research paper about consumer behavior

The past, present, and future of consumer research

• Published: 13 June 2020
• Volume 31 , pages 137–149, ( 2020 )

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• Maayan S. Malter   ORCID: orcid.org/0000-0003-0383-7925 1 ,
• Morris B. Holbrook 1 ,
• Barbara E. Kahn 2 ,
• Jeffrey R. Parker 3 &
• Donald R. Lehmann 1  

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In this article, we document the evolution of research trends (concepts, methods, and aims) within the field of consumer behavior, from the time of its early development to the present day, as a multidisciplinary area of research within marketing. We describe current changes in retailing and real-world consumption and offer suggestions on how to use observations of consumption phenomena to generate new and interesting consumer behavior research questions. Consumption continues to change with technological advancements and shifts in consumers’ values and goals. We cannot know the exact shape of things to come, but we polled a sample of leading scholars and summarize their predictions on where the field may be headed in the next twenty years.

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1 Introduction

Beginning in the late 1950s, business schools shifted from descriptive and practitioner-focused studies to more theoretically driven and academically rigorous research (Dahl et al. 1959 ). As the field expanded from an applied form of economics to embrace theories and methodologies from psychology, sociology, anthropology, and statistics, there was an increased emphasis on understanding the thoughts, desires, and experiences of individual consumers. For academic marketing, this meant that research not only focused on the decisions and strategies of marketing managers but also on the decisions and thought processes on the other side of the market—customers.

Since then, the academic study of consumer behavior has evolved and incorporated concepts and methods, not only from marketing at large but also from related social science disciplines, and from the ever-changing landscape of real-world consumption behavior. Its position as an area of study within a larger discipline that comprises researchers from diverse theoretical backgrounds and methodological training has stirred debates over its identity. One article describes consumer behavior as a multidisciplinary subdiscipline of marketing “characterized by the study of people operating in a consumer role involving acquisition, consumption, and disposition of marketplace products, services, and experiences” (MacInnis and Folkes 2009 , p. 900).

This article reviews the evolution of the field of consumer behavior over the past half century, describes its current status, and predicts how it may evolve over the next twenty years. Our review is by no means a comprehensive history of the field (see Schumann et al. 2008 ; Rapp and Hill 2015 ; Wang et al. 2015 ; Wilkie and Moore 2003 , to name a few) but rather focuses on a few key thematic developments. Though we observe many major shifts during this period, certain questions and debates have persisted: Does consumer behavior research need to be relevant to marketing managers or is there intrinsic value from studying the consumer as a project pursued for its own sake? What counts as consumption: only consumption from traditional marketplace transactions or also consumption in a broader sense of non-marketplace interactions? Which are the most appropriate theoretical traditions and methodological tools for addressing questions in consumer behavior research?

2 A brief history of consumer research over the past sixty years—1960 to 2020

In 1969, the Association for Consumer Research was founded and a yearly conference to share marketing research specifically from the consumer’s perspective was instituted. This event marked the culmination of the growing interest in the topic by formalizing it as an area of research within marketing (consumer psychology had become a formalized branch of psychology within the APA in 1960). So, what was consumer behavior before 1969? Scanning current consumer-behavior doctoral seminar syllabi reveals few works predating 1969, with most of those coming from psychology and economics, namely Herbert Simon’s A Behavioral Model of Rational Choice (1955), Abraham Maslow’s A Theory of Human Motivation (1943), and Ernest Dichter’s Handbook of Consumer Motivations (1964). In short, research that illuminated and informed our understanding of consumer behavior prior to 1969 rarely focused on marketing-specific topics, much less consumers or consumption (Dichter’s handbook being a notable exception). Yet, these works were crucial to the rise of consumer behavior research because, in the decades after 1969, there was a shift within academic marketing to thinking about research from a behavioral or decision science perspective (Wilkie and Moore 2003 ). The following section details some ways in which this shift occurred. We draw on a framework proposed by the philosopher Larry Laudan ( 1986 ), who distinguished among three inter-related aspects of scientific inquiry—namely, concepts (the relevant ideas, theories, hypotheses, and constructs); methods (the techniques employed to test and validate these concepts); and aims (the purposes or goals that motivate the investigation).

2.1 Key concepts in the late - 1960s

During the late-1960s, we tended to view the buyer as a computer-like machine for processing information according to various formal rules that embody economic rationality to form a preference for one or another option in order to arrive at a purchase decision. This view tended to manifest itself in a couple of conspicuous ways. The first was a model of buyer behavior introduced by John Howard in 1963 in the second edition of his marketing textbook and quickly adopted by virtually every theorist working in our field—including, Howard and Sheth (of course), Engel-Kollat-&-Blackwell, Franco Nicosia, Alan Andreasen, Jim Bettman, and Joel Cohen. Howard’s great innovation—which he based on a scheme that he had found in the work of Plato (namely, the linkages among Cognition, Affect, and Conation)—took the form of a boxes-and-arrows formulation heavily influenced by the approach to organizational behavior theory that Howard (University of Pittsburgh) had picked up from Herbert Simon (Carnegie Melon University). The model represented a chain of events

where I = inputs of information (from advertising, word-of-mouth, brand features, etc.); C = cognitions (beliefs or perceptions about a brand); A = Affect (liking or preference for the brand); B = behavior (purchase of the brand); and S = satisfaction (post-purchase evaluation of the brand that feeds back onto earlier stages of the sequence, according to a learning model in which reinforced behavior tends to be repeated). This formulation lay at the heart of Howard’s work, which he updated, elaborated on, and streamlined over the remainder of his career. Importantly, it informed virtually every buyer-behavior model that blossomed forth during the last half of the twentieth century.

To represent the link between cognitions and affect, buyer-behavior researchers used various forms of the multi-attribute attitude model (MAAM), originally proposed by psychologists such as Fishbein and Rosenberg as part of what Fishbein and Ajzen ( 1975 ) called the theory of reasoned action. Under MAAM, cognitions (beliefs about brand attributes) are weighted by their importance and summed to create an explanation or prediction of affect (liking for a brand or preference for one brand versus another), which in turn determines behavior (choice of a brand or intention to purchase a brand). This took the work of economist Kelvin Lancaster (with whom Howard interacted), which assumed attitude was based on objective attributes, and extended it to include subjective ones (Lancaster 1966 ; Ratchford 1975 ). Overall, the set of concepts that prevailed in the late-1960s assumed the buyer exhibited economic rationality and acted as a computer-like information-processing machine when making purchase decisions.

2.2 Favored methods in the late-1960s

The methods favored during the late-1960s tended to be almost exclusively neo-positivistic in nature. That is, buyer-behavior research adopted the kinds of methodological rigor that we associate with the physical sciences and the hypothetico-deductive approaches advocated by the neo-positivistic philosophers of science.

Thus, the accepted approaches tended to be either experimental or survey based. For example, numerous laboratory studies tested variations of the MAAM and focused on questions about how to measure beliefs, how to weight the beliefs, how to combine the weighted beliefs, and so forth (e.g., Beckwith and Lehmann 1973 ). Here again, these assumed a rational economic decision-maker who processed information something like a computer.

Seeking rigor, buyer-behavior studies tended to be quantitative in their analyses, employing multivariate statistics, structural equation models, multidimensional scaling, conjoint analysis, and other mathematically sophisticated techniques. For example, various attempts to test the ICABS formulation developed simultaneous (now called structural) equation models such as those deployed by Farley and Ring ( 1970 , 1974 ) to test the Howard and Sheth ( 1969 ) model and by Beckwith and Lehmann ( 1973 ) to measure halo effects.

2.3 Aims in the late-1960s

During this time period, buyer-behavior research was still considered a subdivision of marketing research, the purpose of which was to provide insights useful to marketing managers in making strategic decisions. Essentially, every paper concluded with a section on “Implications for Marketing Managers.” Authors who failed to conform to this expectation could generally count on having their work rejected by leading journals such as the Journal of Marketing Research ( JMR ) and the Journal of Marketing ( JM ).

2.4 Summary—the three R’s in the late-1960s

Starting in the late-1960s to the early-1980s, virtually every buyer-behavior researcher followed the traditional approach to concepts, methods, and aims, now encapsulated under what we might call the three R’s —namely, rationality , rigor , and relevance . However, as we transitioned into the 1980s and beyond, that changed as some (though by no means all) consumer researchers began to expand their approaches and to evolve different perspectives.

2.5 Concepts after 1980

In some circles, the traditional emphasis on the buyer’s rationality—that is, a view of the buyer as a rational-economic, decision-oriented, information-processing, computer-like machine for making choices—began to evolve in at least two primary ways.

First, behavioral economics (originally studied in marketing under the label Behavioral Decision Theory)—developed in psychology by Kahneman and Tversky, in economics by Thaler, and applied in marketing by a number of forward-thinking theorists (e.g., Eric Johnson, Jim Bettman, John Payne, Itamar Simonson, Jay Russo, Joel Huber, and more recently, Dan Ariely)—challenged the rationality of consumers as decision-makers. It was shown that numerous commonly used decision heuristics depart from rational choice and are exceptions to the traditional assumptions of economic rationality. This trend shed light on understanding consumer financial decision-making (Prelec and Loewenstein 1998 ; Gourville 1998 ; Lynch Jr 2011 ) and how to develop “nudges” to help consumers make better decisions for their personal finances (summarized in Johnson et al. 2012 ).

Second, the emerging experiential view (anticipated by Alderson, Levy, and others; developed by Holbrook and Hirschman, and embellished by Schmitt, Pine, and Gilmore, and countless followers) regarded consumers as flesh-and-blood human beings (rather than as information-processing computer-like machines), focused on hedonic aspects of consumption, and expanded the concepts embodied by ICABS (Table 1 ).

