scholarly journals Exploring the Relationship between Perceived Big Data Advantages and Online Consumers’ Behavior: An Extended Hierarchy of Effects Model

2020 ◽  
Vol 13 (6) ◽  
pp. 73
Author(s):  
Jean-Luc Pradel Mathurin Augustin ◽  
Shu-Yi Liaw
Keyword(s):  
Big Data ◽  
Information And Communication Technologies ◽  
Customer Service ◽  
Information Search ◽  
Dynamic Pricing ◽  
Recommendation System ◽  
Tourism Industry ◽  
Social Awareness ◽  
Significance Level ◽  
Hierarchy Of Effects

This study intends to extend the hierarchy of effects model into the reality of the tourism industry after incorporation of information and communication technologies. Data analyses were conducted on 260 online questionnaires. The findings indicated consumer behavior follows a three-layer model: Attention-Intention/Desire-Action/Sharing-Social Awareness. Among big data advantages, recommendation system, information search and improved customer service are important to Attention-Intention; information search, dynamic pricing are important to Desire-Action with customer service (lower significance level); only customer service is important to Sharing-Social awareness. This model allows understanding of consumers’ behavior in online tourism as tourists are often sharing their experiences and raise awareness on service quality from e-vendors. Organizations might use big data to guarantee customers’ satisfaction and attract positive feedback particularly from the third layer of behavior.

Enhancing Online Repurchase Intention via Application of Big Data Analytics in E-Commerce

2021 ◽  
pp. 885-924
Author(s):  
Steven Chan Siang Hui ◽  
Omkar Dastane ◽  
Zainudin Johari ◽  
Mardeni Roslee
Keyword(s):  
Big Data ◽  
Customer Satisfaction ◽  
Information Search ◽  
Dynamic Pricing ◽  
Data Analytics ◽  
Recommendation System ◽  
Big Data Analytics ◽  
System Dynamic ◽  
Mediation Effect ◽  
Repurchase Intention

Based on the empirical research, this chapter investigated the impact of big data-based techniques typically used in big-data driven E-commerce such as information search, recommendation system, dynamic pricing, and personalisation on the online repurchase intention in Malaysia. This study also investigated the mediating effect on customer satisfaction. Therefore this study utilised the quantitative research method with an explanatory study to predict the link between dependent and independent variables. Additionally, the snowball sample method was used to select a sample size of 318 working adults in Klang Valley. Next, a self-administered online questionnaire was used to collect the necessary data. The IB, SPSS 22 software was then used to assess the reliability and normality of the variables at the first stage. Next, the Confirmatory Factor Analysis and Structural Equation Modelling were examined via IBM SSS AMOS 22. The findings showed that the big data analytic factors like information search, recommendation system, dynamic pricing, and personalisation had a positive significant impact on customers' repurchase intention. Nonetheless, the mediation effect of customer satisfaction on information search, recommendation system, and dynamic pricing did not encourage the repurchase intention. Then, this chapter discussed the managerial implication, limitations, and future research scope. Finally, this study suggested strategies to enhance online repurchase intention via application of big-data analytics in E-commerce.

Enhancing Online Repurchase Intention via Application of Big Data Analytics in E-Commerce

2021 ◽  
pp. 395-434
Author(s):  
Steven Chan Siang Hui ◽  
Omkar Dastane ◽  
Zainudin Johari ◽  
Mardeni Roslee
Keyword(s):  
Big Data ◽  
Customer Satisfaction ◽  
Information Search ◽  
Dynamic Pricing ◽  
Data Analytics ◽  
Recommendation System ◽  
Big Data Analytics ◽  
System Dynamic ◽  
Mediation Effect ◽  
Repurchase Intention

Based on the empirical research, this chapter investigated the impact of big data-based techniques typically used in big-data driven E-commerce such as information search, recommendation system, dynamic pricing, and personalisation on the online repurchase intention in Malaysia. This study also investigated the mediating effect on customer satisfaction. Therefore this study utilised the quantitative research method with an explanatory study to predict the link between dependent and independent variables. Additionally, the snowball sample method was used to select a sample size of 318 working adults in Klang Valley. Next, a self-administered online questionnaire was used to collect the necessary data. The IB, SPSS 22 software was then used to assess the reliability and normality of the variables at the first stage. Next, the Confirmatory Factor Analysis and Structural Equation Modelling were examined via IBM SSS AMOS 22. The findings showed that the big data analytic factors like information search, recommendation system, dynamic pricing, and personalisation had a positive significant impact on customers' repurchase intention. Nonetheless, the mediation effect of customer satisfaction on information search, recommendation system, and dynamic pricing did not encourage the repurchase intention. Then, this chapter discussed the managerial implication, limitations, and future research scope. Finally, this study suggested strategies to enhance online repurchase intention via application of big-data analytics in E-commerce.

