism industry brings many benefits and changes considering the technology offers the best experience environment of travelling by experiencing the digital world during pandemic covid-19. According to Tan Gek Siang et.al, (2019) it was discovered that mobile application had a major positive impact on the AR mobile application by museum visitors because the AR mobile application aesthetic dimension can inspire users to make use of it. The writers also stated AR makes the lesson. 13. FYP FHPK. total absorption of a true environment and turns it into a digital world . To safeguard the.

(26) understanding of the local history and heritage. However, if the application is more convenient for users to understand the displays and explore local history for themselves, instead of responding to leaflets or questioning others (Tom Dieck et al, 2018). The researcher found the most effective ways to find the intention to use smart apps. Virtual Reality and Augmented Reality has the ability to improve the discerning experience by offering interactive and interesting information with the digital world. Augmented and virtual digital world experience can be made extensively to the user by facilitating their journey and building their current formatting travelling during the planned journey. To differentiate, VR offers a full virtual environment while AR comes up with online images that are overlaid based on the real-world view taken out of the camera device (Kounavis et al., 2012). AR had played a major role in improving the social understanding and experience of visitors along with historical and geological information (Jung et al, 2015). Timothy Jung et al, (2016) stated, AR and VR is an asset and invaluable device in improving the experience of visitors that can eventually contribute to the purpose of visiting the real destination. The writers also stated their research, VR giving. unique. experience. to. those. who. use. these. apps. while. visiting. museums. Augmented reality is purifying after the Virtual Reality reconsideration. Virtual reality requires a headset while Augmented Reality more depends on the visual overlap of fading, buzzy haptic feedback, or projections of other sensors into real-world user situations.. 14. FYP FHPK. of history more interactive and fun, it allows visitors to go to the museum to get a better.

(27) According to Venketash, Morris, M.G., Davis, G.B., &, F.D. (2003), Performance Expectancy (PE) is referred to measuring how well an individual can recognise and understand in using the system either helping them to gain or achieve profits in using it. It also can be precise as a tool to measure youth expectation in using AR and VR to get information in visiting a museum, and it is directly related with the use of smart apps to explore inside the museum. By using AR and VR, youth can access the information contained in the museum in the virtual or digital platform. According to Yovcheva et al. (2012), AR led tourists and users to explore the real world only just using smartphones. For example, eTips which is newly launched in 2016. This app is built to help people with guiding tourists to travel. The eTips app makes it easy for travellers to find information, scanning free Wi-Fi areas, restaurants, accommodation, transportation, directions, maps, historical facts and even user reviews of a place. All this information is available by simply scanning the area around them. Other than that, it also enables the user to interact and get new experience in the digital world (Kounavis et al., 2012). An example of AR technology being used in the real-world is the Translate app, IKEA Place app, Pokemon Go, Google Pixel’s Star Wars Stickers and L’Oreal Makeup App. These AR technology applications are available and installed in various types of gadget such as in screens, cellular phone, television or even head-mounted displays and handheld devices. Virtual reality is a simulation generated from a computer and then translated into a three-dimensional image or environment, where the image seems real. This application should only be used through the use of typical electronic equipment such as VR. 15. FYP FHPK. 2.4 PERFORMANCE EXPECTANCY.

(28) make them aware that they interact with the digital environment and create real-life simulations. To be exact, virtual reality creates a virtual world for the user that the difference between the real world and virtual is quite difficult to distinguish. This tool creates artificial or recreational simulations of several situations based on real-life vision, and user hearing will be simulated and makes users sink in a completely different world. In this research, Smart Apps or more precisely, Augmented Reality and Virtual reality is useful and can help people to travel or more directly useful to youth on visiting the museum during pandemic covid-19.. 2.5 EFFORT EXPECTANCY. Miadinovic and Xiang (2016) have described Effort Expectancy (EE) or are perceived to be used as the amount of individual effort needed to use the information system. Higher EE will achieve faster acceptance by potential users as less learning effort is required (Evon & Lau, 2016). Users believe that if the smart app is easier, users would feel more secure in performing those tasks using the smart app. Effort Esperance has a direct connection to the use of the smart app to visit the museum. This is because the use of the smart app for people to visit museums is likely to be affected by how simple or complex it is to retrieve relevant information with the smart app within the shortest time possible. Therefore, if tourists know that it is very convenient to use their smart app to visit the museum, they will not refrain from doing it. UTAUT studies have shown that EE has a positive effect on the intention to use technology in a wide range of contexts, including smart app (Gupta et al., 2018), mobile. 16. FYP FHPK. headsets. Virtual reality technologies absorb or imitate the environment for any user to.