2.6 Methods after 1980

The two burgeoning areas of research—behavioral economics and experiential theories—differed in their methodological approaches. The former relied on controlled randomized experiments with a focus on decision strategies and behavioral outcomes. For example, experiments tested the process by which consumers evaluate options using information display boards and “Mouselab” matrices of aspects and attributes (Payne et al. 1988 ). This school of thought also focused on behavioral dependent measures, such as choice (Huber et al. 1982 ; Simonson 1989 ; Iyengar and Lepper 2000 ).

The latter was influenced by post-positivistic philosophers of science—such as Thomas Kuhn, Paul Feyerabend, and Richard Rorty—and approaches expanded to include various qualitative techniques (interpretive, ethnographic, humanistic, and even introspective methods) not previously prominent in the field of consumer research. These included:

Interpretive approaches —such as those drawing on semiotics and hermeneutics—in an effort to gain a richer understanding of the symbolic meanings involved in consumption experiences;

Ethnographic approaches — borrowed from cultural anthropology—such as those illustrated by the influential Consumer Behavior Odyssey (Belk et al. 1989 ) and its discoveries about phenomena related to sacred aspects of consumption or the deep meanings of collections and other possessions;

Humanistic approaches —such as those borrowed from cultural studies or from literary criticism and more recently gathered together under the general heading of consumer culture theory ( CCT );

Introspective or autoethnographic approaches —such as those associated with a method called subjective personal introspection ( SPI ) that various consumer researchers like Sidney Levy and Steve Gould have pursued to gain insights based on their own private lives.

These qualitative approaches tended not to appear in the more traditional journals such as the Journal of Marketing , Journal of Marketing Research , or Marketing Science . However, newer journals such as Consumption, Markets, & Culture and Marketing Theory began to publish papers that drew on the various interpretive, ethnographic, humanistic, or introspective methods.

2.7 Aims after 1980

In 1974, consumer research finally got its own journal with the launch of the Journal of Consumer Research ( JCR ). The early editors of JCR —especially Bob Ferber, Hal Kassarjian, and Jim Bettman—held a rather divergent attitude about the importance or even the desirability of managerial relevance as a key goal of consumer studies. Under their influence, some researchers began to believe that consumer behavior is a phenomenon worthy of study in its own right—purely for the purpose of understanding it better. The journal incorporated articles from an array of methodologies: quantitative (both secondary data analysis and experimental techniques) and qualitative. The “right” balance between theoretical insight and substantive relevance—which are not in inherent conflict—is a matter of debate to this day and will likely continue to be debated well into the future.

2.8 Summary—the three I’s after 1980

In sum, beginning in the early-1980s, consumer research branched out. Much of the work in consumer studies remained within the earlier tradition of the three R’s—that is, rationality (an information-processing decision-oriented buyer), rigor (neo-positivistic experimental designs and quantitative techniques), and relevance (usefulness to marketing managers). Nonetheless, many studies embraced enlarged views of the three major aspects that might be called the three I’s —that is, irrationality (broadened perspectives that incorporate illogical, heuristic, experiential, or hedonic aspects of consumption), interpretation (various qualitative or “postmodern” approaches), and intrinsic motivation (the joy of pursuing a managerially irrelevant consumer study purely for the sake of satisfying one’s own curiosity, without concern for whether it does or does not help a marketing practitioner make a bigger profit).

3 The present—the consumer behavior field today

3.1 present concepts.

In recent years, technological changes have significantly influenced the nature of consumption as the customer journey has transitioned to include more interaction on digital platforms that complements interaction in physical stores. This shift poses a major conceptual challenge in understanding if and how these technological changes affect consumption. Does the medium through which consumption occurs fundamentally alter the psychological and social processes identified in earlier research? In addition, this shift allows us to collect more data at different stages of the customer journey, which further allows us to analyze behavior in ways that were not previously available.

Revisiting the ICABS framework, many of the previous concepts are still present, but we are now addressing them through a lens of technological change (Table 2 )

. In recent years, a number of concepts (e.g., identity, beliefs/lay theories, affect as information, self-control, time, psychological ownership, search for meaning and happiness, social belonging, creativity, and status) have emerged as integral factors that influence and are influenced by consumption. To better understand these concepts, a number of influential theories from social psychology have been adopted into consumer behavior research. Self-construal (Markus and Kitayama 1991 ), regulatory focus (Higgins 1998 ), construal level (Trope and Liberman 2010 ), and goal systems (Kruglanski et al. 2002 ) all provide social-cognition frameworks through which consumer behavior researchers study the psychological processes behind consumer behavior. This “adoption” of social psychological theories into consumer behavior is a symbiotic relationship that further enhances the theories. Tory Higgins happily stated that he learned more about his own theories from the work of marketing academics (he cited Angela Lee and Michel Pham) in further testing and extending them.

3.2 Present Methods

Not only have technological advancements changed the nature of consumption but they have also significantly influenced the methods used in consumer research by adding both new sources of data and improved analytical tools (Ding et al. 2020 ). Researchers continue to use traditional methods from psychology in empirical research (scale development, laboratory experiments, quantitative analyses, etc.) and interpretive approaches in qualitative research. Additionally, online experiments using participants from panels such as Amazon Mechanical Turk and Prolific have become commonplace in the last decade. While they raise concerns about the quality of the data and about the external validity of the results, these online experiments have greatly increased the speed and decreased the cost of collecting data, so researchers continue to use them, albeit with some caution. Reminiscent of the discussion in the 1970s and 1980s about the use of student subjects, the projectability of the online responses and of an increasingly conditioned “professional” group of online respondents (MTurkers) is a major concern.

Technology has also changed research methodology. Currently, there is a large increase in the use of secondary data thanks to the availability of Big Data about online and offline behavior. Methods in computer science have advanced our ability to analyze large corpuses of unstructured data (text, voice, visual images) in an efficient and rigorous way and, thus, to tap into a wealth of nuanced thoughts, feelings, and behaviors heretofore only accessible to qualitative researchers through laboriously conducted content analyses. There are also new neuro-marketing techniques like eye-tracking, fMRI’s, body arousal measures (e.g., heart rate, sweat), and emotion detectors that allow us to measure automatic responses. Lastly, there has been an increase in large-scale field experiments that can be run in online B2C marketplaces.

3.3 Present Aims

Along with a focus on real-world observations and data, there is a renewed emphasis on managerial relevance. Countless conference addresses and editorials in JCR , JCP , and other journals have emphasized the importance of making consumer research useful outside of academia—that is, to help companies, policy makers, and consumers. For instance, understanding how the “new” consumer interacts over time with other consumers and companies in the current marketplace is a key area for future research. As global and social concerns become more salient in all aspects of life, issues of long-term sustainability, social equality, and ethical business practices have also become more central research topics. Fortunately, despite this emphasis on relevance, theoretical contributions and novel ideas are still highly valued. An appropriate balance of theory and practice has become the holy grail of consumer research.

The effects of the current trends in real-world consumption will increase in magnitude with time as more consumers are digitally native. Therefore, a better understanding of current consumer behavior can give us insights and help predict how it will continue to evolve in the years to come.

4 The future—the consumer behavior field in 2040

The other papers use 2030 as a target year but we asked our survey respondents to make predictions for 2040 and thus we have a different future target year.

Niels Bohr once said, “Prediction is very difficult, especially if it’s about the future.” Indeed, it would be a fool’s errand for a single person to hazard a guess about the state of the consumer behavior field twenty years from now. Therefore, predictions from 34 active consumer researchers were collected to address this task. Here, we briefly summarize those predictions.

4.1 Future Concepts

While few respondents proffered guesses regarding specific concepts that would be of interest twenty years from now, many suggested broad topics and trends they expected to see in the field. Expectations for topics could largely be grouped into three main areas. Many suspected that we will be examining essentially the same core topics, perhaps at a finer-grained level, from different perspectives or in ways that we currently cannot utilize due to methodological limitations (more on methods below). A second contingent predicted that much research would center on the impending crises the world faces today, most mentioning environmental and social issues (the COVID-19 pandemic had not yet begun when these predictions were collected and, unsurprisingly, was not anticipated by any of our respondents). The last group, citing the widely expected profound impact of AI on consumers’ lives, argued that AI and other technology-related topics will be dominant subjects in consumer research circa 2040.

While the topic of technology is likely to be focal in the field, our current expectations for the impact of technology on consumers’ lives are narrower than it should be. Rather than merely offering innumerable conveniences and experiences, it seems likely that technology will begin to be integrated into consumers’ thoughts, identities, and personal relationships—probably sooner than we collectively expect. The integration of machines into humans’ bodies and lives will present the field with an expanding list of research questions that do not exist today. For example, how will the concepts of the self, identity, privacy, and goal pursuit change when web-connected technology seamlessly integrates with human consciousness and cognition? Major questions will also need to be answered regarding philosophy of mind, ethics, and social inequality. We suspect that the impact of technology on consumers and consumer research will be far broader than most consumer-behavior researchers anticipate.

As for broader trends within consumer research, there were two camps: (1) those who expect (or hope) that dominant theories (both current and yet to be developed) will become more integrated and comprehensive and (2) those who expect theoretical contributions to become smaller and smaller, to the point of becoming trivial. Both groups felt that current researchers are filling smaller cracks than before, but disagreed on how this would ultimately be resolved.

4.2 Future Methods

As was the case with concepts, respondents’ expectations regarding consumer-research methodologies in 2030 can also be divided into three broad baskets. Unsurprisingly, many indicated that we would be using many technologies not currently available or in wide use. Perhaps more surprising was that most cited the use of technology such as AI, machine-learning algorithms, and robots in designing—as opposed to executing or analyzing—experiments. (Some did point to the use of technologies such as virtual reality in the actual execution of experiments.) The second camp indicated that a focus on reliable and replicable results (discussed further below) will encourage a greater tendency for pre-registering studies, more use of “Big Data,” and a demand for more studies per paper (versus more papers per topic, which some believe is a more fruitful direction). Finally, the third lot indicated that “real data” would be in high demand, thereby necessitating the use of incentive-compatible, consequential dependent variables and a greater prevalence of field studies in consumer research.

