IJBA Journal Issues
September 2021
A conceptual review on the mediating effect of patient satisfaction towards patient loyalty in the dental practice in Thailand
Supaprawat Siripipatthanakul, Chok Nyen Vui
Abstract : This study reviewed the mediating effect of patient satisfaction between dental practice-related factors and patient loyalty in dental healthcare services (dental practice). This research identifies the link between measurements regarding their efforts to increase dental practice quality to respond to patients’ needs and expectations, influencing patient satisfaction and turn it into patient loyalty. The elements of dental practice-related factors include Dentist Services, Staff Services, Prices, and Facilities. The healthcare sector shows the relationship between these three measurements in the theoretical framework in this review article.
Keywords: Dental Practice, Services, Patient Satisfaction, Revisit, Patient Loyalty
Bibliography
Supaprawat, S., & Chok, N. V. (2021). A conceptual review on the mediating effect of patient satisfaction towards patient loyalty in the dental practice in Thailand. International Journal of Behavioral Analytics, 1(2), 1–16.
In-text citation
According to new research… (Supaprawat & Chok, 2021).
In research from Supaprawat and Chok (2021) …
Dental practice-related factors and patient loyalty in dental clinics, Laem Chabang, Thailand : The mediating role of patient satisfaction
Supaprawat Siripipatthanakul, Chok Nyen Vui
Abstract : This study investigates the mediating effect of patient satisfaction between dental practice related factors and patient loyalty in the dental clinics, Laem Chabang, Thailand. This research identifies the link between measurements regarding their efforts to increase dental practice quality to respond to patients’ needs and expectations, influencing patient satisfaction and turn it into patient loyalty. The dental clinics in Laem Chabang explain relationship between these three measurements in this sector. The researchers developed the questionnaire items from previous research that has validity. The elements of dental practice-related factors include dentist services, staff services, prices, and facilities. Stratified random sampling of 385 cases was in a data collection and distribution through printed self-administered questionnaires (Likert’s rating scale) to the dental clinic’s patients in Laem Chabang, Thailand. The PLS-SEM results showed that patient satisfaction is a significant mediator between dental practice-related factors (prices, facilities, dentist services, and staff services) and patient loyalty. It also reveals that staff services, dentist services, and prices directly affect patient satisfaction.
Keywords: Dental Practice, Services, Patient Satisfaction, Revisit, Recommendation, Loyalty
Bibliography
Supaprawat, S., & Chok, N. V. (2021b). Dental practice-related factors and patient loyalty in dental clinics, Laem Chabang, Thailand : The mediating role of patient satisfaction. International Journal of Behavioral Analytics, 1(2), 1–17.
In-text citation
According to new research… (Supaprawat & Chok, 2021).
In research from Supaprawat and Chok (2021) …
The role of big data analytics in influencing artificial intelligence (AI) adoption for coffee shops in Krabi, Thailand
Pongsakorn Limna, Supaprawat Siripipatthanakul, Bordin Phayaphrom
Abstract : This study aims to explain the role of big data analytics and artificial intelligence (AI) adoption in coffee shops in Krabi its effect towards brand authenticity, brand sentiment, and customer services. The data was collected through in-depth interviews from six purposive samples of coffee shop owners in Krabi. The study findings shows that the coffee shop owners perceived big data analytics and artificial intelligence (AI) is necessary for businesses. The implementation of new technologies and transforming the coffee shops into digital enterprises aid success. The results could benefit coffee shop owners by improving their brand authenticity-brand sentiment and services to respond to customer behavior in a digital era. Furthermore, owners in any industry could improve data analytics and artificial intelligence (AI) management to adapt the business model to their consumer behavior and increase customer satisfaction and loyalty. Finally, high business performance will incur.
