The impact of the COVID-19 pandemic on retailer performance: empirical evidence from India
-
Author(s)Amgad S.D. KhaledLink to ORCID Index: https://orcid.org/0000-0003-0040-3849
,
Nabil Mohamed AlabsyLink to ORCID Index: https://orcid.org/0000-0002-9443-1738
,
Eissa A. Al-HomaidiLink to ORCID Index: https://orcid.org/0000-0002-6230-8276
,
Abdulmalek M.M. SaeedLink to ORCID Index: https://orcid.org/0000-0002-7941-5780
-
DOIhttp://dx.doi.org/10.21511/im.16(4).2020.11
-
Article InfoVolume 16 2020, Issue #4, pp. 129-138
- Cited by
- 3462 Views
-
5876 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
The study aims to synthesize the challenges that retailers are facing during the COVID-19 emergency. The research is definitive, informative, and based on a single design of cross-sectional research. Quantitative data based on the research instrument were produced (a questionnaire). Five hundred responses were collected from employees of major retail stores in India. Retailer performance is considered a dependent variable, whereas employee well-being, customer and brand protection, use of technology, government policies, and supply chain are used as independent variables. The current study results indicated that employee well-being and government policies have a significant positive impact on retailer performance, while customer and brand protection, use of technology, and supply chain have a significant positive impact on retailers’ performance. This study will help retailers develop strategies for their employees to protect them and understand that technology is needed in the new normal times. This study highlights the need to be flexible in executing strategic strategies, but retailers need to develop comprehensive action plans, including selecting managers of initiative and defining goals and deadlines. Provided that retailers’ current reality is different from the old normal, no time is lost in taking audacious action.
- Keywords
-
JEL Classification (Paper profile tab)M31, M39
-
References45
-
Tables7
-
Figures1
-
- Figure 1. Regression model
-
- Table 1. Demographics of respondents
- Table 2. KMO and Bartlett’s test
- Table 3. Total variance explained
- Table 4. Exploratory Factor Analysis
- Table 5. Cronbach’s alpha
- Table 6. CFA results
- Table 7. Regression analysis
-
- Blankson, C., & Kalafatis, S. P. (2004). The development and validation of a scale measuring consumer/customer-derived generic typology of positioning strategies. Journal of Marketing Management, 20(1-2), 5-43.
- Business Today. (2020). Сoronavirus impact: jobs take a hit; Indian retailers see revenue slide.
- Carpenter, D., Maasberg, M., Hicks, C., & Chen, X. (2016). A multicultural study of bio- metric privacy concerns in a fire ground accountability crisis response system. International Journal of Information Management, 36(5), 735-747.
- Deng, S., Wang, W., & Chao, Y. (2020). Will consumption rebound after the COVID-19 pandemic? 1 A structural equation modeling research based on the psychological arousal theory.
- Dennis, C., Alamanos, E., Papagiannidis, S., & Bourlakis, M. (2015). Does social exclusion influence multiple channel use? The interconnections with community, happiness and wellbeing. Journal of Business Research, 69(3), 1061-1070.
- Dolnicar, S., & Zare, S. (2020). Coronavirus and Airbnb – disrupting the disruptor.
- Evans, O. (2020). Socio-economic impacts of novel coronavirus: The policy solutions. BizEcons Quarterly, 7, 3-12.
- Fadel, M., Salomon, J., & Descatha, A. (2020). Coronavirus outbreak: The role of companies in preparedness and responses. The Lancet. Public Health, 20, 30051-30057.
- Fashion Network. (2020). India’s retail sector takes a $30 billion hit due to COVID-19.
- Fernandes, N. (2020). Economic Effects of Coronavirus Outbreak (COVID-19) on the World Economy.
- Ferry, Y., & Leech, D. (2005). Amperometric detection of catecholamine neurotransmitters using electrocatalytic substrate recycling at a laccase electrode. Electroanalysis: An International Journal Devoted to Fundamental and Practical Aspects of Electroanalysis, 17(2), 113-119.
- Field, A. (2009). Discovering statistics using SPSS: (and sex and drugs and rock’n’roll). Sage.
- Fielding, N. G., Lee, R. M., & Blank, G. (Eds.) (2008). The SAGE handbook of online research methods. Sage.
- Gordon, A. V., Ramic, M., Rohrbeck, R., & Spaniol, M. J. (2020). 50 Years of corporate and organizational foresight: Looking back and going forward. Technological Forecasting and Social Change, 154, 119966.
- Gummesson, E. (2009). The global crisis and the marketing scholar. Journal of Customer Behaviour, 8(2), 119-135.
- Hair, J., Black, B., Babin, B., & Anderson, R. (2010). Multivariate data analysis. Upper Saddle River, NJ: Pearson Prentice Hall.