As a result, young scholars would benefit from developing a “toolkit” of methodologies for collecting and analyzing the abundant new data of interest to the field. This includes (but is not limited to) a deep understanding of designing and implementing field studies (Gerber and Green 2012 ), data analysis software (R, Python, etc.), text mining and analysis (Humphreys and Wang 2018 ), and analytical tools for other unstructured forms of data such as image and sound. The replication crisis in experimental research means that future scholars will also need to take a more critical approach to validity (internal, external, construct), statistical power, and significance in their work.

4.3 Future Aims

While there was an air of existential concern about the future of the field, most agreed that the trend will be toward increasing the relevance and reliability of consumer research. Specifically, echoing calls from journals and thought leaders, the respondents felt that papers will need to offer more actionable implications for consumers, managers, or policy makers. However, few thought that this increased focus would come at the expense of theoretical insights, suggesting a more demanding overall standard for consumer research in 2040. Likewise, most felt that methodological transparency, open access to data and materials, and study pre-registration will become the norm as the field seeks to allay concerns about the reliability and meaningfulness of its research findings.

4.4 Summary - Future research questions and directions

Despite some well-justified pessimism, the future of consumer research is as bright as ever. As we revised this paper amidst the COVID-19 pandemic, it was clear that many aspects of marketplace behavior, consumption, and life in general will change as a result of this unprecedented global crisis. Given this, and the radical technological, social, and environmental changes that loom on the horizon, consumer researchers will have a treasure trove of topics to tackle in the next ten years, many of which will carry profound substantive importance. While research approaches will evolve, the core goals will remain consistent—namely, to generate theoretically insightful, empirically supported, and substantively impactful research (Table 3 ).

5 Conclusion

At any given moment in time, the focal concepts, methods, and aims of consumer-behavior scholarship reflect both the prior development of the field and trends in the larger scientific community. However, despite shifting trends, the core of the field has remained constant—namely, to understand the motivations, thought processes, and experiences of individuals as they consume goods, services, information, and other offerings, and to use these insights to develop interventions to improve both marketing strategy for firms and consumer welfare for individuals and groups. Amidst the excitement of new technologies, social trends, and consumption experiences, it is important to look back and remind ourselves of the insights the field has already generated. Effectively integrating these past findings with new observations and fresh research will help the field advance our understanding of consumer behavior.

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Malter, M.S., Holbrook, M.B., Kahn, B.E. et al. The past, present, and future of consumer research. Mark Lett 31 , 137–149 (2020). https://doi.org/10.1007/s11002-020-09526-8

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Psychological factors and consumer behavior during the COVID-19 pandemic

Contributed equally to this work with: Adolfo Di Crosta, Irene Ceccato

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The COVID-19 pandemic is far more than a health crisis: it has unpredictably changed our whole way of life. As suggested by the analysis of economic data on sales, this dramatic scenario has also heavily impacted individuals’ spending levels. To better understand these changes, the present study focused on consumer behavior and its psychological antecedents. Previous studies found that crises differently affect people’s willingness to buy necessities products (i.e., utilitarian shopping) and non-necessities products (i.e., hedonic shopping). Therefore, in examining whether changes in spending levels were associated with changes in consumer behavior, we adopted a fine-grained approach disentangling between necessities and non-necessities. We administered an online survey to 3833 participants (age range 18–64) during the first peak period of the contagion in Italy. Consumer behavior toward necessities was predicted by anxiety and COVID-related fear, whereas consumer behavior toward non-necessities was predicted by depression. Furthermore, consumer behavior toward necessities and non-necessities was predicted by personality traits, perceived economic stability, and self-justifications for purchasing. The present study extended our understanding of consumer behavior changes during the COVID-19 pandemic. Results could be helpful to develop marketing strategies that consider psychological factors to meet actual consumers’ needs and feelings.

Citation: Di Crosta A, Ceccato I, Marchetti D, La Malva P, Maiella R, Cannito L, et al. (2021) Psychological factors and consumer behavior during the COVID-19 pandemic. PLoS ONE 16(8): e0256095. https://doi.org/10.1371/journal.pone.0256095

Editor: Marcel Pikhart, University of Hradec Kralove: Univerzita Hradec Kralove, CZECH REPUBLIC

Received: March 8, 2021; Accepted: July 31, 2021; Published: August 16, 2021

Copyright: © 2021 Di Crosta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data are available from the figshare database (accession number(s) DOI: 10.6084/m9.figshare.14865663.v2 , URL: https://figshare.com/articles/dataset/RawData_PO_sav/14865663 ).

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Coronavirus disease 2019 (COVID-19) refers to an infection (SARS-CoV-2) of the lower respiratory tract [ 1 , 2 ], which was first detected in Wuhan (China) in late December 2019. Since then, the number of contagions by COVID-19 has been increasing globally each day [ 3 ]. In March 2020, the World Health Organization (WHO) declared the COVID-19 outbreak a global pandemic [ 4 ]. Subsequently, several national governments implemented long-term full or partial lockdown measures to reduce the spread of the virus. Although these strict measures have proven to be quite effective in containing the further spread of the virus, they have severely impacted the global economic system and caused an unprecedented shock on economies and labor markets [ 5 ]. As a matter of fact, the COVID-19 pandemic can be defined as far more than just a health crisis since it has heavily affected societies and economies. COVID-19 outbreak has unpredictably changed how we work, communicate, and shop, more than any other disruption in this decade [ 6 ]. As reflected by the analysis of economic data on sales, this dramatic situation has greatly influenced consumer attitudes and behaviors. According to a study conducted by the Nielsen Company, the spread of the COVID-19 pandemic led to a globally manifested change in spending levels related to consumer behavior [ 7 ]. Specifically, a growing tendency in the sales of necessities has been observed: consumer priorities have become centered on the most basic needs, including food, hygiene, and cleaning products. In Italy, consumer shopping preferences have changed throughout the pandemic. Initially, when Italy was the first country in Europe to experience the spreading of COVID-19 (between March and April 2020). Consumer behavior tended to compulsively focus on purchasing essential goods, especially connected with preventing the virus, such as protective devices and sanitizing gel [ 8 ]. The pandemic changed the consumption patterns, for instance reducing sales for some product categories (e.g., clothes), and improving sales for other categories (e.g., entertainment products) [ 9 ]. Also, research indicated that job insecurity and life uncertainty experienced during the pandemic negatively impacted on consumer behavior of Italian workers [ 10 ].

It comes as no surprise that in such a situation of emergency, the need for buying necessities takes precedence [ 11 ]. However, the investigation of antecedent psychological factors, including attitudes, feelings, and behaviors underlying changes in consumer behavior during the COVID-19 pandemic, have received less attention. Nevertheless, understanding the psychological factors which drive consumer behavior and products choices can represent a crucial element for two main reasons. First, such investigation can extend our understanding of the underpinnings of the changes in consumer behavior in the unprecedented context of COVID-19. Second, obtained results could be helpful in the development of new marketing strategies that consider psychological factors to meet actual consumers’ needs and feelings [ 12 ]. On the one side, companies could benefit from this knowledge to increase sales during the COVID-19 pandemic [ 13 ]. Moreover, understanding these needs and feelings could be fundamental to improve the market’s preparedness to face future pandemics and emergencies [ 14 , 15 ]. On the other hand, consumers could take advantage of this new market’s preparedness to respond to their actual needs and feelings. As a result, in case of future emergency, factors such as anxiety and a perceived shortage of essential goods could be reduced [ 16 ], whereas well-being and the positive sense of self of the consumers could be supported [ 17 ]. Furthermore, the novelty of the present study lies in two main aspects. First, based on previous studies highlighting that crises differently affect people’s willingness to buy necessities and non-necessities products [ 11 , 18 ], we adopted a fine-grained approach and disentangled between necessities and non-necessities. Second, considering the unprecedented context of the COVID-19 pandemic, we adopted an integrative approach to investigate the role of different psychological factors such as fear, anxiety, stress, depression, self-justifications, personality traits, and perceived economic stability in influencing consumer behavior. Noteworthy, all these factors have been implicated in consumer behavior in previous research, but, to our knowledge, no study has considered all of them at once. Therefore, considering both the lack of studies that have focused on these factors at once and the unique opportunity to study them in the context of such an unprecedented global pandemic, we adopted an integrative approach to get one of the first overviews of the role of the several psychological factors influencing consumer behavior.

Previous studies in consumer psychology and behavioral economics have highlighted that several psychological factors impact consumer behavior differently [ 18 – 20 ]. Consumer behavior refers to the study of individuals or groups who are in the process of searching to purchase, use, evaluate, and dispose of products and services to satisfy their needs [ 12 ]. Importantly, it also includes studying the consumer’s emotional, mental, and behavioral responses that precede or follow these processes [ 21 ]. Changes in consumer behavior can occur for different reasons, including personal, economic, psychological, contextual, and social factors. However, in dramatic contexts such as a disease outbreak or a natural disaster, some factors, more than others, have a more significant impact on consumer behavior. Indeed, situations that potentially disrupt social lives, or threaten individuals’ health, have been proven to lead to strong behavioral changes [ 22 ]. An example is panic buying, a phenomenon occurring when fear and panic influence behavior, leading people to buy more things than usual [ 23 ]. Specifically, panic buying has been defined as a herd behavior that occurs when consumers buy a considerable amount of products in anticipation of, during, or after a disaster [ 24 ]. A recent review on the psychological causes of panic buying highlighted that similar changes in consumer behavior occur when purchase decisions are impaired by negative emotions such as fear and anxiety [ 25 ]. Noteworthy, in the context of the COVID-19 pandemic, Lins and Aquino [ 23 ] showed that panic buying was positively correlated with impulse buying, which has been defined as a complex buying behavior in which the rapidity of the decision process precludes thoughtful and deliberate consideration of alternative information and choice [ 25 ]. The analysis of the different psychological factors involved in consumer behavior and changes in purchase decisions still represents an area that is scarcely explored. Arguably, during an uncertain threatening situation, such as a health crisis or a pandemic, the primitive part of our brain usually becomes more prominent, pushing individuals to engage in behaviors that are (perceived as) necessary for survival [ 26 – 29 ]. Importantly, these primitive instinctual behaviors can override the rational decision-making process, having an immense impact on usual consumer behavior. Therefore, the basic primitive response of humans represents the core factor responsible for changes in consumer behavior during a health crisis [ 16 ]. Specifically, fear and anxiety originated from perceived feelings of insecurity and instability, are the factors driving these behavioral changes [ 30 ]. In line with the terror management theory [ 31 ], previous studies have shown that external events, which threaten the safety of individuals, motivate compensatory response processes to alleviate fear and anxiety [ 32 , 33 ]. These response processes can prompt individuals to make purchases to gain a sense of security, comfort, and momentarily escape, which can also serve as a compensatory mechanism to alleviate stress. However, as such buying motivation represents an attempt to regulate the individuals’ negative emotions, the actual need for the purchased products is often irrelevant [ 34 ].