scholarly journals Design and Development of a Real-Time Optimal Route Recommendation System Using Big Data for Tourists in Jeju Island

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 506 ◽  
Author(s):  
Faisal Mehmood ◽  
Shabir Ahmad ◽  
DoHyeun Kim
Keyword(s):  
Big Data ◽  
Real Time ◽  
Recommendation System ◽  
Tourism Industry ◽  
Jeju Island ◽  
Third Party ◽  
User Preference ◽  
Optimal Route ◽  
Route Recommendation ◽  
Travel Route Recommendation

Nowadays researchers and engineers are trying to build travel route recommendation systems to guide tourists around the globe. The tourism industry is on the rise and it has attracted researchers to provide such systems for comfortable and convenient traveling. Mobile internet growth is increasing rapidly. Mobile data usage and traffic growth has increased interest in building mobile applications for tourists. This research paper aims to provide design and implementation of a travel route recommendation system based on user preference. Real-time big data is collected from Wi-Fi routers installed at more than 149 unique locations in Jeju Island, South Korea. This dataset includes tourist movement patterns collected from thousands of mobile tourists in the year 2016–2017. Data collection and analysis is necessary for a country to make public policies and development of the global travel and tourism industry. In this research paper we propose an optimal travel route recommendation system by performing statistical analysis of tourist movement patterns. Route recommendation is based on user preferences. User preference can vary over time and differ from one user to another. We have taken three main factors into consideration to the recommend optimal route i.e., time, distance, and popularity of location. Beside these factors, we have also considered weather and traffic condition using a third-party application program interfaces (APIs). We have classified regions into six major categories. Popularity of location can vary from season to season. We used a Naïve Bayes classifier to find the probability of tourists going to visit next location. Third-party APIs are used to find the longitude and latitude of the location. The Haversine formula is used to calculate the distance between unique locations. On the basis of these factors, we recommend the optimal route for tourists. The proposed system is highly responsive to mobile users. The results of this system show that the recommended route is convenient and allows tourists to visit maximum number of famous locations as compared to previous data.

Contemporary Management Approaches to the Global Hospitality and Tourism Industry

2020 ◽  
pp. 45-57
Author(s):  
Edwin Agwu
Keyword(s):  
Information And Communication Technologies ◽  
Information Search ◽  
Business Models ◽  
Business Environment ◽  
Tourism Industry ◽  
Brick And Mortar ◽  
Hospitality And Tourism ◽  
Management Approaches ◽  
Hospitality And Tourism Industry ◽  
Contemporary Management

This chapter describes how the era of brick and mortar, hitherto called the analogue years, has given way to the era of digits where everything functions with the touch of a button. From agriculture to banking, health to education, information search to manufacturing; and the hospitality and tourism sector is not left out. Information and communication technologies (ICT) has changed the way businesses are done in contemporary ways. The business environment and their managers have been challenged to think of how to integrate the opportunities posed by technology into their business models for competitive advantages.

scholarly journals Research on Precision Marketing Model of Tourism Industry Based on User’s Mobile Behavior Trajectory

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Jialin Zhang ◽  
Tong Wu ◽  
Zhipeng Fan
Keyword(s):  
Big Data ◽  
Recommendation System ◽  
Tourism Industry ◽  
Location Based Service ◽  
Design Data ◽  
Location Services ◽  
Marketing Mode ◽  
Marketing Model ◽  
Data Platform ◽  
Industry Needs

With the deep cross-border integration of tourism and big data, the personalized demand of tourist groups is increasingly strong. Precision marketing has become a new marketing mode that the tourism industry needs to pay close attention to and explore. Based on the advantages of big data platform and location-based service, starting from the precise marketing demand of tourism, we design data flow mining technology framework for user’s mobile behavior trajectory based on location services in mobile e-commerce environment to get user track data that incorporates location information, consumption information, and social information. Data mining clustering technology is used to analyze the characteristics of users’ mobile behavior trajectories, and the precise recommendation system of tourism is constructed to provide support for tourism decision making. It can target the tourist group for precise marketing and make tourists travel smarter.

scholarly journals Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

2021 ◽  
Vol 13 (15) ◽  
pp. 8141
Author(s):  
Haseeb Ur Rehman Khan ◽  
Chen Kim Lim ◽  
Minhaz Farid Ahmed ◽  
Kian Lam Tan ◽  
Mazlin Bin Mokhtar
Keyword(s):  
Systematic Review ◽  
Information And Communication Technologies ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Time Of Day ◽  
Tourism Industry ◽  
Time Data ◽  
Model Framework ◽  
Agenda 2030 ◽  
The Government