Keywords: Big Data Analytics, Artificial Intelligence (AI), Brand Authenticity Sentiment, Consumer Behavior Analysis
Bibliography
Limna, P., Siripipatthanakul, S., & Phayaphrom, B. (2021). The Role of Big Data Analytics in Influencing Artificial Intelligence (AI) Adoption for Coffee Shops in Krabi, Thailand. International Journal of Behavioral Analytics, 1(2), 1–18.
In-text citation
According to new research… (Limna et al., 2021).
In research from Limna et al. (2021) …
List of References
’Click
Boire, R. (2018). Understanding AI in a world of big data. Big Data & Information Analytics. Caulfield, J. (2019). Methodology: How to Do Thematic Analysis. Scribbr. Retrieved from https://www.scribbr.com/methodology/thematic-analysis/.
Chiang, W.-Y. (2018), “Applying data mining for online CRM marketing strategy: An empirical case of coffee shop industry in Taiwan”, British Food Journal, Vol. 120 No. 3, pp. 665-675. https://doi.org/10.1108/BFJ-02-2017-0075
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24- 42.
Dhanabalan, T., & Sathish, A. (2018). Transforming Indian industries through artificial intelligence and robotics in industry 4.0. International Journal of Mechanical Engineer-ing and Technology, 9(10), 835-845.
Elish, M. C., & Boyd, D. (2018). Situating methods in the magic of Big Data and AI. Communi cation monographs, 85(1), 57-80.
Ghani, W. S. A., Azman, N. A. A., Rashid, N. M., & Halim, A. H. A. (2020). The Relationship of Brand Marketing Communication and Brand Authenticity. Journal of International Business, Economics and Entrepreneurship, 5(1), 43-49.
Huang, P. Y., Ling, H. C., & Wang, R. Y. (2021). Effective Combination and Analysis of” Big Data” and” Classic Marketing”. International Journal of Organizational Innovation (Online), 13(3), 125-131.
Jain, P., & Aggarwal, K. (2020). Transforming Marketing with Artificial Intelligence. International Research Journal of Engineering and Technology, 7(7), 3964-3976.
Jitpleecheep, P. (2020). Deliveries Beef Up Number of 2019 Eatery Openings. Bangkok Post.
Jitpleecheep, P., & Hicks, W. (2019). Wake up and sell the coffee. Bangkok Post.
Katz, E. (2020). Does Brand Sentiment Still Matter?. Oktopost. Retrieved from https://www.ok topost.com/blog/brand-sentiment-still-matter/.
Khatri, M. (2021). How Digital Marketing along with Artificial Intelligence is Transforming Consumer Behaviour?. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 9(7), 523-527.
Ogbuokiri, B.O. MSc., Udanor C.N. Ph.D & Agu, M.N. Ph.D. (2015). Implementing big data analytics for small and medium enterprise (SME) regional growth. IOSR Journal of Computer Engineering, 17 (6), 35-43.
Rahmani AM, Azhir E, Ali S, Mohammadi M, Ahmed OH, Yassin Ghafour M, Hasan Ahmed S, Hosseinzadeh M. (2021). Artificial Intelligence Approaches and Mechanisms for Big Data Analytics: A Systematic Study. PeerJ Computer Science 7:e488.
Ram, J., Zhang, C., & Koronios, A. (2016). The implications of big data analytics on business intelligence: A qualitative study in China. Procedia Computer Science, 87, 221-226.
Recker, J. (2013). Scientific Research in Information Systems: A Beginner’s Guide. Springer Berlin Heidelberg, Brisbane, QLD, Queensland, Australia. Riley,
M. F. (2018). Big data, HIPAA, and the Common Rule: Time for big change. Big data, health law, and bioethics, Cambridge University Press, Cambridge, 251-264.
Ranjan, J. (2009). Business intelligence: Concepts, components, techniques and benefits. Jour-nal of theoretical and applied information technology, 9(1), 60-70.
Satish, L & Yusof, N (2017). A review: big data analytics for enhanced customer experiences with crowdsourcing. Procedia computer science, 116, 274-283.