Pandemics and natural disasters are highly stressful situations, which can easily induce negative emotions and adverse mental health states [ 35 – 37 ] such as perceived lack of control and instability, which are core aspects of emergency situations, contribute directly to stress. In turn, research has highlighted that stress is a crucial factor in influencing consumer behavior. For example, past studies have shown that individuals may withdraw and become passive in response to stress, and this inaction response can lead to a decrease in purchasing [ 38 , 39 ]. However, some studies point out that stress can lead to an active response, increasing impulsive spending behaviors [ 40 , 41 ]. Moreover, event-induced stress can lead to depressive mood. In some cases, the depressive mood may translate into the development of dysfunctional consumer behavior, such as impulsive (the sudden desire to buy something accompanied by excessive emotional response) and/or compulsive buying (repetitive purchasing due to the impossibility to control the urge) [ 41 , 42 ]. In this context, Sneath and colleagues [ 37 ] highlighted that changes in consumer behavior often represent self-protective strategies aimed at managing depressive states and negative emotions by restoring a positive sense of self. Importantly, a recent study conducted during the COVID-19 pandemic showed that depression predicted the phenomenon of the over-purchasing, which was framed as the degree to which people had increased their purchases of some necessities goods (e.g. food, water, sanitary products, pharmacy products, etc.) because of the pandemic [ 43 ].

A recent study recommended a differentiation between necessity and non-necessity products to better understand consumer behavior in response to stressful situations [ 18 ]. According to the authors, contrasting findings on the link between stress and consumer behavior may be due to the fact that stress affects certain purchasing behaviors negatively, but others positively, depending on the type of product under investigation. On one side, it has been argued that consumers may be more willing to spend money on necessities (vs. non-necessities) by making daily survival products readily available. Accordingly, recent research documented an increase in buying necessities products (i.e., utilitarian shopping) during and after a traumatic event [ 11 ]. However, other findings showed that impulsive non-necessities purchasing (i.e., hedonic shopping) could also increase as an attempt to escape or minimize the pain for the situation. That is, non-necessities buying is used as an emotional coping strategy to manage stress and negative emotional states [ 44 ]. To reconcile these findings, Durante and Laran [ 18 ] proposed that people adopt strategic consumer behavior to restore their sense of control in stressful situations. Hence, high stress levels generally lead consumers to save money and spend strategically on products perceived as necessities. Importantly, regarding the impact of perceived stress due to the COVID-19 pandemic on consumer behavior, a recent study showed that the likelihood of purchasing quantities of food larger than usual increased with higher levels of perceived stress [ 45 ].

Another psychological factor implicated in consumer behavior that deserves special attention is self-justification strategies [ 46 ]. Self-justification refers to the cognitive reappraisal process by which people try to reduce the cognitive dissonance stemming from a contradiction between beliefs, values, and behaviors. People often try to justify their decisions to avoid the feeling of being wrong to maintain a positive sense of self [ 17 ]. In consumer behavior research, it is widely acknowledged that consumers enhance positive arguments that support their choices and downplay counterarguments that put their behavior in question [ 47 ]. Based on previous research, it is plausible that, within the context of the COVID-19 pandemic, self-justifications for buying non-necessities products may also include pursuing freedom and defying boredom [ 11 , 48 ]. Further, the hedonistic attitude of “I could die tomorrow” or “You only live once” could certainly see a resurgence during the COVID-19 emergency [ 48 ], and become a crucial mechanism accounting for individual differences in consumer behavior. Based on these considerations, in the context of the COVID-19 pandemic, self-justifications strategies could be relevant for non-necessities, since products for fun or entertainment could be more suited to the pursuit of freedom and to defy boredom. Conversely, self-justifications strategies related to necessities could be implemented to a lesser degree, due to the very nature of the products. The unprecedented context of the pandemic could already justify the purchase of those essential goods by itself, and additional justifications may not be necessary.

Furthermore, several studies have shown that household income has a significant impact in determining people’s expenses [ 49 – 51 ]. Not surprisingly, the research highlighted a positive relationship between income and spending levels [ 52 ]. Income is defined as money received regularly from work or investments. Interestingly, a different line of research pointed out that self-perceived economic stability is a more appropriate determinant of consumer behavior than actual income [ 53 , 54 ]. Usually, people tend to report subjective feelings of income inadequacy, even when their objective financial situation might not support such attitude [ 55 ]. An interesting explanation for this bias draws on the social comparison process. Indeed, the study of Karlsson et colleagues [ 53 ] showed that, compared to families who considered themselves to have a good financial situation, households which considered themselves to be worse off economically than others reported fewer purchases of goods, perceived the impact of their latest purchase on their finance to be greater, and planned purchases more carefully. Furthermore, a recent study in the context of the COVID-19 emergency showed that people who believed to have limited financial resources were the most worried about the future [ 56 , 57 ]. Therefore, in the present study, we measured both the income and the perceived economic situation of the respondents to respectively consider the objective economic information and the subjective perception of respondents. However, considering the state of uncertainty experienced by many households during the COVID-19 pandemic [ 58 ], we changed the comparison from other families to participants’ economic situation in different time frames. We asked respondents to report perceived economic stability before, during, and after the emergency.

Finally, besides situational factors related to the specific emergency, the individuals’ personality traits are likely to have a role in determining consumer behavior as well. Past research has highlighted that the Big Five personality traits [ 59 ] can differently predict consumer behavior [ 60 ]. Specifically, conscientiousness, openness, and emotional stability (alias neuroticism) were related to compulsive buying, impulsive buying, and utilitarian shopping. Nevertheless, how different personality traits are related to consumer behavior is still an open question [ 61 ].

We conducted a nationwide survey in the Italian population to examine consumer behavior during the lockdown phase due to the COVID-19 pandemic. Since the COVID-19 emergency has emphasized the usefulness of essential goods (e.g. food, medications, etc.) compared to non-essential products (e.g. luxury items such as clothes and accessories) [ 62 ], in our study, we categorized products in necessities and non-necessities. Furthermore, changes in spending levels (necessities vs. non-necessities) were examined to confirm the effect that COVID-19 had on people’s expenses. Moreover, we tried to clarify the relationship between changes in spending levels and changes in consumer behavior. Finally, we focused on the psychological factors underlying changes in consumer behavior toward the target products. Based on the literature, we expected to find an increase in purchases with a more noticeable rise in necessity products. Specifically, we explored potential underpinnings of consumer behavior by examining mood states and affective response to the emergency, perceived economic stability, self-justification for purchasing, and personality traits. All these factors have been implicated in consumer behavior in previous research, but, to our knowledge, no study has considered all of them at once. Therefore, in this study, we adopted an integrative approach to study the contribution of different psychological factors by considering their mutual influence (see Fig 1 ). Specifically, based on the empirical findings and theoretical accounts presented above, we hypothesized that during the COVID-19 pandemic:

• Higher levels of anxiety and COVID-related fear would explain changes in consumer behavior, increasing the need for buying necessities.
• Higher levels of stress would lead consumers to save money or, in alternative, would increase the need to spend money on necessities (i.e., utilitarian shopping).
• Higher levels of depressive state would be associated with an increase in the need for buying, both necessities and non-necessities.
• Higher implementation of self-justification strategies would be associated with a higher need for buying, especially for non-necessities.
• Higher perceived economic stability would be associated with an increase in the need for both necessities and non-necessities.

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https://doi.org/10.1371/journal.pone.0256095.g001

The construct involved in the study is placed in the center of the figure. Arrows depart from these constructs to show the hypothesized relationship between the constructs and the outcomes of the present study (Necessities and Non-necessities). The symbol “±” was used to take into consideration two possible opposite directions.

Materials and methods

Data were collected through a series of questionnaires, using a web-based survey implemented on the Qualtrics software. The survey was active in the period starting from April 1st, 2020, to April 20th, 2020, during the first peak of the contagion in Italy. We used a convenience sample due to the exceptional situation of the COVID-19 pandemic and the time constraints to conduct our investigation. Therefore, participants were recruited through word-of-mouth and social media. Inclusion criteria were the age over 18 and be resident in Italy. First, socio-demographic information was collected, including gender, age, annual income, and education. Then, questions on spending levels and consumer behavior, both before the COVID-19 pandemic and during the first week of lockdown in Italy, were presented, separating necessities and non-necessities. Finally, a series of specifically created questionnaires and standardized measures were administered to investigate psychological and economic variables.

