Study on the Effect of Consumer Experience on Food Delivery Apps | Chapter 9 | An Overview on Business, Management and Economics Research Vol. 7

 This chapter primarily focuses on the relationships between the determinants that affect consumers’ use of food delivery apps in Malaysia. Food service businesses now rely on technology as a major information resource and marketing tool. Using an extended flow theory model, we explored consumers’ experiences in purchasing delivery food through mobile apps. This study chose Grab Food as the popular food delivery app in Malaysia. This study collected data from an online survey and an offline survey conducted among postgraduate students at public universities. This study used a dichotomous scale to measure online purchasing behaviour and the research model of this study was tested using Partial Least Squares (PLS) with PLS Graph 3.0.

 

We distributed a self-administered questionnaire online and used structural equation modeling to test the hypotheses. We found that consumer experience (web and digital) had a significant effect on buying intention behavior. The empirical findings show that consumers’ experience has significant effect on buying behavior when using the application. Consumer experience in term of the usability, interactivity and aesthetic of the web positively affects food delivery apps buying intention behavior. Additionally, this study found that consumers had experiences buying from the website based on functionality rather than psychology and content factors. Further, this study finds that consumers had experience buying from the website are based on the functionality rather than psychology and content factors. Furthermore, digital experience demonstrates a stronger effect on buying behavior with more experience using the food delivery application. This study is one of the early studies to investigate the role of consumer experience. In addition, we find that in user’s first interaction with food delivery apps, web experience (usability, interactivity, aesthetic) and digital experience have larger impact on their buying intention behaviour.

Author(s) Details:

Nina Farisha,
University of Malaya, Malaysia.

Norhayati Mat Yusoff Mohd Yusof,
Universiti Teknologi MARA, Malaysia.

Irina Mohd Akhir,
Universiti Teknologi Mara (Pulau Pinang), Malaysia.

Suriati Osman,
Universiti Teknologi MARA, Malaysia.

Please see the link here: https://stm.bookpi.org/AOBMER-V7/article/view/13255

Determination of Mobile Learning Application Usability: A Pattern Mining Approach that Goes Beyond the Statistical Method | Chapter 13 | New Visions in Science and Technology Vol. 6

 In today's world, the use of mobile learning applications in student-centered learning is quickly increasing. To offer the best academic outcome on their learning, student satisfaction must be taken into account. As a result, testing the usability of mobile learning systems (MLS) is critical. The usability of earlier MLS has been tested using a variety of statistical methodologies. The study's main goals are to assess the usability of the mobile learning system using a data science method and to compare it to a statistical technique. Questionnaire results from 100 students were used to evaluate the proposed mobile learning system using a data science approach. Two pattern mining algorithms, Apriori and FP-Growth, were used to analyse these replies. The Apriori algorithm ensures 94 percent system usability, while the FP-Growth algorithm ensures 93 percent system usability, according to the results. The aggregate mean value 4.083 of the questionnaire responses was calculated as the system's usability using the statistical approach. Finally, it is determined that while determining the usability of MLS, the pattern mining approach is more obvious than the statistical method.

Author (S) Details

 

D. D. M. Dolawattha

Department of Geography, Faulty of Social Sciences, University of Kelaniya, Sri Lanka.

  H. K. S. Premadasa

Centre for Computer Studies, Sabaragamuwa University of Sri Lanka, Sri Lanka.

View Book :- https://stm.bookpi.org/NVST-V6/article/view/4144