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Mahadevan, Lakshman
- User Acceptance and Usage of Food App – A Moderating Influence of Work-family Conflict using Extended TAM
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Authors
Affiliations
1 Assistant Professor, Rajagiri Business School, Kochi, Kerala, IN
2 Florida Gulf Coast University, Florida, US
1 Assistant Professor, Rajagiri Business School, Kochi, Kerala, IN
2 Florida Gulf Coast University, Florida, US
Source
Journal of Hospitality Application and Research, Vol 17, No 2 (2022), Pagination: 95-122Abstract
Purpose/Aim: The paper aims at the technology adoption of a food app. The study is conducted in India using the TAM Model and investigates how work-family conflict moderates the relationship between Behavioral Intention and independent variables like Perceived Value, Perceived Ease of Use and Perceived Usefulness. The study also investigates if there is a significant difference in behavioral intention controlling for socio-demographic factors. Design/Methodology/Approach: A survey was conducted among 273 respondents across India. A convenient and snowball sampling was used for this purpose and the respondents were working professionals. Confirmatory analysis was conducted to test the measurement model and structural equation modelling was done to test for moderation effects. The analysis was conducted in R Studio. Findings: The study found that Perceived Value has high significance in the adoption of food tech apps. The moderating effect of work-family conflict is very significant in the case of Value and Perceived Usefulness. However, work-family conflict does not have a moderating effect on Perceived Ease of Use. There is a significant difference in behavioral intention accounting for control variables like age and spouse working. Research Limitations: This study was conducted in India. Future research can use this model to study the phenomenon of food app adoption in other countrieswhile adding important constructs like personal innovativeness, network effects, and habit if required. Quota sampling can be done to pick a minimum number of respondents for each socio-demographic indicator for better explanatory power. Practical Implications: The study provides online aggregator companies, data on how work-family conflict affects the adoption of the app. Companies like Swiggy and Zomato could improve perceived value irrespective of absence of work-family conflict. Restaurants can use this study to understand how they can add further value to make sure their services are attractive to even customers with less workfamily conflict. Originality/Value : The findings allow the factors that can influence the adoption of food apps in India to be understood. Unlike existing studies based on Technology Acceptance Model (TAM), this study includes perceived value and how work-family conflict moderates the relationship between Perceived Usefulness, Perceived Value and Perceived Ease of Use with Behavioral Intention. The research also investigates the significant differences in behavioral intention controlling for factors like amily type, Age, Gender and Marital status. Most studies on adoption of apps are either generalized or more focused on sectors like banking and shopping. By focusing on India, this model can also be applied to other countries which are relatively new to food app adoption.Keywords
Food App, Technology Adoption, Work-family Conflict, Perceived Value
References
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- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
- Davis, F. D., Bogozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982-1003.
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- An Applied Research into Understanding how Socio-demographic Factors Affect the Eating of Outside Food for Urban Population
Abstract Views :155 |
PDF Views:0
Authors
Affiliations
1 Assistant Professor, Rajagiri Business School, Kochi, Kerala, IN
2 Florida Gulf Coast University, Florida, US
1 Assistant Professor, Rajagiri Business School, Kochi, Kerala, IN
2 Florida Gulf Coast University, Florida, US
Source
Journal of Hospitality Application and Research, Vol 17, No 1 (2022), Pagination: 19-34Abstract
Purpose/Aim – This article aims at analyzing the consumers’ eating out habits and preferences as to restaurant service attributes and factors affecting eating out, through a primary survey of 193 urban respondents who were interviewed through a structured questionnaire. For leveraging consumer insight about eating outside food, and to understand how socio-demographic factors affecting eating outside food are important to the restaurant industry for developing strategies to attract potential consumers in order to tap the emerging market potential. Eating of outside food corresponds to both dining out as well as ordering through food apps. The study also investigates how the COVID pandemic has affected the consumption of outside food. Design/Methodology/Approach – A survey was initiated, and data collected from 193 respondents across urban areas. A convenient sampling was used for the purpose. A Random Forest was used to find the key socio-demographic factors influencing the eating of outside food. Paired t-test to test differences between pre-COVID and