COVID-19 Involvement, Shopping Motives and Buying Behaviour: A German/ South African Comparison

This study aimed to investigate whether consumers’ personal involvement with the COVID-19 pandemic led to hedonic or utilitarian buying motives, and how these buying motives might encourage impulse or planned buying behaviour. Furthermore, it examined whether these influences differed between a developed country (Germany) and a developing country (South Africa). The methodology involved a quantitative, descriptive, cross sectional survey, using a questionnaire based on the literature and sent by e-mail to a quota sample from an online-accessed consumer panel. Useable responses of 548 each from the two countries were analysed, showing that respondents with high levels of involvement with COVID-19 also show high levels of hedonic motivation, whereas utilitarian motivation appeared less important and not linked to a greater involvement with COVID-19. The study also found that a high hedonic motivation is associated with more impulsive shopping, whereas utilitarian motivation is not. The implication is that those with a utilitarian motivation tend towards planned shopping. Finally, the findings show that there appear to be no significant differences between the buying behaviour of consumers in a developing country and a developed country. This study contributed new knowledge about consumer shopping behaviour by examining the interaction of the hedonic/utilitarian construct and the impulsive shopping construct as components of consumer behaviour, research that has not been done before, and especially not in a developing country nor relative to the COVID-19 pandemic.
JEL Classification L81, M30, M31
Full Article


The COVID-19 pandemic resulted in a number of changes throughout the world. One of the areas where these changes were felt was in the retail world, with particular reference to individuals' shopping behaviour (Vinerean, 2020). A number of severe lockdowns had contributed towards a state of panic, accompanied by feelings of fear, as individuals felt threatened by a perceived decline in health and safety levels (Naeem, 2020; Wright and Blackburn 2020). Although one might expect more careful and planned purchasing when being exposed to 'dangerous' shopping experiences or limited shopping trips, impulse purchasing, or panic purchasing driven by uncertainty and fear, has appeared to be more prevalent during the COVID-19 pandemic (Ahmed et al., 2020; Kaur and amp; Sharma, 2020; Harahap et al., 2021; Naeem, 2021). Chenarides et al. (2020) concurred, by observing an increase in the percentage of impulse buying in both the online and traditional shopping environments, as customers scrambled to obtain what they deemed to be essentials, in order to overcome fears of a potential scarcity of goods. Furthermore, research also indicated that consumers have been more focused on shopping for utilitarian type products rather than hedonic products during the pandemic (Yang et al., 2020; Garbe et al., 2020). Research in past pandemics has also indicated that some consumers become very involved in understanding more about the details underlying the crisis, through news and media communication (Qin 2011).

Although COVID-19 has had wide-spread global influence over purchasing behaviour, findings of extant research appear to be inconsistent, indicating a necessity for further research. This study contributes to new knowledge by examining the interaction of hedonic/utilitarian and impulsiveness constructs as components of consumer behaviour, research that has not been done before in developing countries and relative to the COVID-19 pandemic. According to Omar, et al. (2021), COVID-19 has played a significant role in altering consumer behavioural processes, creating many research questions needing to be answered. Ivkovic (2021) and Wright and Blackburn (2020) added that, owing to the length of the pandemic, certain behaviours might become habitual, and continue into the future, while Vinerean (2020) found that consumers would continue with shopping behaviours adopted during the pandemic. This highlights the necessity for marketers to take cognisance of these changes which could influence future business practice.

2. Purpose and Objectives of this Research

The aim of this paper is to investigate consumer involvement with the COVID-19 pandemic, and the role it plays in consumer buying behaviour with respect to hedonic and utilitarian motives and how these might impact on impulse versus planned buying behaviour. It pays homage to the world at large by comparing two different countries, one a first world advanced one (Germany) and the other an emerging economy (South Africa), that have both been affected substantially by the pandemic. The objectives of this study, based on the conceptual framework illustrated in Figure 1, are therefore:

  • Objective 1: To determine whether COVID-19 involvement with influences hedonic shopping motivation
  • Objective 2: To determine whether COVID-19 involvement influences utilitarian shopping motivation
  • Objective 3: To investigate whether hedonic shopping motivation is more likely to result in impulsive buying behaviour.
  • Objective 4: To investigate whether utilitarian shopping motivation is more likely to result in planned buying behaviour during a pandemic.
  • Objective 5: To identify if any differences reflected in Objectives 1 to 4 vary between developing (South Africa) versus developed (Germany) countries.

Figure 1. Conceptual framework

3. Literature Review

The COVID-19 pandemic could be described as a classic scenario where a threatening environment has resulted in consumers changing their methods of obtaining the goods they require. Chenarides et al. (2020) noted that the COVID-19 pandemic highlighted some important aspects of consumer behaviour and how customers respond when faced with feelings of uncertainty and risk.

Despite the recency of the COVID-19 pandemic, a number of studies have taken place investigating numerous aspects of the pandemic, including how it has impacted both marketing and retail environments in various countries. For example, Milakovic (2021) investigated how consumers might behave during a pandemic and found that consumers were still able to carry out effective decision making within the changed environment that they found themselves, with satisfaction still affecting any intention to purchase again. Despite the changes that have taken place, shopping remains essential in many contexts, for example groceries. Ivkovic (2021), amongst others, investigated how consumer behaviour changed in response to the pandemic, suggesting that some of the behavioural changes would become part of the 'new normal'. Finally, Nie et al., (2020) showed an association between a concern for health issues and purchase behaviour, with a significant difference between countries with different levels of economic development (effect size of 0.38).

3.1 Theoretical Grounding

This paper is underpinned by reactance theory which emanates from social psychology. Originally conceptualized by Brehm in 1966, reactance was described as a motivational state that would arise in the case of a perceived loss of freedom, in order to preserve one's freedom of choice (Rodrigues et al., 2019). As a result of this feeling of loss, individuals attempt to restore the status quo by adjusting their behaviour (Lessne and Venkatesan, 1989). Gupta and Gentry (2019) used it when referring to customer decision making when conditions of scarcity were experienced. One of the potential responses could be that of impulsive buying in response to a sense of urgency when acquiring essentials.

In addition, the influence of the COVID-19 pandemic on customer buying behaviour is explained by the stimulus–organism-response (SOR) model, illustrated in Figure 2. This model provides a framework that demonstrates how individuals might react emotionally in response to an environmental stimulus such as a pandemic (Zheng et al., 2020). They used the SOR model to depict the feelings of tension and anxiety that were associated with COVID-19 and other similar epidemics such as SARS and Swine Flu and their ensuing restrictions. Pandita et al. (2021), also used the model to illustrate how these threats can result in changes in human behaviour. The ensuing model is outlined in Figure 2.