Shirdastian, H., Laroche, M., & Richard, M. O. (2017). Using big data analytics to study brand authenticity sentiments: The case of Starbucks on Twitter. International Journal of Infor mation Management, 48, 291-307.
McCombes, S. (2019). Methodology: An Introduction to Research Methods. Scribbr. Retrived from https://www.scribbr.com/methodology/sampling-methods/.
Ractham, V. & Charoendechathon, K. (2020). Expectation that Influence Big Data Usage of SMEs Business Entrepreneurs. College of Management, Mahidol University. Retrieved from https://archive.cm.mahidol.ac.th/handle/123456789/3540.
Watson, H. J. (2014). Tutorial: Big Data Analytics: Concepts, Technologies, and Applications. Communications of the Association for Information Systems, 34 (1), 65. pp-pp. https://doi.org/10.17705/1CAIS.03462.
The implementation of business intelligence using data analytics and its effects towards performance in hotel industry in Thailand
Panithan Tong-On, Supaprawat Siripipatthanakul, Bordin Phayaphrom
Abstract : The hotel industry is one of Thailand’s most important economic sectors and is in a fiercely competitive market these days. It is flooded with new technologies as driver for innovation. The hotel industry is a data-intensive industry that collects large amounts of data in various forms. The study aims to investigate business intelligence activities using data analytics and the impact on corporate performance in the hotel industry in Thailand. The qualitative method-ology is employed to explain the relationship between business intelligence-data analytics and performance in Thailand’s hotel organizations, in-depth interviews is conducted with eight purposive samples of hotel managers and finance controllers in Thailand. The study’s findings revealed that business intelligence and data analytics impacted the business performance of hotel industry in Thailand. The respondents expressed an interest in artificial intelligence and believed that the rapid advancement of artificial intelligence in hotel management should be considered to improve corporate performance.
Keywords: Data Analytics, Business Intelligence (BI), Artificial Intelligence (AI), Corporate Performance, Qualitative Study
Bibliography
Tong-On, P., Siripipatthanakul, S., & Phayaphrom, B. (2021). The implementation of business intelligence using data analytics and its effects towards performance in hotel industry in Thailand. International Journal of Behavioral Analytics, 1(2), 1–16.
In-text citation
According to new research… (Tong-On et al., 2021).
In research from Tong-On et al. (2021) …
List of References
’Click
Aho, M. (2010). The distinction between Business Intelligence and corporate performance management- a literature study combined with empirical findings. Proceedings of the MCSP 2010 Conference.
Big Data Analytics (2021) Big Data Analytics in the Hotel Industry. http://www.bigdatanalysis.com/big-data-analytics-in-hotel-industry/
Daryaei, M., Shirzad, M., & Kumar, V. (2013, July). Adoption of business intelligence: In the hotel industry. In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
Höpken, W., Fuchs, M., Keil, D., & Lexhagen, M. (2015). Business intelligence for cross-process knowledge extraction at tourism destinations. Information Technology & Tourism, 15(2), 101-130.
Jaklič, J., Grublješič, T., & Popovič, A. (2018). The role of compatibility in predicting business intelligence and analytics use intentions. International Journal of Information Management, 43, 305–318.
Korte, D., Ariyachandra, T., & Frolick, M. (2013). Business intelligence in the hospitality industry. International Journal of Innovation, Management and Technology, 4(4), 429.
Lai, W.-C., & Hung, W.-H. (2018). A Framework of Cloud and AI based Intelligent Hotel, 8.
Llave, M. R. (2017). Business intelligence and analytics in small and medium-sized enterprises: A systematic literature review. Procedia Computer Science, 121, 194–205.
Mariani, M., Baggio, R., Fuchs, M., & Höepken, W. (2018). Business intelligence and big data in hospitality and tourism: A systematic literature review. International Journal of Contemporary Hospitality Management, 30(12), 3514–3554. https://doi.org/10.1108/IJCHM-07-2017-0461
Marina, K. (2019). Processing and evaluating big data and business analytics in tourism: 49.