- Hamilton, R., Thompson, D., Bone, S., Chaplin, L. N., Griskevicius, V., Goldsmith, K., et al. (2019). The effects of scarcity on consumer decision journeys. Journal of the Academy of Marketing Science, 47(3), 532-550.
- Harris, P., Dallolmo Riley, F., Riley, D., & Hand, C. (2017). Online and store patronage: A typology of grocery shoppers. International Journal of Retail & Distribution Management, 45(4), 419-445.
- Hogan, T. P., Benjamin, A., & Brezinski, K. L. (2000). Reliability methods: A note on the frequency of use of various types. Educational and psychological measurement, 60(4), 523-531.
- Holmes, E. A., O’Connor, R. C., Perry, V. H., Tracey, I., Wessely, S., Arseneault, L., ... & Ford, T. (2020). Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science. The Lancet Psychiatry.
- Huang, A., Dawes, J., Lockshin, L., & Greenacre, L. (2017). Consumer response to price changes in higher-priced brands. Journal of Retailing and Consumer Services, 39, 1-10.
- Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, 136.
- Janssen, C., Vanhamme, J., & Leblanc, S. (2017). Should luxury brands say it out loud? Brand conspicuousness and consumer perceptions of responsible luxury. Journal of Business Research, 77, 167-174.
- Kumar, P. (2005). The competitive impact of service process improvement: Examining customers’ waitingfor experiences in retail markets. Journal of Retailing, 81(3), 171-180.
- Makridis, C., & Hartley, J. (2020). An Economic Analysis of the COVID-19 Effects on County Real GDP.
- Meyer-Emerick, N. (2015). Using social marketing for public emergency preparedness: Social change for community resilience.
- Miltgen, C. L., Henseler, J., Gelhard, C., & Popovič, A. (2016). Introducing new products that affect consumer privacy: A mediation model. Journal of Business Research, 69(10), 4659-4666.
- Mowen, J. C., Licata, J. W., & McPhail, J. (1993). Waiting in the emergency room: how to improve patient satisfaction. Journal of Health Care Marketing, 13(2), 26-33.
- Papagiannidis, S., Boulakis, M., Alamanos, E., & Dennis, C. (2017). Preferences of smart shopping channels and their impact on perceived wellbeing and social inclusion. Computers in Human Behavior, 77, 396-405.
- Parker, G., Pickard, J., Cookson, C., & Pickford, J. (2020). UK to launch a business rescue package to fight coronavirus fallout. Financial Times Online.
- Pizzi, G., Scarpi, D., Pichierri, M., & Vannucci, V. (2019). Virtual reality, real reactions? Comparing consumers’ perceptions and shopping orientation across physical and virtual-reality retail stores. Computers in Human Behavior, 96, 1-12.
- Reise, S. P., Waller, N. G., & Comrey, A. L. (2000). Factor analysis and scale revision. Psychological assessment, 12(3), 287.
- Rittichainuwata, B. N., & Chakraborty, G. (2012). Perceptions of importance and what safety is enough. Journal of Business Research, 65(1), 42-50.
- Schenker-Wicki, A., Ianunen, M., & Olivares, M. (2010). Unmastered risks: From crisis to catastrophe: An economic and management insight. Journal of Business Research, 63(4), 337-346.
- Schriber, S., & Lowstedt, J. (2019). Reconsidering ordinary and dynamic capabilities in strategic change. European Management Journal, 38(3), 377-387.
- Shah, A. K., Shafir, E., & Mullainathan, S. (2015). Scarcity frames value. Psychological Science, 26(4), 402-412.
- Sjodin, D., Parida, V., Kohtamaki, M., & Wincent, J. (2020). An agile co-creation process for digital servitization: A micro-service innovation approach. Journal of Business Research, 112, 478-491.
- Somason, A., & Voyer, B. G. (2014). Emergency purchasing situations: Implications for consumer decision-making. Journal of Economic Psychology, 21-33.
- Tarki, A., Levy, P., & Weiss, J. (2020). The Coronavirus Crisis Doesn’t Have to Lead to Layoffs. Harvard Business Review Digital.
- Tobias, S., & Carlson, J. E. (1969). Brief report: Bartlett’s test of sphericity and chance findings in factor analysis. Multivariate behavioral research, 4(3), 375-377.
- Tsai, S. P. (2006). Investigating archetype-icon transformation in brand marketing. Marketing Intelligence & Planning, 24(6), 648-663.
- Vannucci, V., & Pantano, E. (2019). Digital or human touchpoints? Insights from consumer-facing in-store services. Information Technology and People, 33(1), 296-310.
- Verhallen, T. M., & Robben, H. S. (1994). Scarcity and preference: An experiment on unavailability and product evaluation. Journal of Economic Psychology, 15(2), 315-331.