Figure 2. The S-O-R Model in the context of COVID-19 and Buying Behaviour
Source: Adapted from Pandita, Mishra and Chib, 2021

3.2 Customer Involvement

Blackwell et al. (2006) define involvement as and ldquo;the level of perceived personal importance and interest evoked by a stimulus within a specific situation”. Customer involvement can change owing to a number of factors such as personal attributes, the product concerned and situational factors. Furthermore, the greater the risk that a consumer perceives, the higher the level of involvement will probably be. Customer involvement is a significant factor in consumer behaviour (Dave and Patel, 2016), playing a role right through from product development through the various stages of behaviour to post-purchase and disposal (Michaelidou and Dibb, 2008). Sharma and Klein (2020) contend that an individual's decision-making style is influenced by their level of involvement, as they adjust the way that they behave to alleviate risk and benefit as much as they can in a given situation.

The higher the level of involvement on a consumer's part, the greater the likelihood that they will make use of complex thought processes when obtaining products (Pucinelli et al., 2009). Customer involvement is also susceptible to social pressures, with customers responding when they feel that they are required to behave in certain ways (Blackwell et al., 2006). Research in a previous pandemic has shown that some consumers become very involved in understanding more about the details of a pandemic, and subsequent vaccination, informed by news and media communication (Qin, 2011). During the COVID-19 pandemic, the use of social media has become critical for keeping consumers personally involved and accurately informed about the pandemic (Vazquez et al., 2020; Naeem, 2021), with media reports also having had an influence on purchase intentions (Koch et al., 2020).

3.3 Hedonic versus Utilitarian Buying Motives

Dey and Srivastav (2017, citing Babin et al., 1994) isolated two main dimensions used to classify shopping motivations, namely hedonic and utilitarian. Hedonic motives can be ascribed to the seeking out of pleasurable experiences, whereas utilitarian buying motives are related to shopping where a certain amount of deliberation takes place assessing the purchase in terms of the benefits that it will provide when compared with the sacrifices that need to be made (both time and financial) (Overby and Lee, 2006; Collins et al., 2014; Koch et al., 2020; Cheng et al., 2018). Utilitarian motives are usually observed when there is a specific goal in mind, resulting in buying behaviour that is carried out in a structured manner (Li et al., 2020; Hashmi et al., 2020). Overby and Lee (2006) add that hedonic motives often serve to drive behaviour when a consumer is seeking entertainment and adventure as part of their shopping experience. Dey and Srivastava (2017) indicated that past studies (for example Babin et al., 1994) suggested that hedonic buying motives can often result in impulsive buying behaviour, suggesting that enquiries into the nature of hedonic buying behaviour could provide further insight into how impulsive purchases take place.

However, Bridges (2020) acknowledges that both hedonic and utilitarian motivations can be important within the same product category. For example, Chowdhury et al. (2018) found that these motives could be clearly described in the selection and consumption of food, with hedonic choices ascribed to the selection of food with respect to taste and appearance, while utilitarian choices are more likely to result in an evaluation of items using terms such as wholesomeness and value. In this case, buying behaviour would more likely be impulsive in the case of the hedonic motive, while a utilitarian motive would be more likely to result in more careful, planned behaviour. According to Yang et al. (2021), utilitarian products are more likely to be purchased to solve a problem, in contrast with hedonic products. Bridges (2020, citing Chiu et al., 2014) states that, as the sense of perceived risk increases, the more utilitarian values diminish, with hedonic values coming to the fore. Ahmed et al. (2020) noted that literature has mainly focused on both utilitarian and hedonic drives, while ignoring external antecedents, such as the atmosphere of fear and hence involvement that has been driven by the pandemic.

However, Yang et al. (2021) suggested that products that rectify a problem or provide a solution would be deemed to be more attractive to a consumer in the face of a crisis such as COVID-19 compared to products that feed the emotions. In the situation of a public health crisis, problem solving becomes paramount. As a result, Yang et al. (2020) stated that the COVID-19 pandemic has resulted in a noticeable increase in the purchasing of utilitarian products on a global basis. During the COVID-19 crisis it would be assumed that additional perceived sacrifices might have to be made in terms of exposing oneself to risk when shopping. Similarly, Lehberger et al. (2021) found that a number of German consumers practiced stockpiling during the pandemic, in order to lower the risk of infection and reduce the time spent wearing masks. Hence, each shopping outing becomes bigger, longer and more planned.

3.4 Impulse versus Planned Buying Behaviour

Xiao and Nicholson (2013) state that impulsive buying emanates from a number of antecedents such as traits related to impulsiveness, sociocultural values and buying beliefs. It is triggered by stimuli that are driven from both internal and external sources, resulting in a sudden desire to buy which is difficult to resist. Impulse buying theory (according to Hawkins Stern in 1962), suggests that, although people are usually expected to behave in a sensible and structured manner, they can revert to impulse buying when exposed to unexpected environmental forces (Naeem, 2020; Ahmed et al., 2020). Factors that drive impulse buying include marketing stimulation, the situation, and impulsivity traits in the individual. In particular, the situational factor describes the personal or social factor that is part of the particular environment in which the consumer finds him or herself. This is moderated by the consumer's judgement of whether impulse buying would be appropriate in the given situation (Chen and Wang, 2016). Impulse buying behaviour is often not seen in a positive light, due to its overriding implication of behaviour that is both risky and not always rational (Mamuaya and Pandowo, 2018; Li et al., 2020).

The understanding of impulse buying behaviour has been widely researched with a number of studies attempting to understand the factors that precede impulse purchases (Hashmi et al., 2020). Dave and Patel (2016) suggested that consumer involvement has a significant effect on buying behaviour in many situations. However, they also comment that impulsive buying behaviour typically is not one of those situations, due to the lack of planning that is part of this buying situation. Ivkovic (2021) states that daily needs such as food, health care products and medical goods are all subject to impulsive buying behaviour. Mamuaya and Padnowo (2018) added that impulsive buying behaviour is often associated with hedonic products, with luxury products consumed for hedonic reasons being purchased impulsively. Vazquez et al. (2020) concurred that impulse buying was a hedonic process, driven by a desire for gratification that was both spontaneous and without much thought for the final result of the purchase. Cornish (2019) analysed consumers' post-impulse-consumption behaviour, highlighting problems such as post purchase regret. Of more significance to this paper, Cornish highlighted the fact that impulse buying contributed towards the success of many retailers, but nevertheless could be detrimental for consumers.

In the case of COVID-19, Ahmed et al. (2020) noted that impulse buying often assisted individuals in coping with and ldquo;negative emotional states” induced by the pandemic. Wang et al. (2020) posited that self-control theory suggested that self-control is external and factors such as COVID-19 can cause personal resources to be exhausted, which could result in self-control waning, creating a greater tendency to engage in impulsive purchases.