Mariani, M. (2019). Big data and analytics in tourism and hospitality: a perspective article. Tourism Review.
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Re-search, 98, 261–276.
Nyabuti, R. (2018). The Impact of Business Intelligence on Corporate Performance Management: A Study of Equity Bank.
Padberg, M. D. (2015). Big Data and Business Intelligence: a data-driven strategy for e-commerce organizations in the hotel industry (Master’s thesis, University of Twente).
Pierce, A. (n.d.). Pierceaj@techtoday.us; follow on Twitter @TechToday_us. 2.
Ram, J., Zhang, Changyu., & Koronios, A. (2016). The Implications of Big Data Analytics on Business Intelligence: A Qualitative Study in China. Procedia Computer Science, 87, 221–226. https://doi.org/10.1016/j.procs.2016.05.152
Richards, G., Yeoh, W., Chong, A. Y. L., & Popovič, A. (2019). Business intelligence effectiveness and corporate performance management: An empirical analysis. Journal of Computer Information Systems, 59(2), 188–196.
Rikhardsson, P., & Yigitbasioglu, O. (2018). Business intelligence & analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems, 29, 37–58.
Russom, P. (2011). Big data analytics. TDWI Best Practices Report, Fourth Quarter, 19(4), 1–34.
Shahini, R. (2020). Business Intelligence in the Hotel Industry.
Siripipatthanakul, S., & Bhandar, M. (2021). A qualitative research factors affecting patient satisfaction and loyalty: A case study of Smile Family Dental Clinic. International Journal of Trend in Scientific Research and Development.
Siripipatthanakul, S., & Chana, P. (2021). Service Marketing Mix (7Ps) and Patient Satisfaction in Clinics: A Review Article. International Journal of Trend in Scientific Research and Development. 842-850.
Siripipatthanakul, S., & Puttharak, S. (2021). Marketing Mix Analysis in Private Dental Healthcare Service Sector. International Journal of Trend in Scientific Research and Development, 827-831.
Siripipatthanakul, S (2021) Service Quality, Patient Satisfaction, Word of Mouth and Re-visit Intention in a Dental Clinic, Thailand. International Journal of Trend in Scientific Research and Development. 832-841.
Siripipatthanakul, S., & Sixl-Daniell, K. (2021). A Review Article Branding Dental Clinic through Corporate Social Responsibility (CSR). International Journal of Trend in Scientific Research and Development. 5(5): 866-876.
Van Niekerk, M. (2016). Business, technology, and marketing trends influencing the financial performance of the hotel industry. The Journal of Hospitality Financial Management, 24(2), 153-157.
Yadegaridehkordi, E., Nilashi, M., Shuib, L., Nasir, M. H. N. B. M., Asadi, S., Samad, S., & Awang, N. F. (2020). The impact of big data on firm performance in the hotel industry. Electronic Commerce Research and Applications, 40.
Kim, J. J., & Han, H. (2020). Hotel of the future: Exploring the attributes of a smart hotel adopting a mixed-methods approach. Journal of Travel & Tourism Marketing, 37(7), 804–822. https://doi.org/10.1080/10548408.2020.1835788
Varshney, M., Garg, S., Rajpoot, J., (2017). A Study on Issues, Challenges and Application in Data Science. International Journal of Trend in Scientific Research and Development, Volume-1(Issue-5), 526–533. https://doi.org/10.31142/ijtsrd2340
Effect of electronic word of mouth (e-WOM) and perceived value on purchase intention during the COVID-19 pandemic: the case of ready-to-eat food
Wannalak Sosanuy, Supaprawat Siripipatthanakul, Wasutida Nurittamont, Bordin Phayaphrom
Abstract : This study examines whether electronic word-of-mouth (e-WOM) and customer’s perceived value affect the purchase intention on ready-to-eat food. The contribution could be more understanding of the impact of electronic word of mouth (e-WOM) and customer’s perceived value on purchase intention on ready-to-eat food to enhance the digital marketing strategy effectiveness. The research model was analyzed from 417 ready-to-eat food customers (respondents) in Satun, Thailand. The data were analyzed using the multiple regression analysis (MRA). It reveals that electronic word of mouth (e-WOM) and customer perception in monetary value, hedonic value, and utilitarian value significantly influenced purchase intention of ready-to-eat food. The utilitarian value had the most influence on purchase intention, followed by hedonic value, electronic word of mouth (e-WOM), and monetary value.