(29) Veer, Peeters, Brabers, Schellevis, Rademakers, & Francke, 2015) and mobile internet applications (Wang, 2010) since many scholars have discovered that EE can have a powerful impact on behavioural intent.. 2.6 SOCIAL INFLUENCE. According to Venketash et al. (2003), social influence refers to measuring whether what users see would influence and encouraging other people to use the system or technology they have used. Social influence is one of the core factors to construct a technology (Thompson et al., 1991). Nowadays people tend to interact and socialise with each other in the virtual world because it makes it easier for them, with the existence of community interest in socialising virtual world has made developers enthusiastic about building more advanced technology for the community. Social influence can give a positive impact on behaviour of intentions to use technology (Arman et al., 2015). However, there are other studies saying. Otherwise, social influence cannot affect others when its institute is changed (Bennani et al., 2013). User’s character is another obvious possibility to not get influenced by others. Individuals who are beliefs in oneself or already encountered by the technology will less influence social insistency (Chang et al., 2012). The youth choose to interact with people virtually and gain information also virtually rather than meeting a real person to avoid the Covid-19 from spreading.. 17. FYP FHPK. apps (Kang, 2014), mobile commerce (Alkhunaizan, 2013), e-health applications (De.

(30) Facilitating conditions refer to the organisational and technical infrastructures available that support technology usage by a group of users. (Venkatesh et al. 2012). Individuals who have favourable facilitating conditions would show stronger. Martins et al. (2014), reported facilitating conditions exert a strong positive impact on users’ behavioural intention to adopt internet banking. People unconsciously seek for assistance when they are trying to use new technology and require seamless aid when faced with problems of using technology. A study conducted in Latvia found facilitating conditions as among the most important determinants (Fuksa 2013). Likewise, Koenig-Lewis et al. (2015) Found facilitating conditions have a significant impact on users'. A user-friendly mobile app should be able to quickly help to resolve users’ problems when using the app when visiting a museum.. Facilitating Conditions are defined as the degree to which an individual believes that an organisational and technical infrastructure exists to support the use of the system. Three constructs embody this determinant were Perceived Behavioural Control (Ajzen 1991; Taylor and Tood 1995a, 1995b), Facilitating Conditions (Thompson et al. 1991) and Compatibility (Moore and Benbasat 1991). The facilitating conditions refer to technical infrastructures that are available to support the technology usage by a group of users. The youth believes that when a museum is providing these Smart Apps, they will visit the museum more.. 18. FYP FHPK. 2.7 FACILITATING CONDITIONS.

(31) Dependent Variable (DV). Independent Variable (IV) Performance expectancy Effort expectancy. Intention to use Smart Apps for visiting museum among youth in Malaysia. Social influence. Facilitating condition. Figure 2.2: The Conceptual Framework of this study. 19. FYP FHPK. 2.8 CONCEPTUAL FRAMEWORK.

(32) Speculation must be testable and reasonable, contemplating current information and procedures. Likewise, speculation is characterised as an expectation or clarification between two factors. It suggests an efficient relationship exists between an autonomous variable and a reliant variable. In this manner, the examination has proposed:. H1: There is the relationship between performance expectancy and intention to use Smart Apps for visiting museum among youth in Malaysia. H2: There is the relationship between effort expectancy and intention to use Smart Apps for visiting museum among youth in Malaysia. H3: There is the relationship between social influence and intention to use Smart Apps for visiting museum among youth in Malaysia. H4: There is the relationship between facilitating and intention to use Smart Apps for visiting museum among youth in Malaysia.. 2.10 SUMMARY. This research gauges the relationships of performance expectancy and intention to use Smart Apps for visiting museum among youth in Malaysia. Moreover, this research likewise investigates the relationship between effort expectancy and intention to use Smart Apps for visiting museum among youth in Malaysia. Besides this research gauges. 20. FYP FHPK. 2.9 HYPOTHESIS.

(33) museum among youth in Malaysia. This research also investigates the relationship between facilitating condition and intention to use Smart Apps for visiting museum among youth in Malaysia. By and large, the other intention to use Smart Apps is technology, ethical consumption, and so on.. 21. FYP FHPK. the relationship between social influence and intention to use Smart Apps for visiting.

(34) METHODOLOGY. 3.1 INTRODUCTION. This chapter is about research methodology. The researchers set out, and brief about the research procedure was used to accumulate the data and finish the research. In addition, researchers also explain collecting data and information to deal with the research objective. To design the research method, the research problem must be defined to establish a research method to a bigger extent. That includes the population target and the difficulty of accessing it. The significance of the decision will influence the research and affecting the research method. This section shows the technique that the researchers have founded. The entire process involved in this research will be seen in this chapter.. 3.2 RESEARCH DESIGN. In general, the research design refers to the framework for preparing and implementing a particular design. Once a decision is taken to continue with the study, a data collection plan is required. To be carried out to achieve the study objectives (Aaker et al., 2000).. 22. FYP FHPK. CHAPTER 3.

(35) general, the research design refers to the framework for the preparation and implementation of a particular design. Once a decision was taken to continue with the study, 23, a data collection plan is required. To be carried out to achieve the study objectives (Aaker et al., 2000). In the present analysis, Quantitative analysis was used in the research design. Quantitative analysis refers to a systematic way of gathered and analysed data. It was collected from several sources. The quantitative analysis required the use of mathematics, 21 tools for statistical and analytical results. It can, therefore, be defined as a structured cause-and-effect relationship between issues and factors. A large-scale study survey helps produce accurate research statistics using a system via questionnaires or formal interviews (SIS International Market Research, 2018). This research examines the relationship between performance expectancy, effort expectancy, social influence and facilitating condition as an independent variable and intention to using Smart Apps for visiting museum among youth in Malaysia.. 3.3 POPULATION. A whole community is the definition of the population where some knowledge is required to be sure. Population statistics consist of persons and high population, weight, BMI, events, and results if the population with strong inclusion can be well defined (Banerjee & Chaudhury, 2010). However, the scope of this research is more to youth or young people, which is the age from 16 to 25 years old in Malaysia. 23. FYP FHPK. In the present analysis, Quantitative analysis was used in the research design. In.