Participants

A total of 4121 participants were initially recruited. For the present study, we adopted a rigorous approach, excluding 104 participants over the age of 64, since they relied on retirement benefits and -from an economic point of view- were considered a specific population, not comparable to the rest of the sample [ 63 ]. Furthermore, we excluded 184 participants who did not report spending any money before the COVID-19 pandemic on buying necessities and/or non-necessities. Therefore, 3833 Italian participants (69.3% women, age M = 34.2, SD = 12.5) were included in this study. All participants provided their written informed consent before completing the survey. The study was conducted following the ethical standards of the Declaration of Helsinki and was approved by the Institutional Review Board of Psychology (IRBP) of the Department of Psychological, Health and Territorial Sciences at G. d’Annunzio University of Chieti-Pescara (protocol number: 20004). Participants did not receive monetary or any other forms of compensation for their participation.

Demographic variables

A demographic questionnaire was administered to collect background information. The questions considered age, gender, annual income, and education. The annual income was then categorized into five levels, based on the income brackets established by the Italian National Statistical Institute [ 64 ]. Education was categorized into five levels, from elementary to school to postgraduate degree.

Consumer behavior during COVID-19

We created this questionnaire from scratch to get a comprehensive overview of people’s economic attitudes and behaviors during the COVID-19 emergency. The idea of this new questionnaire was developed based on a series of previous studies on consumer behavior [ 43 , 65 – 67 ]. However, specific items were developed from scratch adapting them to the specific unprecedented context of the COVID-19 pandemic. Specifically, these items were created following a series of group discussions between all co-authors of the present study. To directly measure changes in consumer behavior due to the COVID-19 pandemic, participants were requested to compare their actual behavior to their normal behavior before the COVID-19 outbreak. Therefore, the initial statement in the questionnaire underlined that answers had to be given by referring to the COVID-19 emergency period compared to everyday life before the outbreak.

The factor structure and reliability were evaluated in the larger sample ( n = 4121), using principal component analysis (PCA) and Cronbach’s alpha. The results revealed a six-factor structure and satisfactory reliability values (see S1 Table for more details). Note that the PCA and reliability analyses were also conducted on the current subsample, and the pattern of results did not change.

For the present study’s aims, we focused on three scales: “Necessities”, “Non-necessities”, and “Self-justifications”. Items are shown in Table 1 . The first two scales investigated consumer behavior toward the different framed products. Specifically, items addressed the individual’s attitudes, feelings, and behaviors toward necessities and non-necessities. Thus, higher scores reflected greater value (e.g., need, utility) placed on the target products.

https://doi.org/10.1371/journal.pone.0256095.t001

The self-justifications scale referred to consumers’ thoughts to justify their purchases, with no distinction between necessity and non-necessity products. Higher scores reflected a frequent use of self-justifications in purchasing items.

For all these scales, responses were given on a Likert scale ranging from 0 ( not at all ), to 100, ( extremely ). Total scores on each scale were obtained by averaging all items.

Change in spending levels due to COVID-19

A fourth scale, i.e. “Spending Habits,” was extracted from the questionnaire mentioned above. As we aimed at measuring changes in the spending levels due to the COVID-19 emergency, we decided to use single items instead of the total scale score (items are presented in Table 1 ). Specifically, we created three percentage scores: “Changes in General Spending”, “Changes in Necessities spending”, and “Changes in Non-necessities spending” considering the difference between the money spent during the first week of lockdown, and the money spent on average in a week before the emergency (see Table 1 notes). Scores reflect the change in the amount (in Euro) that people devolved in purchasing the target products (hypothetical range from -1999 to +1999).

Big Five Inventory 10-item (BFI-10)

Big Five Inventory 10-item (BFI-10) is a short scale designed to briefly assess the five personality traits with two items for each trait. Specifically, these traits are: Agreeableness (example item: “I see myself as someone who is generally trusting”), Conscientiousness (example item: “I see myself as someone who does a thorough job”), Emotional stability (example item: “I see myself as someone who is relaxed, handles stress well”), Extraversion (example item: “I see myself as someone who is outgoing, sociable”), and Openness (example item: “I see myself as someone who has an active imagination”) [ 68 ]. In addition, respondents are asked to indicate whether they agree or disagree with each statement on a 5-point Likert-type scale, ranging from 1 ( not agree at all ) to 5 ( totally agree ). A previously validated Italian version was used in the present study [ 69 ].

Generalized anxiety disorder (GAD-7)

The GAD-7 [ 70 ] is a 7-item self-reported measure designed to screen for generalized anxiety disorder and to measure the severity of symptoms, based on the DSM-IV criteria. This measure is often used in both clinical practice and research. Specifically, respondents are asked the frequency they have experienced anxiety symptoms in the past two weeks (e.g., “Not being able to stop or control worrying”) on a 4-point Likert scale, ranging from 0 ( not at all ) to 3 ( nearly every day ). The total score ranges from 0 to 21, with higher scores indicating worse anxiety symptomatology.

Patient health questionnaire (PHQ-9)

The patient health questionnaire (PHQ-9) is a 9-item self-reported brief diagnostic measure for depression [ 71 ]. Specifically, respondents are asked of the frequency they felt bothered by several depressive symptoms during the past two weeks (e.g., “Little interest or pleasure in doing things”) on a 4-point Likert scale, ranging from 0 ( not at all ) to 3 ( nearly every day ). Total score ranges from 0 to 27, with higher scores indicating higher depressive symptoms.

Perceived Stress Scale (PSS)

The Perceived Stress Scale (PSS) is a 14-item self-report measure designed to assess the degree to which situations are appraised as stressful [ 72 ]. Each item (e.g., “In the last month, how often have you been upset because of something that happened unexpectedly?”) is rated on a 5-point Likert scale ranging from 0 ( never ) to 4 ( very often ). Thus, the total score ranges from 0 to 56, with a higher score indicating a higher level of perceived stress during the COVID-19 emergency.

Fear for COVID-19

We administered the Fear for COVID-19 questionnaire to measure fear and concerning beliefs related to the COVID-19 pandemic [ 35 , 36 , 73 ]. This questionnaire was created from the assumption that, during a health crisis, the individual’s fear is determined by both the hypothesized susceptibility (i.e., probability of contracting a disease) and the expected severity of the event (i.e., perceived consequences of being infected) [ 25 ]. Therefore, the 8 items dealt with the perceived probability of being infected by COVID-19 (Belief of contagion) and the possible consequences of the contagion (Consequences of contagion). See Table 1 for the complete list of the items. Previous studies have reported the PCA and reliability of the questionnaire [ 36 ]. Responses were given on a Likert scale ranging from 0 ( not at all ), to 100, ( extremely ). A total score was obtained by averaging the items (range 0–100).

Perceived economic stability

This questionnaire was developed to assess the subjective perception of an individual’s economic situation. The PCA in the larger sample revealed a unidimensional structure (see S2 Table for more details). The scale assessed perceived economic stability in three different timepoints: before, during, and after (in terms of expectation) the COVID-19 pandemic. Responses were given on a Likert scale ranging from 0 ( not at all ), to 100, ( extremely ). The total score was calculated by averaging these three items (range 0–100).

Statistical analysis

We preliminary investigated changes in spending levels due to the COVID-19 pandemic, comparing expenses before the emergency to expenses during the COVID-19 pandemic. First, we analyzed changes in the average general spending level. Then, we performed dependent (paired) sample t -tests between “Changes in necessities spending” and “Changes in non-necessities spending” to examine differences between products framed as necessities and non-necessities.

Afterward, we checked whether changes in spending levels were associated with changes in consumer behavior by conducting Pearson’s correlation analyses, respectively between “Changes in necessities spending” and “Necessities”, and “Changes in non-necessities spending” and “Non-necessities” scores.

Finally, to investigate the psychological underpinnings of consumer behavior, we performed two hierarchical multiple regressions, respectively, with “Necessities” (Model 1) and “Non-necessities” (Model 2) as outcomes. The same predictors were entered in Model 1 and Model 2. Specifically, the order of the steps was designed to include at first the socio-demographic information as control variables. Hence, we entered the age, gender, annual income brackets, and education in the first step. In Step 2, we included the personality measures (i.e., Big-Five personality traits) since these traits are stable and are not affected by the specific situation. In Step 3, Anxiety, Depression, and Stress were entered, to analyze the impact of emotional antecedents of consumer. Further, we decided to include Fear for the COVID-19 in a separate fourth step to evaluate the effect of this specific aspect. We included perceived economic stability at Step 5 after the psychological variables. This choice allowed to analyze the impact of the perceived economic stability after controlling for the role of emotional antecedents on consumer behavior. Finally, following the same logic, we included self-justifications strategies.

Considering “Changes in General spending”, our results showed that our sample reported, on average, an increase of 60.48% in the general spending level during the first week of lockdown. Furthermore, significant differences between “Changes in Necessities spending” and “Changes in Non-necessities spending”, t (3832) = 11.99, p < .001, were detected. Indeed, the spending level for necessities products showed an increase of 90.69%, while for non-necessities products, the average increase was only 36.11%. Means and standard deviations are presented in Table 2 .

https://doi.org/10.1371/journal.pone.0256095.t002

The results of the correlation analyses indicated that there was a significant positive association between “Changes in necessities spending” and “Necessities”, r (3831) = .22, p < .001. Furthermore, a significant positive association was highlighted between “Changes in non-necessities spending” and “Non-necessities”, r (3831) = .23, p < .001. Therefore, people’s changes in spending levels were related to their attitudes and feelings toward specific products. This finding supported our choice to investigate the psychological underpinnings of people’s consumer behavior.

Hierarchical multiple regression analyses were performed on the two consumer behavior scores. In addition, control variables, psychological factors, and economic variables were entered as predictors as detailed above.