3.5 Buying Behaviour and the COVID-19 pandemic

Koch et al. (2020) investigated shopping motivation with respect to online purchase behaviour in Germany during the COVID-19 pandemic, finding that hedonic motivation was the strongest predictor of intention to purchase in an online apparel scenario, followed by utilitarian and normative motives. However, this study did not extend to other formats or merchandise offerings.

Dey and Srivastava (2017) investigated how situational characteristics for consumers could have an effect on hedonic motivations, thereby driving impulse buying behaviour. Ahmed et al. (2020, citing Leverin and Liljander, 2006) highlighted that and ldquo;conscious (planned) or subconscious (impulsive) purchase patterns are driven mainly by hedonic (emotional) and utilitarian (practical) stimuli”. Many studies show that hedonic and utilitarian buying motives serve mainly as moderators or mediators (Ahmed et al., 2020, citing Koparal and Calik, 2015 and Ha and Abbasi, 2016), while others suggest that these are independent variables that have a direct effect on buying behaviour.

Mehta et al. (2020) found that consumers, in an effort to avoid risk in a crisis situation, often reverted to less complex products that offered value, stating that the acquirement of basics was more likely to be planned. They suggested that the purchasing of less essential items diminished, (in particular those that were more likely to fall into the hedonic category), causing concern for retailers in that sector. In contrast, Li et al. (2020) found that impulsive buying behaviour is related to how threatening the pandemic was perceived to be in a country, with Schmidt et al. (2021) describing how concerns of German consumers related to falling ill and not having access to food, resulted in changes in shopping behaviour with consumers shopping less often and then stockpiling. Lehberger et al. (2021) noted that in the early days of the pandemic, German consumers dramatically increased their purchases of non-perishable foods, which remained above average.

Ahmed et al. (2020) concurred that fear of a lockdown and the resulting panic buying had a strong influence on buying behaviour. Along with the resultant observations of friends and associates buying, scarcity and perceptions of scarcity and supply chain disruptions, impulse buying increased dramatically. Although more careful and planned purchasing could be expected when being exposed to 'dangerous' shopping experiences, or limited shopping trips, impulse purchasing, or panic purchasing driven by uncertainty and fear, has appeared to be more prevalent during the COVID-19 pandemic than careful, planned shopping trips (Ahmed et al., 2020; Kaur and amp; Sharma, 2020; Harahap et al., 2021; Naeem, 2021). Naeem (2020) reported that, according to Slickdeals, the average American's impulse buying increased by about 18% when compared to figures before the pandemic. However, although most extant research implies increased impulse purchasing, there do appear to be situations where impulse buying decreased (Scacchi et al., 2021), justifying further research into this construct.

In addition, many instances of panic buying, along with hoarding of purchases were observed, to the extent that more than 50% of shoppers revealed that they had participated in such behaviour (Verma and Naveen, 2021). A number of studies were conducted on buying behaviour in Germany with both Schmidt et al. (2021) and Lehberger et al. (2021) studying stockpiling, which is closely associated with panic buying, both of which result from perceptions of the risks involved in accessing food during Covid lockdowns. Vinerean (2020) also noted panic buying of essentials by people who became nervous and worried. The work of Wang et al. (2020) could be extended to the behaviour observed during COVID-19 where impulse buying could have been ascribed to consumer involvement in the pandemic, driven by information and the avalanche of word of mouth information which heightened fears and hence excessive impulsive buying behaviour. Due to this, the loss of self-control described previously may be driven by the influence of others and the depletion of personal resources. Ahmed et al. (2020) concurred, finding that and ldquo;fear of a complete lockdown, peers' buying, scarcity of essential goods on shelves, the limited supply of essential goods and panic buying” all contributed towards an unprecedented increase in impulse buying. Wang et al. (2020) confirmed how others can influence impulsivity with personal goals changing in line with personal influences. Naeem (2020) noted that there is a lack of understanding as to why there has been an increase in impulse buying during the fear appeals generated by the health sector, despite this not being the planned objective of such appeals. However, Naeem (2020) noted that intention to buy was related to fears of catching the virus, out-of-stock situations, price increases and other risks associated with COVID-19. This further motivates the importance of understanding how impulse buying is exacerbated in an environment such as that created by the COVID-19 pandemic, so that it can be managed where necessary.

Ahmed et al. (2020) suggest that insufficient studies have considered external influences and how they might affect buying motives and behaviour. Yang et al. (2020), found that COVID-19 involvement was more significantly linked to utilitarian products in contrast with hedonic products, questioning why an event such as the COVID-19 pandemic or similar could result in consumers tending towards utilitarian products rather than hedonic ones. Moreover, Ahmed et al. (2020) found that impulse buying behaviour was mediated by the COVID-19 pandemic as individuals responded with fear and panic, often driven by excessive social media reports which may or may not have been true, but were certainly exaggerated. Ivkovic (2021) found that consumers' perceptions of how severe a pandemic was had an impact on impulsive consumption, with both perceived control and materialism playing a role. If perceived control was enhanced or materialism was diminished, consumers were less impulsive. However, this finding only applied to online shopping.

The significance of any studies done during the COVID-19 period is that they suggest how individuals might behave in similar circumstances in the future (Chenarides et al., 2020). Wright and Blackburn (2020) add that any investigations of this nature will assist retailers in planning for the future. Ross (2021) highlights the value of studies that analyse behavioural factors related to COVID-19 reiterating that the virus is not going to disappear in the near future and as such will need to be factored into future consumer behaviour theory. 

4. Research Methodology

This study adopted a quantitative, descriptive, cross sectional survey, with a questionnaire e-mailed to quota samples in the two countries, namely Germany and South Africa.

4.1 Respondents

For Germany, all consumers aged 18+ formed the study population, as the German Gini coefficient of 31.7 indicates a wide spread of wealth (The World Bank Group 2019). Therefore, most of the population are able to shop for both hedonic and utilitarian products, in both an impulsive and planned manner. Thus, an income, gender, and age (18+) based quota was set for the German sample. For South Africa, the Living Standards Measure (LSM) categories 5-10 (mainly urbanised) were selected as the study population (Chronison, 2012). South Africa's high Gini coefficient (63.0 in 2015) (The World Bank Group, 2019) indicates that only a small proportion of the South African population would be able to buy hedonic products, with most of the lower LSM consumers living a hand-to-mouth existence. Such consumers would probably not be able to purchase impulsively, but would tend to buy only essential products. Furthermore, LSMs 1-4 are 80-100% rural, having very low incomes, and thus contributing little to consumer purchasing. Therefore, LSM groups 5-10 were chosen as the sample for this study.