(36) Sample refers to the element of the population. The sample member is known as the subject, and the total number of subject in the sample know as sample size. The sample size is normally determined by population. According to Krejcie and Morgan (1970), for a population that is more than 1 000, 000 the required sample is 384. This is because when the population increases, the sample size increases. The sample size will stay at a diminishing rate as it eventually constant at 380 sample size and slightly more. Therefore, it is best for the researcher to use a sample size of 384 because it saved expense and energy from sampling 380.. s = X2NP(1-P) ÷ d2(N-1) + X2P(1-P).. s = Required sample size. X2= The table value of chi-square for 1 degree of freedom at the desired confidence level (3.841). N = The population size. P = The population proportion (assumed to be .50 since this would provide the maximum sample size). d = The degree of accuracy expressed as a proportion (.05).. 24. FYP FHPK. 3.4 SAMPLE SIZE.

(37) Source: Kerjcie and Morgan (1970). 25. FYP FHPK. Table 3.1: Table for Determining Sample Size from a Given Population..

(38) The research was conducted in the past that sampling method is selecting an adequate number of elements from the population. The sampling method can be categorised into two types which are probability sampling and non-probability sampling. The location density of population is the average number among people youth in Malaysia. For this research, the total population Intention to Use Smart Apps was used to determine for visiting Museum among Youth in Malaysia. The sampling part of experience sampling is a central feature of the method, and thus it is essential to consider different approaches to this sampling. Non-probability sampling techniques where the samples are gathered in a process so that each element of the population does not have a known chance of being selected. The samples are selected because they are accessible to the researchers, which means it involves picking up any available set of respondents convenient for the researcher. The questionnaire will be designed using Google Forms and shared through social media, WhatsApp, Instagram, Twitter, and Facebook, to the whole of Malaysia. By using Google Forms and social media, the researchers could get the entire respondents from local youth. This technique was used when collecting the total population Intention to Use Smart Apps was used to determine for visiting Museum among Youth in Malaysia. The sample, n is 384 sample size random visitors Museum who have a visit.. 3.6 DATA COLLECTION METHOD Data were obtained in this research using online internet questionnaires through Google form. Online questionnaires were allocated randomly to the local youth in. 26. FYP FHPK. 3.5 SAMPLING METHOD.

(39) have experienced using the smart app. The data were collected online at the Universiti Malaysia Kelantan, which is easier for researchers to share the Google form link of questionnaires through students WhatsApp group. Respondents were chosen based on several characterised. Firstly, based on this research, the respondents must be local youth. Secondly, respondents must be 16 until 25 years old, and lastly, respondents must have experienced using the smart application. The reason to choose a youth as a respondent was too small the scope of respondents. To make sure the respondents selected are qualified to all the criteria that will be listed, the researchers were asked a few screening questions ‘Are you experienced using the smart app and what was your opinion about it?’ before respondents answer the questions questionnaires. The questionnaire was distributed in March and April 2021 as a researcher came back to their campus for the new semester after a semester break in February 2021. The questionnaire contained items to answer the research objectives and has privacy and confide agreements of the responses.. 3.7 RESEARCH INSTRUMENT The survey is the most often utilised quantitative research method. You can use short answer or dichotomous questions, multiple choice answers, paragraphs, check boxes, drop downs, linear scales, multiple choice grids, and more in a quantitative survey. (Trigueros, Roxana & Juan, Med & Sandoval, Francisco, 2017). For this study, the researchers have gathered the information through the questionnaire to find information about using smart apps for visiting the museum. 27. FYP FHPK. Malaysia. This questionnaire was allocated to several youths, especially for youth who.

(40) KISS principle (Keep It Short and Simple) for the questionnaire. The question in this survey is close-ended questions that use a 5-point Likert Scale. This method has been used to gain information about the intention of the youth to use smart apps when visiting the museum.. Table 3.2: Measurement of 5-Point Likert Scale Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree 1. 2. 3. 4. 5. The researchers chose the questionnaire method because it is easy to ask questions, easy to answer by the respondents, and easy to submit through the application. This method also can save the respondents time as they can answer it at any time. These questionnaires have six sections divided into sections A, B, C, D, E, and F. In section A. The researchers put the demographic segment of the respondents. In this section, the researcher asks the age, gender, occupation and marital status. For section B, the researcher has included the intention to use smart apps when visiting the museum. Section C discussed the first independent variable, which is the performance expectancy, Section D question is on the second independent variable, which is the effort expectancy, Section E is about the social influence, which is the third independent variable, and the last section, Section F is for the last independent variable which is facilitating condition. Table 3.3 shows the compositions of the questionnaire, and the explanation was made in the following section.. 28. FYP FHPK. The researchers have asked simple and easy to understand the respondent like the.