Regarding Model 1 (Necessities), results showed that all the steps explained a significant amount of additional variance (see Table 3 for detailed results). When personality traits were entered in the model (Step 2), only agreeableness, openness, and emotional stability negatively predicted the outcome. However, when anxiety, depression, and stress were entered in the model (Step 3), only openness remained statistically significant. The variables entered in Step 3 contributed to explaining 7% of the variance, with anxiety and stress positively predicting the outcome. Adding fear for COVID-19 in the following step increased the explained variance by 6%, reduced the impact of anxiety, and completely overrode the effect of stress, which became non-significant. In the following steps, perceived economic stability offered a small but significant contribution (1%), and Self-justifications explained even further variance (4%). Overall, in the final step, the final model explained 23% of the variance in Necessities. Inspecting coefficients, we found that, after accounting for control variables, openness ( p < .001), anxiety ( p < .001), fear for COVID-19 ( p < .001), perceived economic stability ( p < .001), and self-justifications ( p < .001) emerged as significant predictors.

https://doi.org/10.1371/journal.pone.0256095.t003

In Model 2 (Non-necessities), results indicated that each step significantly contributed to explaining the outcome (see Table 4 ). In Step 2, personality traits explained 2% of the outcome variance, with consciousness and openness emerging as significant predictors and remaining significant until the final step. Notably, consciousness was negatively associated with non-necessities behavior, while high scores in openness were associated with higher scores on the Non-necessities scale. In Step 3, only depression was significantly and positively related to the outcome and remained so in subsequent models. Both fear for COVID-19 and perceived economic stability further significantly explained the outcome, albeit weakly (about 1% of variance each one). Higher levels of fear and perceived economic stability were associated with higher scores on the Non-necessities scale. Noteworthy, adding Self-justifications in the final step explained a substantial share of variance, equal to 12%. Specifically, higher scores on self-justifications were associated with higher scores on the Non-necessities scale. Furthermore, self-justifications also had a greater impact on non-necessities compared to those had on necessities, t (7664) = -10.60, p < .05. Total variance explained in the final step was 22%, with conscientiousness ( p < .001), openness ( p = .001), depression ( p = .002), perceived economic stability ( p = .009), and self-justifications ( p < .001) being significant predictors.

https://doi.org/10.1371/journal.pone.0256095.t004

The present study aimed to examine changes in consumer behavior and their psychological antecedents during the lockdown period due to the COVID-19 pandemic. We were specifically interested in separating necessity and non-necessity products since previous studies suggested that such a distinction is helpful to better understand consumer behavior[ 18 , 74 ]. First, our results indicated a 61% increase in spending levels during the first week of the lockdown, compared to the average expenses before the health crisis. Furthermore, spending levels were differently increased for buying products framed as necessities (91%) and non-necessities (36%). Second, we examined consumer behavior through Necessities and Non-necessities scales, which included measures related to the psychological need of buying, the specific aspects of the purchase experience (e.g., impulsiveness, perceived utility, satisfaction), and the number of products purchased. Our results highlighted that changes in consumer behavior were positively associated with changes in spending levels during the COVID-19 emergency.

Finally, we focused on psychological factors that can explain these changes in consumer behavior. In this context, our hypothesis about the role of the identified psychological factors in predicting consumer behavior during COVID-19 was supported. Also, our findings confirmed the importance of separating necessities from non-necessities products, as we found that they had different psychological antecedents. Regarding the investigation on spending levels, our findings are in line with sales data reporting that, during the COVID-19 pandemic, consumer priorities have become more centered on necessities, including food, hygiene, and cleaning products[ 7 , 62 ]. Therefore, the present study confirmed the greater tendency to buy necessities products during the COVID-19 pandemic. It is noteworthy to mention that our sample also reported an increase in spending levels related to non-necessities products. These data can be explained by referring to previous research that considered increases in non-necessities spending levels to respond to the hedonistic pursuit of freedom, defying boredom, restoring the sense of self, and compensatory mechanism, to alleviate negative psychological states[ 16 , 32 , 34 , 37 , 44 , 75 ]. However, as highlighted in the study by Forbes and colleagues[ 76 ] these hedonic needs and compensatory mechanisms can have a different impact during or in the aftermath of a crisis. In addition, the authors highlighted that the consumption of non-necessities products increased, as a way of coping to alleviate negative psychological states, particularly in the short term after a natural disaster. According to these results, a recent study conducted during the COVID-19 pandemic suggested that some factors, such as the degree of perceived threat, may vary during the COVID-19 pandemic, thus, having a different impact on consumer behavior[ 77 ]. Therefore, future research could delve into the analysis of changes in consumer behavior over time in relation to the different phases of the COVID-19 pandemic.

Regarding our investigation of consumer behavior’s antecedent psychological factors, we found partly different antecedents for necessities and non-necessities. Regarding demographic effects, in the present study, we found that men were more oriented in terms of needs and feelings toward non-necessities than women. A possible explanation could consider the context of the COVID-19, whereas the lockdown has imposed the closure of physical stores. In this context, it could be appropriate to refer to those studies that found several gender differences between consumer e-commerce adoption and purchase decision making. Specifically, research has shown that men and women have different psychological pre-disposition of web-based purchases, with men having more positive attitudes toward online shopping[ 78 , 79 ]. Furthermore, a study conducted during COVID-19 showed that women spent more time on necessities such as childcare and chores compared to men[ 80 ]. Regarding age differences, we found that younger people were more oriented toward non-necessities products. A study conducted in Italy during the COVID-19 pandemic highlighted that older adults showed lower negative emotions than younger adults[ 73 , 81 , 82 ]. In this view, it is possible that lower emotional antecedents, such as depressive states, lowered the need to buy non-necessities for more aged people. Another study conducted during the COVID-19 pandemic showed that older adults, aged 56 to 75, had significantly reduced the purchase of non-necessities goods compared to younger people[ 83 ]. Furthermore, considering the closure of physical stores, it is possible that younger people were more able and got used to buy a broader range of non-necessities products by e-commerce. However, it is important to note that we excluded in the present study people aged over 65. We also found a positive effect of income on necessities. A possible explanation is that people more stable from an economic point of view were more oriented to feel the need to buy products. However, surprisingly we did not find this effect for non-necessities. Finally, we found a positive effect of education on non-necessities. This data is congruent with another study conducted during the COVID-19 pandemic, showing that people with higher education (e.g., bachelor’s degrees and graduate or professional degrees) tended to buy an unusual amount of goods than people with lower education[ 84 ].Furthermore, another study highlighted that during COVID-19 pandemic entertainment and outdoor expenses significantly varied across different education groups[ 85 ]. Considering the present results, further studies should better investigate the impact of socio-demographic factors on the need to purchase necessities and non-necessities during health emergency and natural disaster.

Furthermore, after accounting for control variables (gender, age, income brackets, and education), consumer behavior toward necessities was explained by personality traits (openness), negative emotions (anxiety and COVID- related fear), perception of economic stability, and self-justifications. On the other side, consumer behavior toward non-necessities was explained by conscientiousness, openness, depression, perceived economic stability, and self-justifications.

Present findings showed that negative feelings have a considerable role in predicting changes in consumer behavior related to necessities products. This result is consistent with previous literature showing that, during a health crisis, fear and anxiety are developed from perceived feelings of insecurity and instability[ 30 ]. To reduce these negative feelings, people tend to focus on aspects and behaviors that can help them regain control and certainty, such as buying[ 86 ]. Therefore, changes in consumer behavior could be explained as a remedial response to reduce fear and anxiety related to the COVID-19 emergency. According to our hypothesis, present findings indicated that fear and anxiety play an important role in predicting changes in consumer behavior related to necessities. In contrast, no significant effects were found on non-necessities. A possible explanation for this remarkable difference can be provided by research in survival psychology, which highlighted that individuals might undergo behavioral changes during events such as natural disasters or health crises, including herd behavior, panic buying, changes in purchasing habits, and decision making[ 8 , 76 ]. Following these changes, individuals can be more engaged in behaviors that are necessary for survival[ 26 , 87 ]. In this view, COVID-related fear and anxiety could lead individuals to feel the need to buy necessities products useful for daily survival.

Stress is another factor suggested to differently affect changes in consumer behavior toward necessities and non-necessities[ 18 ]. It is noticeable that consumers experiencing stressful situations may show increased spending behavior, explicitly directed toward products that the consumer perceives to be necessities and that allow for control in an otherwise uncontrollable environment[ 18 ]. Our results partly support this position, showing that stress has a specific role in predicting changes in consumer behavior related to necessities but not to non-necessities. However, the role of stress was no longer significant when fear was entered in the regression model. Noteworthy, we focused on fear for COVID-19, therefore, it is possible that in such an exceptionally unprecedented situation, fear had a prominent role compared to stress. Moreover, previous literature shows that the relationship between fear and consumer behavior increases as the type of fear measured becomes more specific[ 88 ]. In this sense, further studies could delve into the relationship between fear and stress in relation to consumer behavior.

Notably, past studies had found a relationship between depressive states and consumer behavior, suggesting that changes in consumer behavior can represent self-protective behaviors to manage negative affective states[ 37 ]. The role of depression was highlighted by our results in respect to consumer behavior only related to non-necessities. Therefore, conversely to the study conducted in the UK and Ireland during the COVID-19 pandemic by Bentall et colleagues (2021), we did not find a relationship between depression and buying necessities. It is important to note that we described non-necessities products as “products for fun or entertainment”. In our opinion, people with higher levels of depressive symptoms may feel a greater need for this kind of product. Thus, people were drawn more toward this category of purchases because it was better suited to satisfy compensatory strategies to improve their negative emotional states. However, future studies are required to investigate this possibility and deepen the relationship between depressive states and the need to buy necessities and non-necessities. Furthermore, considering that depressive mood can be related to severe dysfunctional aspects of consumer behavior, such as impulsivity and compulsivity, future clinical studies should further investigate this relationship.