LSMs have been criticised as being out-dated, partly due to the demographic shift up the LSM categories as consumers become wealthier, and for various technical reasons (Langschmidt, 2017). The ES Socio-Economic Measures (SEM) have been suggested as a replacement (Reidon, 2018), but Muller (2017) shows that SEM and LSM use the same defining variables, and the only real differences appear at the lower end (i.e., LSMs 1-4). Since this study is targeting the more urbanised and wealthier consumers (LSMs 5-10), this difference at the lower end of the market is irrelevant. Furthermore, SEMs are so new that databases of respondents are not yet readily available, while a database of LSM 5-10 respondents was available. Therefore, it was decided to use LSMs rather than the newer SEMs.

An online accessed panel that met the above sample criteria for both Germany and South Africa was sourced with representative quotas as above being guaranteed by the research company. The quotas for LSMs 5-10 were adjusted to cater for the demographic changes in South African over the past 30 years (KANTAR TNS, 2019). The segment proportions from the SEM categories, instead of the LSM proportions, were applied to identify the number of respondents required for each of LSMs 5 to 10. The result is a quota profile as presented in Table 1.

Table 1. Calculation of sample quota and achieved sample

  LSM 5 LSM 6 LSM 7 LSM 8 LSM 9 LSM 10 Total
LSM % of SA population 22 34 11 5 6 3 81
SEM % of SA population 10 9 9 7 6 7 48
SEM % applied to LSM 5-10 20.8 18.8 18.8 14.6 12.4 14.6 100%
Result: n of 5-10 sample 104 94 94 73 62 73 500
Actual sample achieved 114 104 104 79 68 79 548
% of sample achieved 20.8 19.0 19.0 14.4 12.4 14.4 100%

The final samples achieved (see Tables 1 and 3) were similar to the real population and so were accepted as representative of the two populations.

Since the respondents were effectively a self-selected sample (i.e., members decide themselves whether to participate or not), based on quotas, the sampling was non-probability. Such self-selection sampling could result in selection bias or non-response error (Bless et al., 2013), but Table 3 shows a reasonable spread of respondents, which indicates such bias or error as an unlikely problem. 

Based on a 95% level of significance, allowed error of 0,1 (for a 7-point Likert type scale) and an assumed variance of 1, a t-distribution needs a sample size of 384 (excluding a correction factor) (Sekaran and Bougie, 2013). However, a sample size of 550 for each country was set to cater for any unusable or incomplete responses. The size of the actual, useable sample achieved was 548 for each country.

4.2 Data Collection

The questionnaire was developed from the relevant literature, covering the 'involvement' variable and the hedonic/utilitarian and impulsiveness variables. Seven point Likert-type scaled responses, with 1 = strongly disagree to 7 = strongly agree, were developed from the academic literature for each construct as shown in Table 2. Also included in the questionnaire were the demographic characteristics of the sample.

The questionnaire was pilot tested for face validity with twelve South African consumers who matched the population criteria. This resulted in some changes to words, phrasing and spelling and the addition of some explanations in the introduction. The questionnaire was then translated into German and checked by the German researcher to ensure translational equivalence (Hair et al., 2003), and then pilot tested with twelve German consumers, resulting in a few wording and phrasing changes. Thereafter the questionnaire was pre-tested with 54 German and 58 South African consumers selected according to the same sample criteria. No changes were needed as the respondents found the questionnaire to be easy to understand and complete. The e-mail, with a link to the questionnaire was supplied to the research company who then sent the questionnaire out to the opt-in panel between 10 and 16 June 2021. The benefits of using an opt-in panel are that the pre-set quotas can be achieved by addressing specific members of the target population, and that the cost is relatively low.

The disadvantages, of multiple participation, self-selection bias and practice bias, were avoided by each panel member only receiving one invitation and the software not allowing multiple participations. The invitation to participate did not mention the topic so as to avoid self-selection bias and practice bias was avoided by each respondent only being allowed by the list broker to participate in research once every two months (George, 2010). The socio-demographic characteristics, both for the German panel and for the South African panel based on the LSM status, are updated once a year.

Table 2. Questionnaire derivation

Construct Questions Source
COVID-19 involvement I actively follow the progress of COVID-19 in the daily press, TV, social media, etc Zaichkowsky, 1985
I often browse the Internet, news channels or the press for information on COVID-19 Qin et al., 2011
While watching news of COVID-19 on TV, I use a cell phone or tablet to learn more about COVID-19 Vazquez et al., 2020
I often talk about COVID-19 with my family and friends Qin et al., 2011
COVID-19 is an important part of, and impacts on, my current life Mital, 1995; Qin et al., 2011
Someone close to me (family, friend, colleague) has, or has had, COVID-19 Houston and amp; Rothschild, 1978
Understanding how COVID-19 is developing and effecting society is very important to me Laurent and amp; Kapferer, 1985
I perceive that COVID-19 can have a considerable negative risk to me
Hedonic I like to shop for the novelty of it, for example, being exposed to exciting new products Yim et al., 2014, based on Hausman, 2000
Shopping satisfies my sense of curiosity
Shopping offers me new experiences
I feel like I am exploring new worlds when I shop
I get a real and ldquo;high” from shopping
Utilitarian On my shopping trips, I accomplish just what I want to Haas and amp; Kenning, 2014, based on Babin et al., 1994
When I go shopping, I buy just the item(s) that I am looking for
I only go shopping when I need something
Impulsive I often spend more than what I can afford Sharma et al., 2014, based on Sharma et al., 2011 and amp; Rook and amp; Fisher, 1995
I like to indulge myself by buying things for pleasure
I lose self-control quite frequently
I often act without thinking about the consequences
I seldom plan anything in advance
I often make decisions spontaneously

4.3 Data Analysis

The researchers received 550 German and 585 South African (total of 1134) completed questionnaires, which were analysed using SPSS version 25. A quality or plausibility check of the data was first done to identify any obviously poor responses, e.g., insufficient data for categorisation, contradictory responses, insufficient time spent on doing the questionnaire. There were 38 respondents who violated at least one of these criteria and so were eliminated, leaving 548 South African and 548 German valid responses.

Then univariate descriptive statistics, in the form of means and standard deviations, were used to compare the various constructs, with t-tests to assess significance. Regression analysis was used to identify relationships between the variables and Cohen's D test was used to assess effect size. Analyses were done by country and total with mean values and standard deviations being shown in Table 6.

4.4 Validity and Reliability

All questions were compared with their relevant variables to assess content validity, which was further supported by the questionnaire having been based on previous validated questionnaires, as illustrated in Table 2 (Bless et al., 2013). Furthermore, subject matter and statistical experts conducted a detailed deconstruction and analysis of the questionnaire, followed by a pilot test with 24 people who matched the population criteria. This provided face and construct validity. Then a live, electronic pre-test showed no significant changes to be necessary. Quality and plausibility checks were done on the full data set, and the final sample was reasonably representative of the German and South African populations. Finally, Table 5 shows Cronbach Coefficient Alphas of 0.7, or very close to 0.7, indicating internal consistency of the questionnaire (Sekaran and amp; Bougie, 2013).