Agenda 2030 of Sustainable Development Goals (SDGs) 9 and 11 recognizes tourism as one of the central industries to global development to tackle global challenges. With the transformation of information and communication technologies (ICT), e-tourism has evolved globally to establish commercial relationships using the Internet for offering tourism-related products, including giving personalised suggestions. The contextual suggestion has emerged as a modified recommendation system that is integrated with information-retrieval techniques within large databases to provide tourists with a list of suggestions based on contexts, such as location, time of day, or day of the week (weekdays or weekends). This study surveyed literature in the field of contextual suggestion and recommendation systems with a focus on e-tourism. The concerns linked with approaches used in contextual suggestion and recommendation systems are highlighted in this systematic review, while motivations, recommendations, and practical implications in e-tourism are also discussed in this paper. A query search using the keywords “contextual suggestion system”, “recommendation system”, and “tourism” identified 143 relevant articles published from 2012 to 2020. Four major repositories are considered for searching, namely, (i) Science Direct, (ii) Scopus, (iii) IEEE, and (iv) Web of Science. This review was carried out under the protocols of four phases, namely, (i) query searching in major article repositories, (ii) removal of duplicates, (iii) scan of title and abstract, and (iv) complete reading of articles. To identify the gaps in current research, a taxonomy analysis was exemplified into categories and subcategories. The main categories were highlighted as (i) review articles, (ii) model/framework, and (iii) applications. Critical analysis was carried out on the basis of the available literature on the limitations of approaches used in contextual suggestion and recommendation systems. In conclusion, the approaches used are mainly based on content-based filtering, collaborative filtering, preference-based product ranking, and language modelling. The evaluation measures for the contextual suggestion system include precision, normalized discounted cumulative, and mean reciprocal rank, while test collections comprise Internet resources. Given that the tourism industry contributed to the environmental and social-economic development, contextual suggestion and recommendation systems have presented themselves to be relevant in integrating and achieving SDG 9 and SDG 11 in many ways such as web-based e-services by the government sector and smart gadgets based on reliable and real-time data and information for city planners as well as law enforcement personnel in a sustainable city.

scholarly journals The academic viewpoint on Big data and patient data ownership (as seen in the scientific literature)

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
I Mircheva ◽  
M Mirchev
Keyword(s):  
Public Health ◽  
Big Data ◽  
Medical Research ◽  
Information And Communication Technologies ◽  
Patient Information ◽  
Scientific Literature ◽  
Medical Data ◽  
Patient Data ◽  
Academic Community ◽  
Ethical Challenges

Abstract Background Ownership of patient information in the context of Big Data is a relatively new problem, apparently not yet fully understood. There are not enough publications on the subject. Since the topic is interdisciplinary, incorporating legal, ethical, medical and aspects of information and communication technologies, a slightly more sophisticated analysis of the issue is needed. Aim To determine how the medical academic community perceives the issue of ownership of patient information in the context of Big Data. Methods Literature search for full text publications, indexed in PubMed, Springer, ScienceDirect and Scopus identified only 27 appropriate articles authored by academicians and corresponding to three focus areas: problem (ownership); area (healthcare); context (Big Data). Three major aspects were studied: scientific area of publications, aspects and academicians' perception of ownership in the context of Big Data. Results Publications are in the period 2014 - 2019, 37% published in health and medical informatics journals, 30% in medicine and public health, 19% in law and ethics; 78% authored by American and British academicians, highly cited. The majority (63%) are in the area of scientific research - clinical studies, access and use of patient data for medical research, secondary use of medical data, ethical challenges to Big data in healthcare. The majority (70%) of the publications discuss ownership in ethical and legal aspects and 67% see ownership as a challenge mostly to medical research, access control, ethics, politics and business. Conclusions Ownership of medical data is seen first and foremost as a challenge. Addressing this challenge requires the combined efforts of politicians, lawyers, ethicists, computer and medical professionals, as well as academicians, sharing these efforts, experiences and suggestions. However, this issue is neglected in the scientific literature. Publishing may help in open debates and adequate policy solutions. Key messages Ownership of patient information in the context of Big Data is a problem that should not be marginalized but needs a comprehensive attitude, consideration and combined efforts from all stakeholders. Overcoming the challenge of ownership may help in improving healthcare services, medical and public health research and the health of the population as a whole.

Sign in / Sign up

Export Citation Format

Share Document