Furthermore, based on the limited and contrasting literature on this topic, we considered the role of personality traits. As suggested by previous studies, conscientiousness and openness were found to be associated with consumer behavior[ 89 – 91 ]. Interestingly, we found that personality traits were more relevant in consumer behavior toward non-necessities than necessities products. Only openness had a role in (negatively) predicting consumer behavior toward necessities, whereas conscientiousness (negatively) and openness (positively) predicted consumer behavior toward non-necessities. Unexpectedly, we found that people with a high level of openness showed high scores in consumer behavior toward non-necessities but low scores in necessities products. We speculated that individuals with higher levels of openness, which are more inclined to develop interests and hobbies[ 92 ], might have experienced a higher need to purchase non-necessities products during the lockdown. On the other hand, individuals with lower scores of openness, which tend to prefer familiar routines to new experiences and have a narrower range of interests, might have been more focused on purchasing necessity products. However, further studies should investigate the different roles of openness on necessities vs non-necessities consumer behavior. Globally, we acknowledge that the specific role and directions of these different personality traits on consumer behavior toward necessities and non-necessities is still an unexplored question, fully deserving of further investigations.

Finally, in both regression models, perceived economic stability and self-justifications predicted changes in consumer behavior. It comes as no surprise that individuals who perceived themselves and their family as more economically stable were prone to spend more in both products categories, necessities and non-necessities [ 52 , 53 ]. More intriguing, we found that the self-justifications that consumers adopted to motivate their purchases were a strong predictor of consumer behavior, especially in relation to non-necessities, where it explained the largest amount of variance (12%). Therefore, our hypothesis on the greater impact of self-justifications strategies on non-necessities compared to necessities was confirmed. Non-necessities, framed as products for fun or entertainment, seem more suited to satisfy that pursuit of freedom and the need to defy boredom that people increasingly experienced during the COVID-19 pandemic[ 48 ]. Therefore, we confirmed that the hedonistic attitude is an important predictor of consumer behavior during the COVID-19 pandemic. This result supported and extended previous literature showing that, during a crisis, changes in consumer behavior are related to self-justifications and rationalizations that people formulate to feel right in making their purchases, including the pursuit of freedom and the reduction of boredom[ 11 , 48 ]. Companies and markets can acknowledge this process and use it to develop new marketing strategies to meet consumers’ actual needs, feelings, and motivation to purchase during the COVID-19 emergency[ 12 ]. On the one hand, satisfying these needs could support and favor well-being and the positive sense of self, which are essentially sought by the consumer developing such self-justification strategies[ 17 ]. On the other hand, focusing on strategies that consider these psychological self-justifications could be a winning marketing strategy for increasing sales, contributing to the economic recovery after the COVID-19 outbreak[ 13 ].

The results of the present study highlighted that the COVID-19 pandemic had a considerable impact on consumer behavior. In our sample, this impact resulted in increased spending levels accompanied by an increase in the psychological need to purchase both necessities and non-necessities products. Furthermore, our findings demonstrated that several psychological factors predicted these changes in consumer behavior. Notably, consumer behavior respectively toward necessities and non-necessities differed on some psychological predictors.

Some limits of the current study need to be acknowledged. First, we studied consumer behavior from a broad perspective on a non-clinical sample, therefore we did not include dysfunctional aspects related to consumer behavior, such as impulsivity and compulsivity buying and hoarding behavior, which the emergency may elicit. Hence, in relation to the COVID-19 pandemic, it would be interesting to integrate our results with investigations of dysfunctional aspects of consumer behavior. Furthermore, since the unique opportunity to study psychological factors and consumer behavior during this unprecedented period, we adopted an integrative approach to consider the impact of several psychological factors at once, obtaining one of the first overviews of consumer behavior during the COVID-19 pandemic. However, combining all these psychological factors could have led to an aggregation bias[ 93 ], which could have masked the specific roles of each of the individual factors influencing consumer behavior. Therefore, future studies could adopt a more fine-grained approach to disentangle the role of each factor. Another limit is that we collected data during the initial stage of the COVID-19 outbreak in Italy. Notably, we reasoned that focusing on the very first period of the lockdown would likely allow us to capture the greater shift in consumer behavior, thus offering compelling evidence on the first impact of the pandemic on consumers. Nevertheless, it is likely that consumer behavior will undergo further changes in the longer term. Hence, future studies should investigate the evolution of consumer behaviors in relation to the development of the pandemic. Indeed, it is likely that when the “sense of urgency” and the negative affective reaction to the emergency will decrease, also the need for buying and purchases preferences would change. Furthermore, since we asked participants to estimate their weekly expenditures before and during the COVID-19 pandemic, it is important to keep in mind that our study focused on the people’s perception of changes in expenses. We did not know how much reliable these estimations were, and it is possible that objective assessment of change in the amount of money spent before and during the pandemic diverge from subjective views. In the present study, we focused on individual internal factors that could influence consumer behavior. However, other external factors, including the lockdown restrictions as the closure of physical stores, had certainly had a further impact on consumer behavior. Notwithstanding these limitations, this study represents one of the first attempts to examine changes in consumer behavior during the COVID-19 pandemic from a behavioral economic perspective, providing a thorough analysis of the psychological factors driving changes in consumer behavior, with a direct link to previous psychological research in consumer behavior. Furthermore, our results provided new evidence on the role of psychological factors influencing necessities and non-necessities spending and extended our knowledge of the antecedents of consumer behavior changes during the unprecedented health crisis we are experiencing.

In conclusion, the present study, by shedding new light on changes in people’s behavior due to the pandemic, fits into the growing body of research which helps increase economic and psychological preparedness in the face of future health emergencies.

Supporting information

S1 table. pattern matrix of the pca for the questionnaire on consumer behavior during the covid-19 pandemic..

https://doi.org/10.1371/journal.pone.0256095.s001

S2 Table. PCA for the “Perceived economic stability” questionnaire.

https://doi.org/10.1371/journal.pone.0256095.s002

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More From Forbes

Consumer confidence takes a hit in april: can they shake it off again.

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Consumers feel inflationary prices rising higher but have yet to show a pullback on spending.

Just as larger indicators, like grocery food prices, show signs of easing, consumers have apparently decided to throw in the towel. Of course, the past four months have been a litany of consumers saying they’re pretty much done with high prices and high interest rates, and yet still spending, so it’s hard to take it seriously, but there are definitely bigger signs of weakness out there now than we saw earlier in the year. Couple that with a few reminders that we’re still in the midst of the giant social experiment that is tech’s impact on humanity, and you have this week’s take on retail and technology news. Let’s dive in.

Retail Economic Indicators

UK non-food prices entered deflationary territory in April, hitting -0.6% and down from 0.2% in March. Food inflation was higher, but also down to 3.4% from 3.7% in March, both according to the BRC-Nielsen index. And Catalina’s Shopping Basket Index reported that annual inflation had decreased in the US in April across 10 major grocery categories, including coffee, frozen vegetables, soft drinks and waters, and even yogurt. However, there were also some increases in inflation and Catalina’s commentary highlighted that consumers aren’t necessarily feeling the impact of slower inflation yet. I guess whether prices are rising fast or more slowly, they’re still rising, and that’s what consumers are feeling.

Which might explain why consumer confidence took a hit in April, according to The Conference Board. Overall confidence fell to 97.0 in April from 103.1 in March, which had already been revised downward. TCB, which has been a bit more negative about the prospects for 2024 in general, felt motivated to point out that while this is a retreat, it’s still movement within a relatively narrow band that has stayed consistent over the last two years. The present situation part of the index is much higher, though it still declined – to 142.9 in April from 146.8 in March. Consumer expectations for the future are much lower, and have been for a while – down to 66.4 in April from 74.0 in March. Consumers seem to feel like the job market is slowing too, with fewer consumers saying that jobs are plentiful and more saying that jobs are hard to get.

I try to keep company-funded research in the “research” section vs. using them as economic indicators, but given the consumer negativity coming to the fore, it made more sense to throw in two more consumer survey results here. Numerator research found that consumers are increasing the number of grocery trips they make in a month, traveling to more places in cheaper zip codes in order to check all the boxes of what they want for less. Between March 2023 to February 2024, consumers visited 20.7 different grocery retailers in a month, up 23% over the same period in 2019-2020. AlixPartners additionally found that consumers are buying fewer items at each stop, reinforcing the theory that they’re bargain hunting across multiple retailers, and also found that store brand purchases are up 15% in the 52 weeks ending March 2023 vs. the previous year.

Linda Lisanti, Editor-in-Chief of Convenience Store News, pulled together commentary from NRF and their own research to point out trouble in consumer spending in the convenience store market as well. I would consider convenience store spending to be more discretionary than grocery, or at least more sensitive to consumer pullbacks. And CSN found that the percent of shopper visits to convenience stores dropped 4 points versus a year ago, and the shoppers who said they visit convenience stores “all the time” fell 10 points. This coincides with consumers citing the need for more affordable prices as a must-have for a positive convenience store experience.

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Finally, in trying to understand why consumers are still spending even though they don’t seem to have a lot of discretionary income available, I’ve covered the “wealth effect” in the past – the idea that high home values and stock market portfolios, even though it’s not cash in pocket, make consumers feel like they can spend money. But the latest news focuses in on who exactly has all those assets – and given that we’re on the cusp of one of the greatest wealth transfers in history, from Boomers to whoever they decide to leave it to – it should not be a surprise that it may well be Boomers who are sustaining consumer spending, even to the point of making it more difficult to bring down inflation.

Inflation and high interest rates keep interest-bearing accounts and stock portfolios high, and Boomers are at a point where they’re realizing you can’t take it with you, which means continuing to eat out and take vacations. The article behind this points out that Boomers are not a monolithic generation and there are plenty who are living on Social Security and not driving all this consumer spend, but at the same time, the Fed says people ages 55 and up now own nearly three quarters of all household wealth, up from 68% in 2010.