(41) Sections Section A. Demographic data. 4. Section B. Intention to use Smart 5 Apps. Section C. Performance. 5. Expectancy. Section D. Effort Expectancy. 5. Section E. Social Influence. 5. Section F. Facilitating Conditions. 4. Researcher Davis et al. 1989; Fishbein and Ajzen 1975; Taylor and Todd 1995a, 1995b, Davis et al. 1992 and Compeau and Higgins 1995b; Compeau et al. 1999 Davis 1989; Davis et al. 1989, Moore and Benbasat 1991 and Compeau and Higgins 1995b; Compeau et al. 1999 Davis et al. 1989, Thompson et al. 1991 and Moore and Benbasat 1991 Ajzen 1991; Davis et al. 1989; Fishbein and Azjen 1975; Mathieson 1991; Taylor and Todd 1995a, 1995b, Thompson et al. 1991 and Moore and Benbasat 1991 Ajzen 1991; Taylor and Todd 1995a, 1995b and Thompson et al. 1991. 3.7.1 Questions Used in Section A of the Questionnaire. Section a is created for the collection of data on respondent’s demographic. It involves gender, age, marital status and occupation. The items listed are shown in Table 3.4.. 29. FYP FHPK. Table 3.3: Questionnaire composition. Items Number of items Support References.

(42) 3.7.2 Questions Used in Section B of the Questionnaire To investigate the intention to use Smart Apps when visiting the museums, five items were chosen to develop in Section B. Respondents need to circle up their agreement level on a five-point satisfaction scale ranging from one (1) “strongly disagree” to five (5) “strongly agree” in this section. Table 3.5 will describe the items for this section.. Table 3.5: Questions Used in Section B of the Questionnaire – Intention to use Smart Apps Dimensions Supporting References Items Intention to use Smart Davis et al. 1989; Fishbein 1. Using the Smart Apps is a Apps and Ajzen 1975; Taylor and good idea Todd 1995a, 1995b, Davis et 2. Using the Smart Apps is al. 1992 and Compeau and pleasant Higgins 1995b; Compeau et 3. I like the idea that using al. 1999 the Smart Apps would be enjoyable 4. I find the use of Smart Apps would be enjoyable 5. I look forward to those aspects of my visiting on the museum that require me to use the Smart Apps. 3.7.3 Questions Used in Section C of the Questionnaire. 30. FYP FHPK. Table 3.4: Questions Used in Section A of the Questionnaire – Demographic profile of Respondents. Dimensions Supporting References Items Demographic Profile of Researcher 1. Gender (Male; Female) Respondents 2. Age (15 to 20 years old; 21-25 years old; 26 to 30 years old 3135 years old; 36 years old and above) 3. Marital status (Single; Married; Prefer not to say) 4. Occupation (Student; Selfemployed; Employee; Unemployed).

(43) Apps. There are five items in these sections, and Table 3.6 shows the list of the questions.. Table 3.6: Questions Used in Section C of the Questionnaire – Performance Expectancy Dimensions Supporting References Items Performance Expectancy Davis et al. 1989; Fishbein 1. Using the Smart Apps and Ajzen 1975; Taylor and would enable me to visit Todd 1995a, 1995b, Davis the museum et al. 1992 and Compeau 2. Using the Smart Apps and Higgins 1995b; will improve my visiting Compeau et al. 1999 to museum 3. Using the Smart Apps make it easier to visit the museum 4. Using the Smart Apps enhance my effectiveness in visiting the museum 5. I would find the Smart Apps is useful in visiting the museum. 3.7.4 Questions Used in Section D of the Questionnaire Section D was created for the collection of the effort expectancy of using Smart Apps. There are five items in these sections, and Table 3.7 shows the list of the questions.. Table 3.7: Questions Used in Section D of the Questionnaire – Effort expectancy Dimensions Supporting References Items Effort Expectancy Davis et al. 1989, 1. The use of Smart Thompson et al. 1991 and Apps for visiting the Moore and Benbasat 1991 museum is not frustrating 2. It takes too long to learn how to use the Smart Apps when visiting the museum to make it worth the effort 3. It would be easy for me to become skilful at using. 31. FYP FHPK. Section C was created for the collection of the performance expectancy of using Smart.

(44) 3.7.5 Questions Used in Section E of the Questionnaire This section was created for the collection of the social influence of using Smart Apps. There are five items in these sections, and Table 3.8 shows the list of the questions.. Table 3.8: Questions Used in Section E of the Questionnaire – Social Influence Dimensions Supporting References Items Social Influence Ajzen 1991; Davis et al. 1. People who 1989; Fishbein and Azjen influence my behaviour 1975; Mathieson 1991; should use the Smart Apps. Taylor and Todd 1995a, 2. People who are 1995b, Thompson et al. important to me should use 1991 and Moore and the Smart Apps. Benbasat 1991 3. My friends are very supportive of the use of the Smart Apps for my visitation to the museum. 4. People my age who use the Smart Apps have a high profile. 5. Using the Smart Apps is a status symbol in my age. 3.7.6 Questions Used in Section F of the Questionnaire. This section was created for the collection of the facilitating conditions of using Smart Apps. There are four items in these sections, and Table 3.9 shows the list of the questions.. 32. FYP FHPK. the Smart Apps for visiting the museum 4. Using Smart Apps involve too much time doing the mechanical operations. 5. Overall, I believe the Smart Apps is easy to use for visiting the museum.