- Wakefield, K. L., & Inman, J. J. (2003). Situational price sensitivity: The role of consumption occasion, social context and income. Journal of Retailing, 79(4), 199-212.
- Zhang, W., Gu, H., & Kavanaugh, R. R. (2005). The impacts of SARS on the consumer behavior of Chinese domestic tourists. Current Issues in Tourism, 8(1), 22-38.
-
The impact of the COVID-19 outbreak on the Indian stock market – A sectoral analysis
Rahul Kumar ,
Prince Bhatia ,
Deeksha Gupta
doi: http://dx.doi.org/10.21511/imfi.18(3).2021.28
Investment Management and Financial Innovations Volume 18, 2021 Issue #3 pp. 334-346 Views: 7363 Downloads: 3487 TO CITE АНОТАЦІЯThis paper aims to examine the impact of the COVID-19 outbreak on Indian firms listed on the NSE and analyze its impact on various sectors. In addition, a sub-sample analysis based on market capitalization was performed to understand the effect of size during extreme events. The sample consisted of 1,335 firms listed on the NSE India. A standard event study outlined by Brown and Warner (1985) was employed to analyze the price impact on the COVID-19 outbreak. The event windows from -10 days to +10 days were selected. The estimation window is 250 days. The Nifty 50 has been chosen as a proxy for market return. The sample firms witnessed a negative impact of the COVID-19 outbreak with a negative CAAR in different event windows. In addition, various sectors are classified according their responsiveness towards the COVID-19 outbreak into three groups: highly negatively affected, moderately negatively affected, and slightly negatively affected. The paper also points out that the pandemic substantially affects the above-median market capitalized firms than the below-median market capitalized firms, which contradicts the size effect phenomenon. The results assist shareholders in managing their portfolios and mitigate the systematic risk of their investments during extreme events such as a pandemic, wars, and others. This study is the first comprehensive analysis of the impact of the COVID-19 outbreak on different sectors in India. It is also the first study to investigate the size effect anomalies during extreme events.
-
The impact of social distancing policy on small and medium-sized enterprises (SMEs) in Indonesia
Muhtar Lutfi ,
Pricylia Chintya Dewi Buntuang ,
Yoberth Kornelius ,
Erdiyansyah ,
Bakri Hasanuddin
doi: http://dx.doi.org/10.21511/ppm.18(3).2020.40
Problems and Perspectives in Management Volume 18, 2020 Issue #3 pp. 492-503 Views: 3787 Downloads: 1360 TO CITE АНОТАЦІЯThis study aims to investigate the impact of social distancing policies on SMEs in Indonesia. It used a quantitative method with a survey design. Respondents were all SMEs in Indonesia that are affected by social distancing policies during the COVID-19 pandemic. It involved a total of 587 SME samples selected randomly. The data were collected through observations, questionnaires, and literature studies. The collected data were analyzed using descriptive statistics with SPSS software to determine the mean value. The result showed that social distancing policies affect SMEs during the COVID-19 pandemic. This is indicated by the decreasing income and demand for SMEs products, and even some have no income (mean values of 2.40) due to the social distancing policies. Besides, the policy’s impact is also shown in the increasing cost of raw materials and production costs due to supply chain problems (mean values of 4.79). The policy’s impact raises anxiety for SMEs to survive so that business actors change their plans by utilizing information technology (mean values of 4.81). This change is a strategy to survive due to the impact of the applied policies. Although social distancing policies affect SMEs’ survival during the pandemic, research findings show that SMEs in Indonesia did not terminate employment (mean values of 4.37) due to the presence of economic stimulus policies that helped SMEs survive and grow during the COVID-19 pandemic.
-
The impact of quarantine due to COVID-19 pandemic on the tourism industry in Lviv (Ukraine)
Mykhailo Rutynskyi ,
Halyna Kushniruk
doi: http://dx.doi.org/10.21511/ppm.18(2).2020.17
Problems and Perspectives in Management Volume 18, 2020 Issue #2 pp. 194-205 Views: 3030 Downloads: 1206 TO CITE АНОТАЦІЯThe global crisis caused by COVID-19 pandemic and the introduction of long-term international quarantine measures have had a very negative impact on the tourism industry. The paper aims to analyze the sectoral losses of the tourism industry during quarantine due to the COVID-19 pandemic in Lviv, a city whose historical center is included in the UNESCO World Heritage Site. The article highlights the potential of the tourism industry before the introduction of quarantine measures, as well as using extrapolation methods estimates the economic losses of the tourist industry of Lviv in the conditions of long-term quarantine. Besides, the anti-crisis measures of the city authorities to minimize the impact of quarantine due to COVID-19 pandemic on the tourism industry of Lviv are described. Based on a statistical analysis of the forecasted economic losses, it is recommended that city authorities abolish some local taxes and fees in the tourism sector to restore its potential as soon as possible.