5. Results

In this section, the sample profile is presented, followed by the descriptive statistics for each question, and an analysis of the three research questions.

5.1. Profile of Sample Demographics

Table 3 indicates the useable responses received from the sample, according to the six demographic categories.

Table 3. Demographic profile of respondents

Dimension Category Total South Africa Germany
f % f % f %
Gender Female 602 54.9 320 58.4 282 51.5
Male 494 45.1 228 41.6 266 48.5
Age 18-24 135 12.3 86 15.7 49 8.9
25-34 337 30.7 253 46.2 84 15.3
35-49 281 25.6 160 29.2 121 22.1
50-59 190 17.3 39 7.1 151 27.6
60+ 153 14 10 1.8 143 26.1
Habitation Metro (250 000 +) 423 38.6 261 47.6 162 29.6
City/large town (40000-249 999) 349 31.8 194 35.4 155 28.3
Small town/village (5000-39999) 218 19.9 74 13.5 144 26.3
Rural (< 5000 people) 106 9.7 19 3.5 87 15.9
Education None, some, or all primary 97 8.9 2 0.4 95 17.3
Some high school 235 21.4 38 6.9 197 35.9
High school/Matric 282 25.7 165 30.1 117 21.4
Technikon 189 17.2 123 22.4 66 12
University degree 282 25.7 211 38.5 71 13
Other post matric 11 1 9 1.6 2 0.4
Monthly Household net income 0 and ndash; R8 000/ 0-€1300 219 20 113 20.6 106 19.3
R8 001 and ndash; 18 000/€1300-2000 298 27.2 179 32.7 119 21.7
R18 001 and ndash; 37 000/€2001-3200 315 28.7 156 28.5 159 29
R37001 and ndash; 63 000/€3201-6000 206 18.8 73 13.3 133 24.3
More than R63000/€6000 58 5.3 27 4.9 31 5.7
Total   1096 100 548 100 548 100

This profile shows an acceptable distribution of the sample for both Germany and South Africa, for all six demographic categories. Since the German sample was based on German population proportions, the German sample is naturally representative. The South African sample reflects the LSM groups as shown in Table 1, but is slightly different to the South African population proportions. There are slightly more females (58.4%) in the sample than in the population (51%) (StatsSA, 2020). LSMs 6, 7, 8 and 9 are biased towards females (Living Standards Measure, 2017) and shopping tends to be more often done by females in emerging countries, as shown by the fact that females account for 59% of mall shoppers in South Africa (Docrat, 2007).

5.2 Factor Analysis

Exploratory factor analysis was used to check that the constructs and questions, as shown in Table 2, are accurate representations of the issues being examined in this study. Table 4 gives the results of this EFA. The statements representing each construct loaded perfectly, showing that they measured what they were supposed to measure, and are consistent with the constructs identified in the extant literature.

Table 4. Exploratory factor analysis

COVID 19 involvement I actively follow the progress of COVID-19 in the
daily press, TV, social media, etc.
0.741 -0.038 0.062 -0.002
I often browse the Internet, news channels or the
press for information on COVID-19
0.796 0.135 0.151 0.008
While watching news of COVID-19 on TV, I sometimes simultaneously
use a cell phone or tablet to learn more about COVID-19
0.673 0.246 0.194 0.014
I often talk about COVID-19 with my family and
0.784 0.059 0.094 -0.018
COVID-19 is an important part of, and impacts on, my current
0.726 0.064 0.104 0.088
There are people close to me (family, friends, colleagues)
who have, or have had, COVID-19
0.469 0.228 0.082 0.077
Understanding how COVID-19 is developing and effecting
society is important to me
0.789 -0.016 0.149 -0.013
I perceive COVID-19 can have a considerable negative
risk to me
0.663 0.071 0.122 0.055
Hedonic I like to shop for the novelty of it, for example,
being exposed to exciting new products
0.139 0.175 0.748 0.014
Shopping satisfies my sense of curiosity 0.154 0.176 0.838 -0.004
Shopping offers me new experiences 0.196 0.151 0.849 0.020
I feel like I am exploring new worlds when I shop 0.153 0.192 0.808 0.054
I get a feeling of euphoria from shopping 0.142 0.272 0.685 -0.005
Utilitarian On my shopping trips, I accomplish just
what I want to
0.108 -0.075 0.242 0.665
When I go shopping, I buy just the item(s) that
I am looking for
0.001 -0.027 -0.038 0.840
I only go shopping when I need something 0.045 -0.059 -0.108 0.815
Impulsiveness I often spend more than what I can afford 0.119 0.812 0.111 0.019
I like to indulge myself by buying things
for pleasure
0.119 0.578 0.340 -0.157
I often don’t have much restraint or
self-discipline when shopping
0.077 0.804 0.146 -0.114
I often act without thinking about the
0.047 0.837 0.113 -0.025
I seldom plan anything in advance 0.112 0.638 0.167 0.087
I often make decisions spontaneously 0.116 0.644 0.229 -0.108

Note: NB: Extraction method: principal component analysis. Rotation method: varimax with Kaiser normalization. Rotation converged in 6 iterations.

5.3 Reliability

Cronbach's Coefficient Alpha was used to assess reliability - a coefficient of 0.7 or higher was considered reliable (Sekaran and Bougie, 2013). As shown in Table 5, coefficients above, or very close to, 0.7 were obtained for all the dimensions. Therefore, the instrument was accepted as reliable.

Table 5. Reliability of constructs

Construct Cronbach's Coefficient Alpha
COVID-19 involvement 0.870
Hedonic motivation 0.887
Utilitarian motivation 0.695
Impulsiveness 0.855

5.4 Descriptive Statistics

Means and standard deviations for each variable construct and the associated statements, for Germany, South Africa and the total sample, are provided in Table 6.