(45) 3. Specialised instruction concerning the Smart Apps is available to me 4. A specific person is available for assistance with the Smart Apps difficulties. 33. FYP FHPK. Table 3.9: Questions Used in Section F of the Questionnaire – Facilitating Conditions Dimensions Supporting References Items Facilitating Conditions Ajzen 1991; Taylor and 1. I know necessary to Todd 1995a, 1995b and use Smart Apps Thompson et al. 1991 2. Guidance was available to me in the selection of using Smart Apps when visiting the museum.

(46) Data analysis is a process of using a statistical practice to organise, describe and evaluate data. Firstly, it is essential to understand the aim of this research clearly. Descriptive analysis is the most frequent method used in a quantitative method.. 3.8.1 Descriptive Analysis. Descriptive analysis is the term used to define, explain, or illustrate data analysis and summarise data (William M.K, 2015). However, the descriptive analysis does not allow us to make conclusions beyond the data we have analysed or reach any decisions regarding the hypotheses we might have created. They are simply a way for our data to be described. Median is the set of values’ numerical average. These data analysis should be based on the research questions and the research design selected for this study. Descriptive is very important in this study because if the research can simply present the, it would be difficult to visualise what the information showed, detail if there was a lot of it. For example, if we get the answer 100 youth, we may be interested in their performance on using the app. Descriptive analysis is the best in limited sample research and when larger population are not needed since descriptive analysis is mostly used for analysing a single variable.. 3.8.2 Reliability Analysis. Reliability refers to the degree to which, if the measurements are replicated, a scale achieves reliable results several times. Reliability analysis is called reliability. The. 34. FYP FHPK. 3.8 DATA ANALYSIS.

(47) scale that can be accomplished by selecting the association between the scores obtained from different scale administrations. For the study to be considered valid, the measurement procedure must first be reliable. The importance of reliability analysis allows for estimating influence to compare the result. Cronbach’s alpha is the most common measure o internal coefficient is in this research. It is most commonly used when researching multiple Likert questions in a survey and questionnaire that form a scale. Nunnally (1967) suggested that the sample size using AMOS (Analysis of Moment Structures) is required. A total of 21 measurement items were used in our study, and therefore, the minimum sample size is 210. In this vein, a total of 269 questionnaires meets this cut off point.. 3.8.3 Pearson Correlation. Pearson correlation test statistics are used to calculate the statistical relationship between the two. Since it is based on the principle of covariance, it is known as the best method of calculating the relationship between the variables of interest. It provides data on the extent of the relationship, the relationship, and the direction of the relationship. Pearson Correlation analysis is used when researchers want to see a linear relationship between these variables. Pearson Correlation produces a sample correlation coefficient, r, which measures the strength and direction of linear relationships between pairs of continuous variables. By extension, the Pearson Correlation evaluates whether there is statistical evidence for a linear relationship among the same teams of variables in the population, represented by a population correlation coefficient, ρ (“rho”). A parametric calculation is the Pearson Correlation. Pearson Correlation analysis was used in this study. 35. FYP FHPK. study of reliability is calculated by obtaining the proportion of systematic variance on a.

(48) Malaysia.. Table 3.10: Rule of Thumb for Interpreting the Size of a Correlation of Correlation. Size of Correlation. Coefficient Interpretation. .90 to 1.00 (-. 90 to -1.00). Very high positive (negative) correlation. .70 to .90 (-.70 to -. 90). High positive (negative) correlation. .50 to .70 (-.50 to -.70). Moderate positive (negative) correlation. .30 to .50 (-.30 to -.50). Low positive (negative) correlation. .00 to .30 (-.00 to -.30). Negligible correlation. Source: Hinkle, Wiersma and Jurs (2003). 3.8.4 Pilot Study. A pilot study can be described as a small study to test research procedures, data collection tools, sample recruitment strategies, and other research techniques. A pilot analysis is one of the primary stages of a research project. It is performed before implementation during the full study to identify possible problem areas and limitations in the research instruments and procedure. (Zailinawati Abu Hassan et al., 2006). This study demonstrated the pilot study’s efficacy in identifying flaws in the questionnaire, which can then be used in a full study after appropriate modifications. It also provided a better understanding of how the survey was to be carried out. The questionnaire will be designed using Google Forms and shared through social media, WhatsApp, Instagram, Twitter, and Facebook to the whole of Malaysia. The steps in this. 36. FYP FHPK. to assess the relationship and purpose of using Smart Apps for youth museum visits in.

(49) project. By carrying out a pilot test first, the researcher gets to test the level.. 3.8.5 Normality Test. Normality tests are used to see if a data collection is well-modelled by a normal distribution and determine how likely it is to be normally distributed for a random variable underlying the data set. Statistics measure the distance between the sample’s empirical distribution function and the reference distribution’s cumulative distribution function or between the two samples’ empirical distribution functions. The null distribution of this statistic is determined based on the null assumption that the sample is derived from the reference distribution (in the case of a single sample) or that the samples are derived from the same distribution (in the case of a two-sample distribution).. 3.9 SUMMARY. The research design was used in this research is discussed in this chapter. The researchers discuss the analysis, obtain the data, population and sample study are also listed in this paper. The sampling method chosen and used by researchers is the quantitative method in terms of the questionnaire distributed to the respondents in public.. 37. FYP FHPK. pilot study demonstrate the benefits and the methods of this important phase of a research.

