Will the COVID-19 Pandemic Lead to Long-Term Consumer Perceptions, Behavioral Intentions, and Acquisition Decisions?

• Author: Watson, Robert

1. Introduction

   The COVID-19 outbreak has limited consumers' freedom of options, has reduced perceived confidence for physical shopping alternatives, and has developed their psychological reactance. (Akhtar et al., 2020) The set of actions implemented by both department stores and governments to adhere to public regulations (Lambovska et al., 2021; Mihaila et al., 2016; Nemteanu et al., 2021) may result in increased embracing of biometric surveillance procedures, such as GPS tracking, body scanning, and face recognition that can modify privacy perceptions over time. (Pantano et al., 2020)

2. Conceptual Framework and Literature Review

   Consumers have cut back in disagreement with relative price escalations during the COVID-19 pandemic. (Cranfield, 2020) Stockpiling essential items configures consumer sentiment and attitudes in relation to panic buying. (Prentice et al., 2020a) The COVID-19 crisis has resulted in significant degrees of switching behaviors among individuals (Barnes and Zvarikova, 2021; Lazaroiu et al., 2017; Mitchell et al., 2021; Nemteanu and Dabija, 2021; Wallin and Sandlin, 2020), with farmers' markets failing to retain almost all of their customers. Local small independent retailers have confronted growing levels of resilience as regards customer retention, configuring modifications in consumer behavior associated with food purchases, including hoarding or buying immoderate volumes of staples. (Li et al., 2020) During interactions with the loved ones and customers, feelings of apprehension and anxiety may become prevalent (Riley and Nica, 2021), leading to panic and stockpiling. (Prentice et al., 2020b) In conjunction with the consequences of demand-side shocks and possible supply-side disorganizations, the COVID-19 outbreak may have prolonged effects on the constitution of food supply chains, resulting in the development of the online grocery delivery industry and consumers' chief options for local food supply chains. (Hobbs et al., 2020)

3. Methodology and Empirical Analysis

   Using and replicating data from Accenture, KPMG, and McKinsey, we performed analyses and made estimates regarding how the COVID-19 pandemic has reshaped customer attitudes, behaviors, values and expectations, reconfiguring consumer traits, sentiments, trust, and engagement, and thus leading to altered purchasing decisions and habits, and buying patterns in terms of psychological risk perception. The results of a study based on data collected from 9,200 respondents provide support for our research model. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

4. Study Design, Survey Methods, and Materials

   The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States.

   Data sources: Accenture, KPMG, and McKinsey.

   Study participants: 9,200 individuals provided an informed e-consent.

   Non-response bias and common method bias, composite reliability, and construct validity were assessed. Multivariate analyses, and not univariate associations with outcomes, are more likely to factor out confounding covariates and more precisely determine the relative significance of individual variables. Results are estimates and commonly are dissimilar within a narrow range around the actual value. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process.

   Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing (e.g., checking for high rates of leaving questions blank). Sampling errors and test of statistical significance take into account the effect of weighting. Question wording and practical difficulties in conducting surveys can introduce error or bias into the findings of opinion polls. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. Stratified sampling methods were used and weights were trimmed not to exceed 3. Average margins of error, at the 95% confidence level, are +/-2%. The design effect for the survey was 1.3. For tabulation purposes, percentage points are rounded to the nearest whole number. The cumulative response rate accounting for non-response to the recruitment surveys and attrition is 2.5%. The break-off rate among individuals who logged onto the survey and completed at least one item is 0.2%.

   The precision of the online polls was measured using a Bayesian credibility interval. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Addressing a significant knowledge gap in the literature, the research has complied with stringent methodology, reporting, and data analysis requirements.

   Flow diagram of study procedures

5. Statistical Analysis

   This survey employs statistical weighting procedures to clarify deviations in the survey sample from known population features, which is instrumental in correcting for differential survey participation and random variation in samples. Independent t-tests for continuous variables or chi-square tests for categorical variables were employed. Descriptive analyses (mean and standard deviations for continuous variables and counts and percentages for categorical variables) were used. Descriptive statistical analysis and multivariate inferential tests were undertaken for the survey responses and for the purpose of variable reduction in regression modeling.

   Mean and standard deviation, t-test, exploratory factor analysis, and data normality were inspected using SPSS. To ensure reliability and accuracy of data, participants undergo a rigorous verification process and incoming data goes through a sequence of steps and multiple quality checks. Descriptive and inferential statistics provide a summary of the responses and comparisons among subgroups. AMOS-SEM analyzed the full measurement model and structural model.

   All data were interrogated by employing graphical and numeric exploratory data analysis methods. An Internet-based survey software program was utilized for the delivery and collection of responses. Panel research represents a swift method for gathering data recurrently, drawing a sample from a prerecruited set of respondents. Behavioral datasets have been collected, entered into a spreadsheet, and cutting-edge computational techniques and empirical strategies have been harnessed for analysis. Groundbreaking computing systems and databases enable data gathering and processing, extracting meaning through robust deployment.

   Flow diagram of statistical parameters and reproducibility

6. Results and Discussion

   The occurrence of constraints, negative cognition, and threats decrease consumers' confidence in physical shopping options during the COVID-19 crisis. (Akhtar et al., 2020) By stocking up and hoarding essential products, consumers generally feel more confident as regards their...