Automation of Direct Sales Processes as a Driver of Economic Efficiency in the Restaurant Industry: A Descriptive Study
DOI:
https://doi.org/10.57125/FS.2023.09.20.06Keywords:
Digital tools, Business optimisation, Sales management, Automated systems, Economic productivityAbstract
As a contributor to economic efficiency, this paper explores the automation of direct sales management in the restaurant industry for business optimisation. The study attempts to examine the effect of automated systems on labour cost, sales increase, customer satisfaction and overall profitability in restaurants. A comparative analysis of 100 restaurants (50 of which have used automation technologies and 50 with no automation technologies) was conducted using the descriptive research methodology. Critical performance indicators data was collected over six months and analysed statistically using t-tests. The results indicate significant benefits associated with automation: According to restaurants that applied automated systems, they achieved a 15 per cent decrease in labour costs, a 20 per cent rise in monthly sales, a 10 per cent better customer satisfaction and a 25 per cent increase in profit margins. These findings challenge conventional perceptions about restraint business, which need to rely on hands-on methods. They propose that restaurants can profit from looking to digital tools to improve business performance incrementally. The study says the march for automation is not a trend but an opportunity to increase economic productivity in the restaurant industry. These systems will benefit most from the strategic implementation of automation technologies based on restaurant type, continuous staff training, and recommendations. The practical implications of automation are highlighted in this research, which contributes to existing literature and informs restaurant operators and policymakers seeking to thrive in an ever more competitive market.
References
Afaq, A., Gaur, L., & Singh, G. (2023). Social CRM: Linking the dots of customer service and customer loyalty during COVID-19 in the hotel industry. International Journal of Contemporary Hospitality Management, 35(3), 992–1009. https://doi.org/10.1108/IJCHM-04-2022-0428
Betrand, C. U., Onyema, C. J., & Awaji, N. M. (2023). Automated hotel management information system. Journal of Research in Engineering and Computer Sciences, 1(1), 19–28. https://hspublishing.org/JRECS/article/view/78
Chakraborti, J., Dutta, A., & Jana, B. (2022). An empirical investigation into why startups resist use of digital marketing. Journal of Content, Community & Communication, 15(8), 69–83. https://doi.org/10.31620/JCCC.06.22/06
Daradkeh, F. M., Hassan, T. H., Palei, T., Helal, M. Y., Mabrouk, S., Saleh, M. I., ... & Elshawarbi, N. N. (2023). Enhancing digital presence for maximizing customer value in fast-food restaurants. Sustainability, 15(7), Article 5690. https://doi.org/10.3390/su15075690
Elhajjar, S., Yacoub, L., & Yaacoub, H. (2023). Automation in business research: Systematic literature review. Information Systems and e-Business Management, 21(3), 675–698. https://doi.org/10.1007/s10257-023-00645-z
Fainshtein, E., Chkoniya, V., Serova, E., & Vorobyev, P. (2023). Sustainable social systems: Innovative service implications in the restaurant business in the post-COVID era with digital transformation strategies. Sustainability, 15(19), Article 14539. https://doi.org/10.3390/su151914539
Ferrari, M. V. (2022). The platformisation of digital payments: The fabrication of consumer interest in the EU FinTech agenda. Computer Law & Security Review, 45, Article 105687. https://doi.org/10.1016/j.clsr.2022.105687
Jabeen, F., Al Zaidi, S., & Al Dhaheri, M. H. (2022). Automation and artificial intelligence in hospitality and tourism. Tourism Review, 77(4), 1043–1061. https://doi.org/10.1108/TR-09-2019-0360
Kaushal, V., & Yadav, R. (2023). Learning successful implementation of Chatbots in businesses from B2B customer experience perspective. Concurrency and Computation: Practice and Experience, 35(1), Article e7450. https://doi.org/10.1002/cpe.7450
Kim, J. K., Yang, J. J., & Lee, Y. K. (2023). How do self-service kiosks improve COVID-19 pandemic resilience in the restaurant industry?. Sustainability, 15(13), Article 10168. https://doi.org/10.3390/su151310168
Klouvidaki, M. I., Antonopoulos, N., Styliaras, G. D., & Kanavos, A. (2023). Virtual restaurants: Customer experience keeps their businesses alive. Information, 14(7), Article 406. https://doi.org/10.3390/info14070406
Kruesi, M. A., & Bazelmans, L. (2023). Resources, capabilities and competencies: A review of empirical hospitality and tourism research founded on the resource-based view of the firm. Journal of Hospitality and Tourism Insights, 6(2), 549–574. https://doi.org/10.1108/JHTI-10-2021-0270