(50) RESULTS AND DISCUSSION. 4.1 INTRODUCTION. The reliability analysis, demographic characteristics of respondents, descriptive analysis, and Pearson’s coefficient analysis were all included in this chapter. Data from 200 respondents were used to generate the study’s findings. The IBM SPSS Statistics software was used in this study. After the data had been collected, version 24 was used to analyse the data.. 4.2 RELIABILITY ANALYSIS. The questionnaires’ reliability was assessed using reliability analysis. In addition, Cronbach’s Alpha analysis was used to ensure the information’s reliability and internal reliability. The table below displayed Cronbach’s Rules of Thumb. Hair et al. size. ’s of the alpha coefficient (2007).. 38. FYP FHPK. CHAPTER 4.

(51) Coefficient Alpha Range, α. Strength of Association. <0.6. Poor. 0.6 to < 0.7. Moderate. 0.7 to < 0.8. Good. 0.8 to < 0.9. Very Excellent. <0.9. Excellent. Source: Hair et al. (2007). Table 4.1 illustration the overall consistency (pilot test) for the dependent and independent variable. The pilot test has been done to 30 respondents before it was distributed to 200 respondents through the online survey method.. Table 4.2: Result of Reliability Coefficient Alpha for the Independent Variables and Dependent Variable Variables. Number of items. Cronbach’s Alpha. Performance Expectancy. 5. 0.894. Effort Expectancy. 5. 0.812. Social Influence. 5. 0.839. Facilitating Condition. 4. 0.840. Intention to use Smart Apps. 5. 0.922. 24. 0.942. to visiting the museum Overall variables. 39. FYP FHPK. Table 4.1: Rules of Thumb of Cronbach’s Alpha coefficient size.

(52) independent and dependent variable in this study. According to the table, all variables were greater than 0.6, and the overall variables were 0.942. Therefore, in this study, the result shown is reliable and can be accepted. In measuring the performance expectancy that influenced the intention of Malaysian youth to use Smart Apps for museum visits, five questions were used. Cronbach’s Alpha for this section’s question was 0.894, which was very good, according to Table 4.2. As a result, the coefficients obtained for the questions in the performance expectancy variable were trustworthy. Following that, five questions measured the Effort expectancy variable that influenced Malaysian youth’s intention to use Smart Apps for museum visits. Cronbach’s Alpha coefficient in this section is 0.812, which indicates that it is very good. Consequently, the coefficients obtained for the questions in the effort expectancy variable were trustworthy. Furthermore, five questions were used to assess the social influence variable that influenced youth in Malaysia’s intention to use Smart Apps for museum visits. The Cronbach’s Alpha result for this section’s question was 0.839, which resulted in very good. As a result, the coefficients obtained for the questions in the situational variable were trustworthy. Furthermore, four questions were used to assess the facilitating condition variables that influenced Malaysian youth’s intention to use Smart Apps for museum visits. The Cronbach’s Alpha result for this section’s question is 0.840, indicating excellent. As a result, the coefficient obtained for this question in measuring Malaysian youth’s intention to use Smart Apps for museum visits was also reliable. Lastly, five questions were used to assess the intention to use Smart Apps for visiting Museum variables. The Cronbach’s Alpha result for this section questions were. 40. FYP FHPK. Table 4.2 showed the overall value of Cronbach’s Alpha Coefficient for the.

(53) in measuring the intention of Malaysian youth to use Smart Apps for museum visits was also reliable. Since the Cronbach’s Alpha charge for the variables exceeded 0.942, it indicates that the questionnaires are highly reliable and that the study can proceed. Overall, the reliability demonstrated that the respondents understood the questions well, implying that the questionnaires were accepted for this study.. 4.3 DEMOGRAPHICS CHARACTERISTICS OF RESPONDENTS. The analysis of this research included the frequency Analysis. The data from section A of the questionnaire included the questions from various demographic variables of respondents such as gender, age, occupation and marital status. The frequency analysis of the respondents’ demographic profiles was presented in a table and pie chart.. Table 4.3: Number of Respondents by Gender Gender. Frequency. Percentage (%). Cumulative Percentage (%). Male. 20. 10. 10. Female. 180. 90. 100. Total. 200. 100. 41. FYP FHPK. 0.922, which is indicating excellent. As a result, the coefficient obtained for this question.

(54) 10% Male. Female. 90%. Figure 4.1: Percentage of Respondents by Gender Table 4.3 and Figure 4.1 showed the respondents by gender. The total number of respondents for male is 20 respondents while the number of females was 180 respondents. Thus, out of 200 respondents, 10 per cent of total respondents were male, and the remaining 90 per cent were female respondents involved in this study.. Table 4.4: Number of Respondents by Age Age. Frequency. Percentage (%). Cumulative Percentage (%). 15-20. 63. 31.5. 31.5. 21-25. 125. 62.5. 94.0. 26-30. 8. 4.0. 98.0. 31-35. 1. 0.5. 98.05. 36 years old and above. 3. 1.5. 100.0. 200. 100. Total. 42. FYP FHPK. Gender.