Table 6. Descriptive statistics (N: SA = 548; Germany = 548; Total = 1096)

Construct Mean Std Dev Statements Country Mean Std. Dev Total mean
COVID-19 involvement SA SA I actively follow the progress of COVID-19 in
the daily press, TV, social media, etc.
SA 5.57 1.655 5.39
5.54 1.256 GER 5.21 1.687
    I often browse the Internet, news channels or the
press for information on COVID-19
SA 5.48 1.7 4.93
Ger Ger GER 4.37 1.9
4.45 1.283 While watching news of COVID-19 on TV, I sometimes simultaneously
use a cell phone or tablet to learn more about COVID
SA 5.21 1.809 4.51
    GER 3.81 2.1
Total Total I often talk about COVID-19 with my family and
SA 5.78 1.502 5.36
4.99 1.382 GER 4.94 1.712
    COVID-19 is an important part of, and impacts on,
my current life
SA 5.67 1.623 5.14
GER 4.61 1.763
There are people close to me (family, friends, colleagues)
who have, or have had, COVID-19
SA 4.99 2.329 4.15
GER 3.3 2.431
Understanding how COVID-19 is developing and effecting
society is important to me
SA 6 1.446 5.53
GER 5.07 1.702
I perceive COVID-19 can have a considerable negative
risk to me
SA 5.64 1.622 4.95
GER 4.26 1.816
Hedonic SA SA I like to shop for the novelty of it, for example,
being exposed to exciting new products
SA 5.09 2.002 4.7
5.08 1.532 GER 4.32 2.057
    Shopping satisfies my sense of curiosity SA 5.25 1.841 4.62
Ger Ger GER 3.98 2.071
3.89 1.712 Shopping offers me new experiences SA 5.52 1.735 4.86
    GER 4.19 1.996
Total Total I feel like I am exploring new worlds when I shop SA 5.12 1.929 4.36
4.48 1.728 GER 3.61 2.074
    I get a feeling of euphoria from shopping SA 4.4 2.089 3.88
GER 3.37 2.103
Utilitarian SA SA On my shopping trips, I accomplish just what
I want to
SA 5.46 1.732 5.16
5.18 1.45 GER 4.86 1.926
Ger Ger When I go shopping, I buy just the item(s)
that I am looking for
SA 4.86 2.063 4.68
4.66 1.645 GER 4.49 2.006
Total Total I only go shopping when I need something SA 5.22 1.999 4.92
4.92 1.571 GER 4.63 2.115
Impulsive SA SA I often spend more than what I can afford SA 3.69 2.162 3.18
3.85 1.531 GER 2.67 1.796
    I like to indulge myself by buying things
for pleasure
SA 4.38 1.941 4.13
Ger Ger GER 3.89 1.782
3.23 1.37 I often don’t have much restraint or
self-discipline when shopping
SA 3.37 2.048 3.15
    GER 2.93 1.816
Total Total I often act without thinking about the
SA 3.2 2.119 2.99
3.54 1.485 GER 2.77 1.772
    I seldom plan anything in advance SA 4.07 2.138 3.59
GER 3.11 1.823
I often make decisions spontaneously SA 4.37 1.867 4.17
GER 3.97 1.71

The statistics presented in Table 6 show that, for all the constructs and for all their comprising statements, South Africa scored higher than Germany. In most cases the majority of respondents agreed with the statements providing scores above the 7-point Likert scale's mid-point of 4. This indicates a relatively high involvement with COVID-19, although many of the German respondent's means were below the mid-point of 4. The South African respondents scored highly for both 'hedonic' and 'utilitarian' motivation, but the German respondents scored considerably higher for 'hedonic' motivation than for 'utilitarian' motivation. Respondents from both countries scored below the midpoint of 4 on 'impulsive', implying that many consumers are not impulsive in their shopping behaviour, and tend to use a planned approach to shopping.

To assess the achievement of objectives 1 and 2, bi-variate analyses of 'involvement' with COVID-19 and 'hedonic' and 'utilitarian' shopping motivations were conducted as shown in Table 7. Participants who scored 3.5 or less for each construct are characterized as and ldquo;low”, participants scoring 4.5 or higher as and ldquo;high”. Those scoring neither high nor low were not considered to be eligible for either category.

Table 7. Relationship between 'involvement with COVID-19' and 'hedonic'/'utilitarian'

Motivation type Involvement level N Mean SD t-test equal of means Cohen's D effect size Regression
t df Sig r2 beta
Hedonic High 740 4.85 1.662 10.07 895 0.000 0.89 14.9% 0.48a
Low 157 3.36 1.743 9.77 220 0.000
Utilitarian High 740 5.02 1.567 0.28 895 0.779 0,03 1% 0.117
Low 157 4.99 1.580 0.28 225,8 0.780

5.5 Objective 1: COVID-19 Involvement's Influence on Hedonic Motivation

Table 7 shows that there is a significant difference in the level of 'hedonic' shopping motivation between those with high levels of 'COVID-19 involvement' and those with low levels, with a mean difference of 1.4846. In other words, those showing high levels of involvement tend to also show high levels of hedonic motivation, with a strong effect size (Cohen's D of 0.885). In addition, a regression analysis indicated that 14.9% (adjusted r2) of the variance in 'hedonic' can be explained by 'COVID-19 involvement', with a relatively strong influence and ndash; a beta of 0.484 (both highly significant). In other words, for each point that 'COVID-19 involvement' increases, 'hedonic' increases by 0.484. Regarding the individual countries, the regressions and beta coefficients for both countries were highly significant and ndash; for Germany, a very low adjusted r2 of 0,097 and a medium beta of 0.314, and for South Africa, a very low adjusted r2 of 0.069 and a medium beta of 0.325). Therefore, it can be concluded that 'involvement with COVID-19' can have a significant influence on the consumers' 'hedonic' shopping motivations, with results for the two countries being similar, but with Germany reflecting a slightly lower level of 'hedonic' shopping motivation.

5.6 Objective 2: COVID-19 Involvement's Influence on Utilitarian Motivation

Table 7 also shows that there is no significant difference between high and low 'involvement' for 'utilitarian' shopping behaviour, with a mean difference of only 0.0387 and very low effect sizes of 0.08 for South Africa and 0.16 for Germany. This indicates that those with both high and low levels of 'involvement' score much the same on 'utilitarian' shopping motivation. A regression analysis indicated that only 1% of variance in 'utilitarian' motivation can be explained by 'COVID-19 involvement' (a low beta of 0. 0.117). In other words, for each point that 'COVID-19 involvement' increases, 'utilitarian' only increases by 0. 0.117. The results for South Africa indicated a highly significant but very low r2 of 0.012 with a low beta of 0.134, while the German results were not significant with a r2 of 0.0 and beta of -0.03, i.e., a non-existent influence.

Thus, it can be concluded that level of 'involvement with COVID-19' does not influence consumers' 'utilitarian' shopping motivations.

5.7 Objective 3: Hedonic Motivation Results in Impulsive Buying Behaviour

As shown in Table 8, the overall influence of 'hedonic' shopping motivation on 'impulsive' purchasing is medium to high - respondents with low 'hedonic' motivation show lower 'impulsiveness' (mean = 2.66) than respondents with high 'hedonic' motivation ('impulse' mean = 4.04). In other words, those with lower 'hedonic' motivation tend not to buy 'impulsively' but can be said to buy more on a planned basis. This relationship accounts for 22% of the variability in 'impulsiveness' (low to medium r2 = 0.22; medium beta = 0.404), and reflects a highly significant difference in the effect of high versus low 'hedonic' motivation, with very high effect size of 0.977.