the current situation for dine out as well as ordering through apps was conducted. The analysis was conducted in R studio. Findings – The study finds there is significant difference in eating of outside food (both through apps as well as dine out) between pre-COVID times and the current situation. It also finds that family type, education, annual income and occupation are significant factors in the ordering of food through food apps and age is found to be a significant factor where people prefer to dine out. Research Limitations – The study was conducted in India and this study can be replicated in the adoption of food apps in other countries as well. Important constructs like personal innovativeness, network effects and habit are not yet studied. Sample size should have been larger and wider for better explanatory power. Explanatory power will be high when we include behavioural constructs along with the socio-demographic variables. Practical Implications – Food tech companies can make a segmented marketing approach using this research. The study results provide practical implications for the Indian restaurant and food tech industry. It provides strategic inputs to the food tech industry for developing effective strategies as per consumers socio-demographic attributes. Restaurants also can use this report to get insight into their customers. Restaurants can customize menu according to different socio-demographics. Originality/Value – There has been no research conducted in understanding how socio-demographic factors affect the app usage as well as dining out during the context of COVID. Previous research studies have not looked into the family type, Occupation type, Sector in which the person works and the type of housing. A Random Forest model would provide us with key significant socio-demographic variables and hence this study could be used.Keywords
Dine Out, Outside Food, Food App, COVID, Paired T-testReferences
- Ajzen, I. (1991). The theory of planned behaviour. Organizational Behaviour and Human Decision Processes, 50(2), 179-211.
- Bahmanziari, T., Pearson, J. M., & Crosby, L. (2003). Is trust important in technology adoption? A policy capturing approach. The Journal of Computer Information Systems, 43(4), 46-54.
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- Dossey, L. (2014). FOMO, digital dementia, and our dangerous experiment. Explorations, 10(2), 69-73.
- Ghalandari, K. (2012). The effect of performance expectancy, effort expectancy, social influence and facilitating conditions on acceptance of e-banking services in Iran: The moderating role of age and gender. Middle-East Journal of Scientific Research, 12(6), 801-807.
- Annaraud, K., & Berezina, K. (2020). Predicting satisfaction and intentions to use online food delivery: What really makes a difference? Journal of Foodservice Business Research, 1-19. doi:10.1080/15378 020.2020.1768039
- Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts, United States: Sloan School of Management, Massachusetts Institute of Technology.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
- Davis, F. D., Bogozzi, R., P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982-1003.
- Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475-487.
- Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments Internet. J. Human-Comput. Stud., 45, 19-45.
- Dapp. T, Stobbe, A., & Wruuck, P. (2012, December 20). The future of (mobile) payments - New (online) players competing with banks (pp. 1-31). Deutsche Bank Research.
- Dewan, & Chen. (2005). Mobile payment adoption in the US: A cross-industry, cross platform solution. Journal of Information Privacy and Security, 1(2), 4-25.
- Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, Mass; Don Mills, Ontario: Addison-Wesley Pub. Co.
- Goodhue, D. L., & Thompson, R. L. (1995). Task technology fit and individual performance. MIS Quarterly, 19, 213-236.
- Venkatesh, V., Morris, M. G., Davis, F. D., & Davis, G. B. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425-478.
- Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Science, 39(2), 273-312.
- Yi, M. Y., Jackson, J. D., Park, J. S. & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43(3), 350-363.
- Parasuraman, A., Valarie A., Z., & Leonard L., B. (1988). Servqual: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40.
- Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). E-S-QUAL a multiple-item scale for assessing electronic service quality. Journal of Service Research, 7(3), 213-233.
- Parasuraman, A., Zeithaml, V., & Berry, L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49(4), 41-50.
- Cho, M., Bonn, M. A., & Li, J. (Justin). (2018). Differences in perceptions about food delivery apps between single-person and multi-person households. International Journal of Hospitality Management. doi:10.1016/j.ijhm.2018.06.019
- Huang, E. Y., Lin, S. W., & Fan, Y. C. (2015). MS-QUAL: Mobile service quality measurement. Electronic Commerce Research and Applications, 14(2), 126-142.
- Tan, F. B., & Chou, J. P. (2008). The relationship between mobile service quality, perceived technology compatibility, and users’ perceived playfulness in the context of mobile information and entertainment services. International Journal of Human-Computer Interaction, 24(7), 649-671.