(55) 2% 1% 4%. 31%. 15-20 21-25 26-30 31-35 36 Years old and above. 62%. Figure 4.2: Percentage of Respondents by Age. Table 4.4 and Figure 4.2 showed the total respondents by age. 200 respondents were consisted of 15 – 20 years old (63 respondents / 31 per cent), 21 – 25 years old (125 respondents / 62 per cent), 26 – 30 years old (8 respondents / 4 per cent), 31 – 35 years old (1 respondents / 1 per cent) and 36 years old and above (3 respondents/ 2 per cent) had responded to the questionnaire.. 43. FYP FHPK. Age.

(56) Marital Status. Frequency. Percentage (%). Cumulative Percentage (%). Single. 184. 92. 92. Married. 6. 3. 95. Prefer not to say. 10. 5. 100. Total. 200. 100. Marital Status 2% 3% Single Married Prefer not to say. 95%. Figure 4.3: Percentage of Respondents by Marital Status. Table 4.5 and Figure 4.3 showed the total of respondents by marital status. 200 respondents consist of single (184 respondent / 92 per cent), married (6 respondents /3 per cent) and prefer not to say (10 respondent / 5 per cent) had responded to the questionnaire.. 44. FYP FHPK. Table 4.5: Number of Respondents by Marital status.

(57) Occupation. Frequency. Percentage (%). Cumulative Frequency (%). Student. 170. 85.0. 85.0. Self-employed. 3. 1.5. 86.5. Unemployed. 1. 0.5. 87.0. Employee. 26. 13.0. 100.0. Total. 200. 100.0. Occupation 13% 1% 2%. Student Self employed Unemployed Employee. 84%. Figure 4.4: Percentage of Respondents by Occupation. Table 4.6 and Figure 4.4 showed the total respondents by occupations. There were 200 respondents which consists of student (170 respondents / 85 per cent), selfemployed (3 respondents / 1.5 per cent), unemployed (1 respondents / 0.5 per cent), employee (26 respondent / 13 per cent) had responded to the questionnaire.. 45. FYP FHPK. Table 4.6: Number of Respondents by Occupation.

(58) This research analyses the mean and standard deviation for section B, C, D, E and F of the questionnaires.. 4.4.1 Independent variables and dependent variables. Table 4.7: Descriptive Statistics. Variables. N. Mean. Standard deviation. Performance expectancy. 200. 4.2600. .69050. Effort expectancy. 200. 3.9590. .61613. Social influence. 200. 4.0480. .73100. Facilitating condition. 200. 4.3113. .61773. Intention to use Smart Apps. 200. 4.4770. .60705. Table 4.7 shows the number of respondents, the mean and standard deviation of the independent and dependent variables. For the independent variable, the highest mean was facilitating condition, which is 4.3113, second followed by performance expectancy, which is 4.2600, followed by the third is the social influence which was 4.080, and effort expectancy, which is 3.9590. The mean for the dependent variable was 4.4770.. 46. FYP FHPK. 4.4 DESCRIPTIVE ANALYSIS.

(59) Table 4.8: Descriptive statistics of Performance Expectancy No. Item description. N. Mean. Standard Deviation. 1. Using the Smart Apps. 200. 4.28. .803. 200. 4.10. .938. 200. 4.33. .758. 200. 4.18. .871. 200. 4.42. .704. would enable me to visit the museum 2. Using the Smart Apps would improve my visiting to museum. 3. Using the Smart Apps make it easier for me to visit the museum. 4. Using the Smart Apps will enhance my effectiveness in visiting the museum. 5. I would find the Smart Apps is useful when visiting the museum. Table 4.8 show the mean and standard deviation analysis on the independent variable, which is performance expectancy. The highest mean was question number 5, which was 4.42, where the respondent agreed that Smart Apps is useful when visiting the museum. The lowest mean value was question number 2, which was 4.10, where the respondent slightly agreed that Smart Apps would improve respondent visiting the. 47. FYP FHPK. 4.4.2 Performance Expectancy.

(60) which lowest than 1 indicated the values close to the mean.. 4.4.3 Effort Expectancy. Table 4.9: Descriptive statistics of Effort Expectancy No. Item description. N. Mean. Standard Deviation. 1. The use of Smart Apps for. 200. 4.26. .731. 200. 3.48. 1.112. 200. 4.27. .714. 200. 3.48. 1.228. 200. 4.31. .753. visiting the museum is not frustrating 2. It takes too long to learn how to use the Smart Apps when visiting the museum to make it worth the effort.. 3. It will be easy for me to become skilful at using the Smart Apps for visiting the museum. 4. Using the Smart Apps will involve too much time doing the mechanical operations.. 5. Overall, I believe the Smart Apps is easy to use for visiting the museum. 48. FYP FHPK. museum. The data set from 200 respondents with the standard deviation of all of the value.