Table 8. Relationship between 'hedonic'/'utilitarian' and 'impulsive'

Buying behaviour Motivation type/level N Mean SD t-test equal of means Cohen's D effect size Regression
t df Sig r2 beta
Impulsive Lo hedonic 311 2.66 1.269 -13,9 894 0.000 0.98 22% 0.40
Hi hedonic 585 4.04 1.481
Lo utilitarian 214 3.82 1.252 3.078 883 0.002 0.24 0,7% -0.09
Hi utilitarian 671 3.44 1.621

Regarding the two countries, the results are very similar with both showing a higher 'impulse' associated with a higher 'hedonic' motivation (4.14 versus 2,92 for South Africa and 3.85 versus 2.56 for Germany). The regression analysis showed highly significant low to medium r2 (0.156 for South Africa and 0.224 for Germany) and medium betas of 0.397 (South Africa) and 0.380 (Germany). These results were all highly significant with very high effect sizes (0.810 for South Africa and 0.997 for Germany).

Therefore, it can be concluded that a 'hedonic' shopping motivation has a definite influence on how 'impulsively' people shop, although it is clear that the German respondents are less 'impulsive' than the South African respondents.

5.8 Objective 4: Utilitarian Motivation Results in Less Impulsive Buying Behaviour

Table 8 shows that low 'utilitarian' motivation results in higher 'impulsive' behaviour (mean = 3.82) than higher 'utilitarian' motivation does (mean = 3.44). Although these results are significant, the r2 of 0.007 and a beta value of -0.086 are very low, as is the effect size of 0.240.

Regarding the two countries, South Africa with a non-existent r2 of 0.0 and beta of 0.7, no significant difference and a very low effect size of 0.113, shows that there is no influence of 'utilitarian' motivation on 'impulse' shopping. German results are a little different with a very low r2 of 0.068 and a beta of -0.264, significant differences and medium effect size of 0.563.

This indicates that the overall influence of 'utilitarian' motivation on 'impulsive' purchasing does not exist or is quite low, but that in Germany this low influence is negative. In other words, a higher 'utilitarian' motivation leads to lower 'impulse' purchasing, although this must be viewed in terms of the low r2 and the medium effect size.

5.9 Objective 5: Do the Differences Reflected in Objectives 1 to 4 vary between Developing (South Africa) Versus Developed (Germany) Countries

Table 9 shows that the South African respondents scored higher than the German respondents on all the variables. All respondents scored above the midpoint score (4) for 'COVID-19 involvement', 'hedonic' and 'utilitarian', but below the midpoint for 'impulsive'. Table 9 also shows that the effect size for these findings was high for 'involvement', medium-high for 'hedonic', low-medium for 'impulsive' and low for 'utilitarian', as per the Cohen's D coefficients. All these findings were highly statistically significant.

Table 9. Comparison of four variables by country (n = 548 for each country = 1096)

Country Involvement ** Hedonism ** Utilitarianism ** Impulsiveness **
Germany Mean 4.4455 3.8942 4.6600 3.2251
Std. Dev 1.28269 1.71170 1.64531 1.36950
South Africa Mean 5.5422 5.0755 5.1794 3.8495
Std. Dev 1.25582 1.53236 1.44955 1.53137
Cohen's D effect size High Medium-high Low Low-medium

**Significant at 0.01; 1 = low to 7 = high; Assumption for buying: low = planned - high = impulsive)

As per the findings discussed for the four objectives above, it also shown that:

- Germany has a slightly lower level of 'hedonic' shopping motivation than South Africa,

- although there is a slight difference for 'utilitarian' motive this is not significant as there is no, or a very low, relationship with 'involvement',

- in both countries there is a 'hedonic' motivation which encourages 'impulsive' shopping, although the German respondents were slightly less 'impulsive' than the South Africans

- the relationship between 'utilitarian' motivation and 'impulsive' shopping is either non-existent for South Africa or very low for Germany. Although the German relationship is negative, it is so slight as to not be worth considering.

6. Discussion

A review of the findings from this study is presented below, along with a discussion on how these results compare with those of previous studies, which mostly took place in first world countries. This discussion is structured according to the five objectives.

6.1 Objective 1 and ndash; Does Involvement in COVID-19 Influence Hedonic Motivation?

The greater the risk in the environment, the greater the involvement by consumers (Blackwell et al. 2006), and their decision making and behaviour changes to cater for risky environment. This is supported by our results which show that higher involvement with COVID-19 is associated with greater hedonic shopping behaviour, but not with greater utilitarian behaviour. Thus, the extreme risk perceived from the pandemic leads consumers to shop less deliberately and in a less structured manner (Li et al., 2020), e.g., not buying according to a pre-identified shopping list, but following the emotions and observations experienced while in the shop, i.e., hedonic shopping. Furthermore, during the pandemic, with the 'lock downs' and lack of social contacts, shopping may have become one of the few relatively pleasurable activities, and so consumers have viewed it as not just a utilitarian activity, but a pleasurable, hedonic one. Thus, it does seem probable that this study's findings support the literature's contention that increased involvement with COVID-19 leads to a more hedonic shopping motivation.

6.2 Objective 2 and ndash; Does Involvement in COVID-19 Influence Utilitarian Motivation?

Yang et al. (2021) imply that utilitarian products are more likely to be bought during the pandemic but do not say anything about the effect of the pandemic on whether the utilitarian shopping motive would be more prevalent or not. The work of Overby and Lee (2006), Collins et al. (2014) and Li et al. (2020) imply utilitarian motives would be more prevalent in a situation like the COVID-19 pandemic, i.e., more goal oriented, problem solving, structured purchasing. Although Mehta et al. (2020) suggested the purchasing of less essential items in a risky environment, the fact that consumers reverted to less complex products that offered value in such an environment could imply that they are more likely to use an emotional and less structured buying process, i.e., a hedonistic motivation, which is what was found in this study. Thus, this study does not support the literature that suggests that increased involvement with COVID-19 leads to increased utilitarian shopping motivation.

6.3 Objective 3 and ndash; Are Hedonic Motives Likely to Result in Impulse Buying?

Li et al. (2020) suggest that risky situations or environments tend to cause consumers to shop less deliberately and in a less structured manner e.g., buy what they see rather than what is on a shopping list, which is typical of impulse shopping. This was supported by Dey and Srivastava (2017) who indicated that past studies (for example Babin et al., 1994) suggested that hedonic buying motives often result in impulsive buying behaviour. This relationship between hedonic motivation and impulse purchasing was also supported by Ahmed et al. 2020. The findings from this study support this relationship as suggested by most of the literature.