Retail Tech & Research Data

Returns continue to be a huge headache for retailers, and Retail Brew pulled together (registration wall) pretty much the only strategies that retailers have for trying to minimize the expense of returns. Retailers can:

• Charge for returns - almost 2/3 of retailers in a recent survey charge for returns, and among those almost half (44.5%) have added it in the last year
• Push consumers to use buy online, return in store (BORIS) - often offered as the "free" alternative to shipping returns back to the warehouse, it also has the benefit of driving an incremental trip to the store, with the opportunity for incremental purchases
• Get more items back into inventory - apparel brands typically can only sell 75-80% of what gets returns, usually due to wear or some sign of damage, so if they can improve this, possibly even by looking at the resale market, they can at least recover some of the cost
• Head off returns in the first place – by leveraging fit technologies (those these have not been highly adopted by consumers still), or at least identifying patterns of behavior for specific items and investing more in addressing high return behavior, either by providing more in-depth information, or as Amazon has been doing, even warning consumers that the item is a “high return” item
• Cut their losses – use predictive behavior to determine if you should tell the customer to keep it vs. return it

And that’s pretty much it. Not much else you can do to rein in returns.

I promised a reminder that we’re in the middle of a giant social experiment when it comes to the impact of technology adoption on humanity. The first of two items that help explore this topic is a study released by WebPurify that found children 8 and younger spend an average of 2.48 hours per week shopping online. This is based on interviews with 1,001 parents of kids 18 or younger.

It was actually the most of any of the 3 age groups – more than 9-12 year olds (2.16 hours per week), and more than 13-18 year olds (2.27 hours). 16% of parents interviewed believe their children are “addicted” to online shopping. Almost 20% say their child has bought an item inappropriate for their age while shopping online. But before you think that’s all bad, 19% of parents said their child intervened to prevent them falling for a scam, and more than 1/3 of parents said their kids’ online shopping has taught them money management skills.

On the one hand, this behavior really isn’t that different than my elementary school days of grabbing the Sears catalog and making a shopping list of everything I would buy if I had unlimited funds (dating myself, I know. On so many levels). It’s just that the buy button is a lot closer to hand for today’s kids. And just like you would hand your kid some money and make them pay at the register in a store in order to build life skills, it’s not that far to say that kids need the same experience online these days. And yet there are still open questions to be answered – social commerce, combining the addictive nature of social media with a buy button, might not be good for developing brains, in the same way that schools are starting to seriously consider banning phones outright. We’ve talked in the past about digital natives – I don’t think that term has fully applied until what we’re seeing now with Gen Alpha.

AI & Retail

The other great social experiment is, of course, GenAI. On the consumer side, we’ve seen the rush to try it, but now the numbers are starting to show that consumers have not sustained their adoption over time. Ben Evans starts by saying he falls into that category, that he really hasn’t found a killer use case for GenAI adoption, and then digs into why with an analogy to the difference between operating systems and applications like spreadsheets.

LLM’s like ChatGPT are really more like operating systems, and it may be what gets built on those models is where the problem solving and unlocking of value really happens. Much like you could get an OS to add and subtract with some programming, why bother when a spreadsheet application can do that and so much more besides, it may be the agents and the GPTs that people build on top of LLM’s are what actually unlock value. My favorite quote – one that I constantly remind myself of: “[LLM’s] are now very good at making things that look right, and for some use-cases this is what you want, but others, ‘looks right’ is different to ‘right’.” I would also recommend the article as a great insight into how a very successful VC thinks about how software solves problems. Just in this article alone, it’s a pretty thorough overview of product/market fit in the software world.

Retail Winners and Losers

In more signals that consumers may, finally, be pulling back, Starbucks reported consumer headwinds and lower than expected earnings and revenue in Q2. Same store sales and traffic fell across all regions, and the company lowered its outlook for the full year. CEO Laxman Narasimhan said that consumer spending headwinds were “sharper and more accelerated than we expected”. They’re retrenching, with new drinks coming, and more sugar free options, but expected revenue growth is now forecasted at low single digits vs. the original forecast of 7-10% growth.

Walmart Health is closing its doors in a surprise move that impacts 51 locations in 5 states and a virtual offering. The company cited an “unsustainable business model”, however with the Change Health ransomware attack still impacting providers with slow or non-existent reimbursements, it’s not clear if this is because the business model isn’t working, or if the resolution of Change Health’s problems look distant enough that Walmart is cutting its losses sooner than later. The company mentioned “the challenging reimbursement environment” and “escalating” operating costs. But just a few months ago it had announced expansion plans. Just a good example of making sure that a company’s performance (or lack thereof) is because of economic conditions, or because of their own choices.

Getir has become another case of “is it because of consumers or because of their own choices”. The company is pretty much retreating to its home market of Turkey. It built significant market cap on the promise of grocery deliveries in 10-15 minutes, and at its peak was operating in 5 countries. It acquired FreshDirect from Ahold Delhaize in December 2023 – which was not that long ago – but even before then it had exited Italy, Portugal and Spain and cut its workforce by 10%. FreshDirect will continue its operations in the US as a subsidiary of Getir. The delivery market is the epitome of the challenge of eCommerce. More business does not necessarily mean more scale, and you need a LOT of infrastructure in place before you can even start to contemplate economies of scale. No amount of VC financing can change that basic math.

On the positive side of retail, Nordstrom announced a digital marketplace. I was skeptical at the headline, but more positive about it by the time I got through the article. You wouldn’t think that Nordstrom would really have enough market to be able to sustain a digital marketplace, but we’re not talking about competing against Amazon or Walmart, we’re talking more about luxury brand discovery in a model where Nordstrom leverages its customer base to provide access to up-and-coming boutique brands seeking discovery. Most importantly it is an “unowned inventory model” for Nordstrom. So think of it more like a Retail Media Network designed for boutique luxury brands, rather than a marketplace.

Also in the digital world, Walmart had previously announced that it was making its commerce APIs available to developers in the metaverse, and now it’s proving the point with the “ Walmart Discovered ” experience on Roblox. Users can buy items for in real life delivery, while also acquiring their digital twin for use in Roblox. The experience is being run as a pilot through May, and is just one option for how to bring its commerce API’s to life, as the company has partnered with Unity to expose them to developers for use across 20 different metaverse platforms.

And Amazon is doing just fine, thank you, but look more to the surge in its AWS business as it catches up on the GenAI frenzy, rather than its owned retail operations. Revenue rose 13% to reach an all time quarterly high in Q1 2024. AWS saw sales rise 17% in the first quarter, and operating profit rose nearly 84%. To keep it in perspective, AWS sales contributes about $25B out of the $143B in the quarter. Advertising revenue, helped along by adding ads to Prime, rose 24%, to $11.82B in the quarter.

Amazon also posted some stats about its shipping speeds , setting new records in Q1 after a record-setting year in 2023. Nearly 60% of Prime member orders arrive the same day or next day across the top 60 largest US metro areas. I, for one, wish that when I select the “7-10am” time slot instead of the 4-7am time slot, they would not come early, as it sets my dogs into a frenzy way too early in the morning, but I guess that also goes to show that there’s a difference between “fast” and “in service level”.

Not to be outdone, Walmart announced that it is expanding its delivery services to midnight with the launch of “ultra late-night delivery”. This follows an expansion in March to a 6am start for morning deliveries. Getir is getting out, but Amazon and Walmart – who both have achieved enormous scale – are turning up the pressure on any other competitors out there.

Finally, only because I trashed it so hard not that long ago, I have to mention Poshmark’s foray into live shopping . The company launched the effort in April 2023 but it is now “beginning to take off”. Several boutique brands like Cleobella, Pact, and Rothy’s have given it a try. What I like about this article is that it does a good job covering what these brands actually got out of the experience, and it wasn’t really about sales. In China, an influencer can move 100,000 units of a t-shirt in an hour. The fact that this is so far away from what we see outside of China is part of the reason why expectations seem to be totally divorced from reality. But maybe we’re expecting the wrong things in order to gain traction in the west.

At Poshmark, the focus has been primarily on charity items, or samples. The brand being featured hosts with a brand representative, but the twist is that Poshmark users can also sell their pieces from the brand at the same time. Their videos pop up in a split screen view. The benefits aren’t necessarily in immediate sales, but in breaking through consumer attention to get on the radar in the future. Pact said searches for their brand increased 95% after they hosted an event. Rothy’s held an event just this April, and said that 2,000 people participated. One benefit they had not anticipated from the event was that of having fans talk about the products and bringing up their own ways of showing off the products. The brand learned a lot. And raised money for a charity while they were at it.

Store Innovations

These innovations are all fun and interesting, but they’re not things that capture or enhance the value of the store. So for store innovation this week, the mention goes to Forever 21 and Happy Returns , who have partnered to enable the return of Shein products to Forever 21 stores.

For Happy Returns, this is the first third party retailer who can accept returns outside of its 9000 “return bar” locations. A few key details: Happy Returns is owned by UPS, and Shein is a one-third owner of Sparc Group, which operates Forever 21 stores in North America. As part of that deal, which was inked in August 2023, Forever 21 began selling Shein products in stores, so the whole “third party” angle isn’t as third party as it looks on the surface. There is some integration with Happy Returns to validate the return, which does not require a box or label. Once the item is accepted, the consumer gets a coupon for use in the Forever 21 store. The items go back to a Shein warehouse via UPS parcel. I think if you were looking for evidence that returns can drive store traffic and incremental sales, this might be it, even if these relationships are a lot more cozy than they look on the surface.

The Bottom Line

In December, I covered comments from Dollar Tree about the impact of the loss of pandemic era SNAP benefits – that it took about six months to really show up in consumer spending impacts. I don’t know why it took six months, and Dollar Tree didn’t really know either, only that they had been seeing the impact snowball over about two quarters.

Coincidence or not, it was about six months ago that student loan repayments resumed – and here we are seeing a big dip in consumer confidence. Whether it’s coincidence or causal, I have no idea. But it’s enough of a coincidence that in this retail world of conflicting indicators, it’s worth taking a closer look. But the question still remains – six months after SNAP benefits vaporized, consumer spending seemed very shaky, even though there were as many indicators of consumer confidence as there was a lack of. But in January and beyond, consumers kept spending. It certainly didn’t fall off a cliff.

Here we are six months after student loan payments returned with a vengeance, and consumer spending seems shaky once again. Will consumers shake it off again? There are enough signs to say that they could. But sheesh, who knows at this point.

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