(61) variable, which is effort expectancy. The highest mean was question number 5, which was 4.31, where the respondents agreed and believe that Smart Apps is easy to use for visiting the museum. The lowest mean value was question number 2 and 4, which was both is 3.48, where the respondent slightly agreed that it takes too long to learn how to use the Smart Apps when visiting the museum to make it worth and using the Smart Apps will involve too much time doing the mechanical operations. The data set from 200 respondents with the standard deviation half of the value that lowest than 1 indicated the values close to the standard deviation. More than one indicated the values were more dispersed.. 4.4.4 Social Influence. Table 4.10: Descriptive statistics of Social Influence No. Item description. 1. People who influence my. N. Mean. Standard Deviation. 200. 3.92. .926. 200. 4.06. .854. 200. 4.22. .835. behaviour should use the Smart Apps 2. People who are important to me should use Smart Apps when visiting the museum. 3. My friends are very supportive of the use of the Smart Apps for visiting the museum. 49. FYP FHPK. Table 4.9 show the mean and standard deviation analysis on the independent.

(62) People my age who use the Smart. 200. 3.95. 1.043. 200. 4.08. .969. Apps have a high profile 5. Using the Smart Apps is a status symbol in my age. Table 4.10 show the mean and standard deviation analysis on the independent variable, which is social influence. The highest mean was question number 3, which was 4.22, where the respondents agreed that their friends are very supportive of using the Smart Apps for visiting the museum. The lowest mean value was question number 1, which was is 3.92, where the respondent slightly agreed that people who influence their behaviour should use the Smart Apps. For the data set from 200 respondents with the standard deviation most of the value which lowest than 1, it indicated the values close to the standard deviation which greater than 1 indicated the values were more dispersed.. 4.4.5 Facilitating Condition. Table 4.11: Descriptive statistics of Facilitating Condition No. Item description. 1. Guidance was available to me in. N. Mean. Standard Deviation. 200. 4.31. .754. 200. 4.34. .712. the selection of using Smart Apps when visiting the museum. 2. Specialised instruction concerning the Smart Apps is available to me. 50. FYP FHPK. 4.

(63) A specific person is available for. 200. 4.32. .755. 200. 4.27. .775. assistance with the Smart Apps difficulties 4. I know necessary to use Smart Apps.. Table 4.11 show the mean and standard deviation analysis on the independent variable, which is facilitating condition. The highest mean was question number 2, which was 4.34, where the respondent agreed that specialised instruction concerning the Smart Apps is available to them. The lowest mean value was question number 4, which was 4.27, where the respondent slightly agreed that they know the necessary to use Smart Apps. The data set from 200 respondents with the standard deviation of all of the value which lowest than 1 indicated the values close to the mean.. 4.4.6 Intention to use Smart Apps. Table 4.12: Descriptive statistics of Intention to Use Smart Apps No 1. Item description Using the Smart Apps is a good. N. Mean. Standard Deviation. 200. 4.53. .672. idea 2. Using the Smart Apps is a pleasant. 200. 4.51. .672. 3. I like the idea of using the Smart. 200. 4.48. .687. 200. 4.49. .687. Apps 4. I find using the Smart Apps would be enjoyable. 51. FYP FHPK. 3.

(64) I look forward to those aspects. 200. 4.37. .798. when I visit the museum that requires me to use the Smart Apps. Table 4.12 shows the mean and standard deviation analysis on the independent variable, which intends to use smart apps. The highest mean was question number 1, which was 4.53, where the respondent agreed that using the Smart Apps is a good idea. The lowest mean value was question number 5, which was 4.37, where the respondent slightly agreed that they look forward to those aspects when they visit the museum that requires them to use the Smart Apps have. The data set from 200 respondents with the standard deviation of all of the value which lowest than 1 indicated the values close to the mean.. 4.5. PEARSON CORRELATION COEFFICIENT. Pearson’s correlation analysis was an important analysis that measured the linear relationship between the two variables. This analysis aimed to determine whether there are correlations between independent variables (performance expectancy, effort expectancy, social influence and facilitating condition) and the dependent variable (intention to use Smart Apps). If the relationship is significant, researchers must decide whether the level of strength of the association is acceptable.. 52. FYP FHPK. 5.

(65) FYP FHPK. Table 4.13: Rule of Thumb for Interpreting the Size of a Correlation Coefficient Size of Correlation. Interpretation. .90 to 1.00 (-. 90 to -1.00). Very high positive (negative) correlation. .70 to .90 (-.70 to -. 90). High positive (negative) correlation. .50 to .70 (-.50 to -.70). Moderate positive (negative) correlation. .30 to .50 (-.30 to -.50). Low positive (negative) correlation. .00 to .30 (-.00 to -.30). Negligible correlation. Source: Hinkle, Wiersma and Jurs (2003). Hypothesis 1: Performance Expectancy. H1:There is a relationship between performance expectancy and intention to use Smart Apps among youth in Malaysia. Table 4.14: Correlation coefficient for performance expectancy factor and intention to use Smart Apps among youth in Malaysia.. Intention to Use. Pearson Correlation. Smart Apps. Sig. (2-tailed). Intention to Use. Performance. Smart Apps. Expectancy. 1. .630** .000. N. 200. 200. .630**. 1. Performance. Pearson Correlation. Expectancy. Sig. (2-tailed). .000. N. 200. 53. 200.