Keywords: electronic word of mouth (e-WOM), perceived value, ready-to-eat, purchase intention, digital marketing
Bibliography
Sosanuy, W., Siripipatthanakul, S., Nurittamont, W., & Phayaphrom, B. (2021). Effect of electronic word of mouth (e-WOM) and perceived value on purchase intention during the COVID-19 pandemic: the case of ready-to-eat food. International Journal of Behavioral Analytics, 1(2), 1–16.
In-text citation
According to new research… (Sosanuy et al., 2021).
In research from Sosanuy et al. (2021) …
List of References
’Click
Ait Youssef, A., Jaafari, M., & Belhcen, L. (2020). Factors Affecting the Online Purchase Intention during COVID-19 Crisis: The Case of Morocco. Available at SSRN 3734389.
Ayuniyyah, Q., Hafidhuddin, D., & Hambari, H. (2017). Factors affecting consumers’ decision in purchasing MUI halal-certified food products. Tazkia Islamic Finance and Business Review, 10(2).
Bansal, H. S., & Voyer, P. A. (2000). Word-of-mouth processes within a services purchase decision context. Journal of service research, 3(2), 166-177.
Basaran U., & Buyukyilmaz O. (2015). The Effects of Utilitarian and Hedonic Values on Young Consumers Satisfaction and Behavioral Intentions. Eurasian Journal of Business and Economics, 8(16), 1-18.
Ben Hassen, T., El Bilali, H., & Allahyari, M. S. (2020). Impact of COVID-19 on food behavior and consumption in Qatar. Sustainability, 12(17), 6973.
Boyacι-Gündüz, C. P., Ibrahim, S. A., Wei, O. C., & Galanakis, C. M. (2021). Transformation of the Food Sector: Security and Resilience during the COVID-19 Pandemic. Foods, 10(3), 497.
de Morais Watanabe E. A., Alfinito S., & Curvelo I. . (2020). Perceived value, trust and purchase intention of organic food: a study with Brazilian consumers. British Food Journal. Retrieved from British Food Journal.
Eti I. A., Horaira M. A., & Bari M. M. (2021). Power and stimulus of social media marketing on consumer purchase intention in Bangladesh during the COVID-19. International Journal of Research in Business and Social Science, (2147-4478), 10(1), 28-37.
Fan, Y. W., & Miao, Y. F. (2012). Effect of electronic word-of-mouth on consumer purchase intention: The perspective of gender differences. International Journal of Electronic Business Management, 10(3), 175.
Fanelli, R. M. (2021). Changes in the Food-Related Behaviour of Italian Consumers during theCOVID-19 Pandemic. Foods, 10(1), 169.
Ghali Z. Z. (2020). Effect of utilitarian and hedonic values on consumer willingness to buy and to pay for organic olive oil in Tunisia. British Food Journal.
Hassen, T. B., El Bilali, H., Allahyari, M. S., Berjan, S., & Fotina, O. (2021). Food purchase and eating behavior during the COVID-19 pandemic: A cross-sectional survey of Russian Adults. Appetite, 165, 105309.
Hidayanto, A. N., Ovirza, M., Anggia, P., Budi, N. F. A., & Phusavat, K. (2017). The roles of electronic word of mouth and information searching in the promotion of a new e-commerce strategy: A case of online group buying in Indonesia. Journal of theoretical and applied electronic commerce research, 12(3), 69-85.
Kazakeviciute A., & Banyte J. (2012). The relationship of consumers ‘perceived hedonic value and behavior. Engineering Economics, 23(5), 532-540.