Leung, R., & Loo, P. T. (2022). Co-creating interactive dining experiences via interconnected and interoperable smart technology. Asian Journal of Technology Innovation, 30(1), 45–67. https://doi.org/10.1080/19761597.2020.1822748
Lockett, A., & Thompson, S. (2001). The resource-based view and economics. Journal of management, 27(6), 723–754. https://doi.org/10.1016/S0149-2063(01)00121-0
Madhovi, P. G., & Dhliwayo, S. (2017). The relationship between customer relationship management (CRM) and performance in the hotel industry. African Journal of Hospitality, Tourism and Leisure, 6(1), 1–13. https://www.ajhtl.com/uploads/7/1/6/3/7163688/article_31_vol_6__1__2017.pdf
Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14, 81–95. https://doi.org/10.1007/s10209-014-0348-1
Martín-Martín, D., García, J. M., & Romero, I. (2022). Determinants of digital transformation in the restaurant industry. Amfiteatru Economic, 24(60), 430–446. https://www.ceeol.com/search/article-detail?id=1038599
Morgan, G. A., Leech, N. L., Gloeckner, G. W., & Barrett, K. C. (2004). SPSS for introductory statistics: Use and interpretation (2nd ed.). New York: Psychology Press. https://doi.org/10.4324/9781410610539
Munshi, A. (2019). Assessment of competitiveness of food-tech start-ups in India. Indian Journal of Public Administration, 65(1), 201–224. https://doi.org/10.1177/0019556118821268
Nam, K., Dutt, C. S., Chathoth, P., Daghfous, A., & Khan, M. S. (2021). The adoption of artificial intelligence and robotics in the hotel industry: Prospects and challenges. Electronic Markets, 31, 553–574. https://doi.org/10.1007/s12525-020-00442-3
National Restaurant Association. (2023, March 2). 2023 state of the restaurant industry. https://restaurant.org/events-and-community/events-calendar/2023-state-of-the-restaurant-industry/
Ndhlovu, E., & Dube, K. (2023). Challenges of radical technological transition in the restaurant industry within developing countries. African Journal of Hospitality, Tourism and Leisure, 12(1), 156–170. https://doi.org/10.46222/ajhtl.19770720.360
Pansari, A., & Kumar, V. (2017). Customer engagement: The construct, antecedents, and consequences. Journal of the Academy of Marketing Science, 45, 294–311. https://doi.org/10.1007/s11747-016-0485-6
Pereira, D., Bozzato, A., Dario, P., & Ciuti, G. (2022). Towards Foodservice Robotics: A taxonomy of actions of foodservice workers and a critical review of supportive technology. IEEE Transactions on Automation Science and Engineering, 19(3), 1820–1858. https://doi.org/10.1109/TASE.2021.3129077
Popkin, B. M., & Reardon, T. (2018). Obesity and the food system transformation in Latin America. Obesity Reviews, 19(8), 1028–1064. https://doi.org/10.1111/obr.12694
Reis, J., Melão, N., Salvadorinho, J., Soares, B., & Rosete, A. (2020). Service robots in the hospitality industry: The case of Henn-na hotel, Japan. Technology in Society, 63, Article 101423. https://doi.org/10.1016/j.techsoc.2020.101423
Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Information & Management, 42(2), 317–327. https://doi.org/10.1016/j.im.2003.12.013
Seo, K. H., & Lee, J. H. (2021). The emergence of service robots at restaurants: Integrating trust, perceived risk, and satisfaction. Sustainability, 13(8), Article 4431. https://doi.org/10.3390/su13084431
Steinhoff, L., Liu, J., Li, X., & Palmatier, R. W. (2023). Customer engagement in international markets. Journal of International Marketing, 31(1), 1–31. https://doi.org/10.1177/1069031X221099211
Swink, M., Hu, K., & Zhao, X. (2022). Analytics applications, limitations, and opportunities in restaurant supply chains. Production and Operations Management, 31(10), 3710–3726. https://doi.org/10.1111/poms.13704
Viardot, E., Brem, A., & Nylund, P. A. (2023). Post-pandemic implications for crisis innovation: A technological innovation view. Technological Forecasting and Social Change, 194, Article 122680. https://doi.org/10.1016/j.techfore.2023.122680
Yang, L., Henthorne, T. L., & George, B. (2020). Artificial intelligence and robotics technology in the hospitality industry: Current applications and future trends. In B. George & J. Paul (Eds.), Digital transformation in business and society: Theory and cases (pp. 211–228). Cham: Palgrave Macmillan. https://doi.org/10.1007/978-3-030-08277-2_13
Zhang, X., & Jin, H. (2023). How does smart technology, artificial intelligence, automation, robotics, and algorithms (STAARA) awareness affect hotel employees’ career perceptions? A disruptive innovation theory perspective. Journal of Hospitality Marketing & Management, 32(2), 264–283. https://doi.org/10.1080/19368623.2023.2166186
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