The Eastern Communication Association (ECA), the largest regional communication association on the East Coast, hosted its 113th annual convention in Philadelphia, PA in April with “Reunion” as the convention theme. It was my honor to present my award paper, Exploring how perceived risk and demographic factors affect consumer behaviors amid the COVID-19 pandemic with predictive analytics, at “Top Papers in the Applied Communication” panel.
By analyzing IPSOS’ panel data (N = 1,033) with predictive analytics (e.g., cluster analysis, decision tree analysis), I explored how psychological (e.g., perceived risk) and demographic factors affect consumers’ choice of shopping outlets during the business reopening period in 2020. By doing so, data-driven sights about consumer behaviors during crises can be provided.
Study Background and Research Findings
Due to public health concerns, a majority of businesses were shut down and stores were closed from mid-March to the end of May in 2020 in the U.S. Non-grocery retail stores were closed during the three-month business shutdown period. Consumers needed to shop online for non-grocery items and could not eat inside the restaurants. Only take-out services were allowed for all restaurants. Until the end of May, stores were not allowed to reopen with limited capacities. Therefore, consumer behaviors were greatly disrupted in the early stage of the pandemic. Even now, some pandemic-induced consumption trends (e.g., explosion of e-commerce, online grocery shopping) that emerged earlier still persist. In order to predict the new normal, researchers and marketers would like to know the factors that may predict consumers’ choice of consumption channels/outlets.
Based on protection motivation theory (PMT) (Rogers, 1975), consumer perceived risk theory (Mitchell, 1999), and consumer demographic theory (Martins & Brooks, 2010), I proposed four research questions in my study. RQ1 asked that how can U.S. consumers be categorized into different clusters, based on their perceived risk for consumption activities and concern about the pandemic by using cluster analysis. I also asked a follow-up question to examine how are cluster memberships associated with consumer demographics. RQ2, RQ3, and RQ4 asked what are the most important variables that predict U.S. consumers’ visiting a non-grocery store behavior, ordering take-out or delivery from restaurants behavior by using decision tree analysis.
The results are quiet sticking and underscore why it is important to use predictive analytics to analyze consumer behaviors and provide evidence-based research findings. First, the results of cluster analysis suggested that the U.S. consumers can be categorized into two clusters, high perceived risk and high concern cluster and low perceived risk and low concern cluster. Cluster membership is associated with gender, ethnicity, and household income. Second, the results of decision tree analysis showed that perceived risk for food delivery and take out is the most important factor that predicts consumers’ ordering food delivery and takeout behaviors. Third, the decision tree analysis results suggested that perceived risk for instore consumption activities is the most important predictor for predicting consumers’ in-store consumption activities, such as visiting a non-grocery retail store and going out to eat. Obviously, the results of this study support consumer demographics theory because demographic factors are associated with cluster membership based on concern and perceived risk. This study also supports consumer perceived risk theory, because consumers’ perceived risks served as the most important predictor for consumers’ choice of consumption channels/outlets.
Practical Suggestions for Organizations
My study has answered a very important question: What do consumers need during crises? The answer is that consumers need a low-risk shopping environment. In order to provide customers a safe consumption environment, restaurants and retail stores are suggested to minimize the risk factors by adopting security measures (e.g., limited in-store capacities, wearing a mask, cleaning, contactless payment). When consumers have choices, they would choose the consumption outlets with lower risks. Another practical implication of this study is that there are different consumer segments in the U.S., based on concern for the pandemic and perceived risks. Thus, organizations/marketers may customize their communication strategies and channels (e.g., social media platforms) for the consumer segment with high concern, high perceived risk scores by emphasizing the security measures that have been implemented in order to reduce target audience’s perceived risks. If organizations (e.g., retailers, restaurants) would like to use social media as communication channels, they may take user demographics into account. For example, more females than males are Instagram and Pinterest users. Thus, retailers/restaurants may use these two platforms to communicate with female consumers and tell them about how they implement security measures in their stores/restaurants.
In addition to present my own paper, I attended many panels to enhance my professional knowledge and to identify teaching trends in higher education and research trends in the communication field.
Trends in Higher Education
The keynote speaker, Kami Silk, talked about how universities and communication departments respond to the COVID-19 pandemic. As she mentioned, we were not prepared for the pandemic. However, we are agile and flexible. We adapt and learn. Then, we survive.
Indeed, the pandemic has created both challenges and opportunities for higher education. For example, universities re-opened the campus and began to offer classes in person again since 2021. However, many students already got used to online learning. They would ask for more online courses. Even when some classes were taught in the classroom, students would ask for a Zoom link to log in online. Thus, universities try to be flexible and give students the online option. In addition, some universities have difficulties recruiting students. Thus, they may modify their curriculum to respond to the societal and industrial trends. Indeed, it is critical to enhance students’ communication and digital skills as we moving forward to the post-pandemic world. For example, our graduate program offers a variety of communication and digitalization courses, such as ethical issues in organizational communication, organizational communication, crisis communication, intercultural communication, consumer behaviors in the online environment, digital era skills, digital marketing analysis, and leveraging digital technologies. Our curriculum design is definitely practical and trending.
There are many COVID-19 related papers and panels at this conference. Communication professors/researchers attempt to analyze what happened/emerged amid the pandemic and figure out how communication professors and practitioners can contribute to the post-pandemic world.
Computer mediated communication (CMC) scholars research about how people use various communication technologies, such as social media and video conferencing tools like Zoom, to communicate with each other. Applied communication scholars want to know the practical implications of communication studies. My paper about consumer behaviors is one of the examples. Some other colleagues research about the effects of communication campaigns. For example, how health communication campaigns can be used to reduce vaccine hesitancy. Health communication scholars mainly conduct COVID-19 related studies, such as masking behaviors, social mentions about vaccine, and COVID-19 vaccine passports. Organizational communication scholars research about critical issues, such as work life balance, diversity, organizational bully, sexual harassment, burnout, and turnover. Indeed, it’s very important for organizations to respect employees’ work life balance to prevent burnout and high turnover rate. As for social media research, scholars research about dis-information, mis-information, AI-driven algorithm, and ethical issues. AI algorithm is used in many digital tools, such as Microsoft Office, Google search engine, and social media. On the positive side, the algorithms can provide tech users and consumers with relevant information and recommendations. However, ethical guidelines for organizational use of AI need to be developed and implemented. Otherwise, there are privacy, security, and other ethical issues (e.g., fairness of algorithms) associated with it.
It’s my great pleasure sharing thoughts, ideas, and research findings with you!
Posted by Ming-Yi Wu, Faculty
Axios/Ipsos. (2020). Axios/Ipsos Coronavirus Index Wave 11 (Version 3) [Dataset]. Cornell University, Ithaca, NY: Roper Center for Public Opinion Research. doi:10.25940/ROPER-31117428
Martins, J. M., & Brooks, G. (2010). Teaching consumer demographics to marketing students, Popular Research Policy Review, 29, 81-92. https://doi.org/10.1007/s11113-009-9146-5
Mitchell, V-W. (1999). Consumer perceived risk: Conceptualizations and models. European model of marketing, 33(1/2), 163-195.
Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. Journal of Psychology, 91(1), 93-114. https://doi.org/10.1080/002239890.1975.9915803