Kepios. (2021). Digital 2021 : Thailand. Retrieved from http://www.datareportal.com: https://datareportal.com/reports/digital-2021-thailand
Kohli, S., Timelin, B., Fabius, V., & Veranen, S. M. (2020). How COVID-19 is changing consumer behavior–now and forever.
Lee C. H., & Wu J. J. (2017). Consumer online flow experience: The relationship between utilitarian and hedonic value, satisfaction and unplanned purchase. Industrial Management & Data Systems.
Lubis, T. A. (2021, August). Hedonic Value and Utilitarian Value on Product Purchasing Decision in Food Truck Business. In The 3rd Green Development International Conference (GDIC 2020) (pp. 21-26). Atlantis Press.
Masri, N. W., Ruangkanjanases, A., & Chen, S. C. (2021). The effects of product monetary value, product evaluation cost, and customer enjoyment on customer intention to purchase and reuse vendors: institutional trust-based mechanisms. Sustainability, 13(1), 172.
Nachit, H., & Belhcen, L. (2020). Digital Transformation in Times of COVID-19 Pandemic: The Case of Morocco. Available at SSRN 3645084.
Nguyen M.H., & Khoa B.T. (2019). Customer electronic loyalty towards online business: The role of online trust, perceived mental benefits and hedonic value. The Journal of Distribution Science, 17(12), 81-93.
Nurittamont, W. (2020). Enhancing Electronic Word of Mouth through Customer Satisfaction for Young Customers’ Mobile Phone Banking Applications. International Journal of Innovation, Creativity and Change,13, (12), 661-637.
Nurittamont, W. (2021). The Role of E-WOM Communication impact to Consumer’s Purchasing Intention to Healthy Food Products: An Empirical Study to Testing the Mediator and Moderator Variables. International Journal of Innovation, Creativity and Change,15, (4), 637-652.
Nurittamont, W. (2021). Enhancing the factors influence on purchasing decision of endowment insurance: Case of testing mediate and moderate variables. Journal of management Information and Decision Sciences, 24(7), 1-11.
Perkasa, D. H., Suhendar, I. A., Randyantini, V., & Andrini, E. (2020). The effect of electronic word of mouth (ewom), product quality and price on purchase decisions. Dinasti International Journal of Education Management And Social Science, 1(5), 695-706.
Parsian, N., & Dunning, T. (2009). Developing and validating a questionnaire to measure spirituality: A psychometric process. Global journal of health science, 1(1), 2-11.
Puriwat, W., & Tripopsakul, S. (2021). Customer Engagement with Digital Social Responsibilityin Social Media: A Case Study of COVID-19 Situation in Thailand. The Journal of Asian Finance, Economics, and Business, 8(2), 475-483.
Safitri I. (2018). The Influence of Product Price on Consumers’ Purchasing Decisions. Review of Integrative Business and Economics Research.
Solomon M. R. (1996). Consumer Behavior (3 rd ed). Englewood Cliff, NJ.: Prentice-Hall.
Statista. (2021). Global digital population as of January 2021. Retrieved from http://www.statista.com: https://www.statista.com/statistics/617136/digital-population-worldwide/
Statista. (2021). Ready-to-Eat Meals. Retrieved from http://www.statista.com:https://www.statista.com/outlook/cmo/food/convenience-food/ready-to-eat-meals/thailand
We are social. (2021). Digital 2021. Retrieved from http://www.wearesocial.com: https://wearesocial.com/digital-2021
Werneck, A. O., Silva, D. R., Malta, D. C., Gomes, C. S., Souza-Júnior, P. R., Azevedo, L. O.,& Szwarcwald, C. L. (2021). Associations of sedentary behaviours and incidence of unhealthy diet during the COVID-19 quarantine in Brazil. Public health nutrition, 24(3), 422-426.
Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2003). Business research methods, 7th ed. Thomson/South-Western.