6.4 Objective 4 and ndash; Are Utilitarian Motives Likely to Result in Impulse Buying?

According to Li et al. (2020) utilitarian motives exist in structured purchasing to meet a specific goal. This implies a conscious planned approach to buying and not impulsive purchasing behaviour. This is supported by Overby and Lee (2006) and Collins et al. (2014) who see utilitarian motives as involving deliberation and weighing up benefits against sacrifices. Although more utilitarian products are being bought during the pandemic (Yang et al., 2020), the literature does not support utilitarian motivation as leading to more impulsive purchasing. In this study there was found to be virtually no relation between utilitarian motivation and impulse purchasing, thus supporting the literature.

6.5 Objective 5 and ndash; Do Objective 1-4 Results Vary Between Countries?

Since there is so little literature comparing developed against developing nations in terms of the variables under study, it is difficult to come to any conclusions other than those found by this study which differ from the association between health issues and level of economic development found by Nie et al. (2021). This study found that the shopping motives and behaviour and the relationships between these variables are much the same in the German and South African contexts. The only differences are that the German respondents had slightly lower levels of hedonic motivation and impulsive shopping behaviour. Any other differences were not statistically significant and so slight as to be irrelevant. Thus, it can be concluded that consumers in both developed and developing countries may react similarly when shopping under the COVID-19 pandemic context, but that this requires further research.

7. Conclusion, Contribution, Recommendations, Limitations, and Further Research

7.1 Conclusion

The study's findings for objective 1 have shown that respondents with high levels of involvement with COVID-19 also show high levels of hedonic motivation, possibly following emotions more, with shopping being seen as one of the few available pleasurable activities. Regarding objective 2, the opposite was found, with utilitarian motivation being less important and not linked to a greater involvement with COVID-19. Regarding objectives 3 and 4, the study found that a high hedonistic motivation is associated with more impulsive shopping, but utilitarian motivation is not at all linked to impulsive shopping. The implication is that those with a utilitarian motivation tend towards planned shopping, typically buying according to a shopping list. Finally, the findings for objective 5 show that there is no significant difference between the findings for the German and South African respondents.

7.2 Recommendations for Managers

Since this study has shown that shopping has become one of the few relatively pleasurable activities available to consumers during 'lock downs' and 'social distancing', and that involvement with COVID-19 leads to more hedonic motivations, it is suggested that marketers and retailers institute marketing and merchandising actions that promote pleasurable emotional and observational experiences that will strengthen the hedonic experience in the shop. Such actions could include:

  • point of sale material highlighting products that could make 'lock down' more pleasurable,
  • train staff to be more friendly and welcoming, especially those who control the entrance to stores or sanitise arriving customers
  • introduce more attractions such as product demonstrations or tasting,
  • inclusion of socially distanced, safe meeting places such as coffee bars.

Because COVID-19 and hedonic motivations encourage impulse shopping, retailers must put special effort into display dominance, layouts that make it easy to progress through the whole shop and to view all the products, as well as regular up-dating of aisle displays, gondola end displays and product displays at the tills to encourage and facilitate impulse shopping. No significant differences were identified between the shopping motivations and buying behaviour of German and South African consumers, so the suggested marketing actions can be used for both countries.

7.3 Contribution

This study has contributed to new knowledge by providing a better understanding of the interaction of hedonic/utilitarian and impulsiveness constructs as components of consumer behaviour. Although these findings support Omar, et al. (2021), who found that COVID-19 has been influential in altering consumer behavioural processes in Malaysia, our findings also add to the body of knowledge as little research had been done in developing countries, and especially in developed versus developing countries. Furthermore, this research is the first to be done in South Africa, and the first to investigate hedonic versus utilitarian shopping motivations relative to the COVID-19 pandemic. The findings are important because, as indicated by Ivkovic (2021), Wright and Blackburn (2020) and Vinerean (2020), there is a risk that new consumer behaviours adopted during the pandemic might become habitual, and continue into the future. Therefore, research such as this is essential to understand such changes which could influence future marketing practice. This study will hopefully instigate a further stream of research into consumer motivation during contexts such as COVID-19.

7.4 Limitations and Further Research

Although every care was taken to minimise limitations, there are some issues that researchers need to be aware of when reading this paper or attempting a replication study. First, since the research was conducted only in South Africa and Germany, care should be taken if attempting to extrapolate to consumers or shoppers in other countries. Second, the South African sample was limited to only LSMs 5-10. Although approximately 80% of consumers and potential consumers fall within LSMs 5-10 this may be changing as lower LSMs become more urbanised and wealthier and aspire to the purchase behavioural characteristics of the higher LSM categories. However, the majority of LSM 1-4 consumers are probably still in survival mode or focus their purchases on essential, utilitarian products, and do not have the purchasing power to do much in the way of impulsive or hedonic purchasing. Thus, it is unlikely that excluding LSMs 1-4 will have provided significantly biased results. Nevertheless, a further study into LSMs 1-4 purchasing patterns during the COVID-19 pandemic would be worthwhile to confirm this assumption. Third, other possible attributes or factors that could influence impulse purchasing may exist, so a qualitative study is needed to identify any such factors, as well as to better understand those factors identified by this study.


Author Contributions: Karen M. Corbishley: Conceptualization, Literature review, Writing of original draft, Writing review and editing. Roger B. Mason: Conceptualization, Questionnaire and methodology development, Writing of original draft, Writing review and editing, Visualization, Supervision and project administration. Thomas Dobbelstein: Funding acquisition, Conceptualization, Software development, Data curation, Data analysis and interpretation, Validation.

Acknowledgements: The authors would like to thank the funder for their generous support and to thank the respondents for providing the raw material for this research.

Funding: This research was funded by Baden-Württemberg Cooperative State University, Ravensburg, Germany.

Conflicts of Interest: The authors state that they have no conflicts of interest.

  1. Ahmed, R.R., Streimikiene, D., Rolle, J. and Duc, P.A., 2020. The Covid-19 pandemic and the antecedents for the Impulse Buying Behavior of US Citizens. Journal of Competitiveness, 12(3), pp. 5-27.
  2. Blackwell, R.D., Miniard, P.W. and Engel, J.F., 2006. Consumer Behavior. 10th ed. Mason, OH: Thomson/South-Western.
  3. Bless, C., Higson-Smith, C. and Sithole, S.L., 2013. Fundamentals of Social Research Methods: An African Perspective. 5th ed. Cape Town: Juta.
  4. Bridges, E., 2020. Hedonic and utilitarian shopping goals: a decade later. Journal of Global Scholars of Marketing Science, 28(3), pp. 282-290.
  5. Chen, Y. and Wang, R., 2016. Are humans rational? Exploring factors influencing impulse buying intention and continuous impulse buying intention. Journal of Consumer Behaviour, 15, pp. 186-197.
  6. Chenarides, L., Grebitus, C., Lusk, J. and Printeziz, I., 2020. Food Consumption behavior during the COVID-19 pandemic. Agribusiness, 2021(37), pp. 44-81.
  7. Cheng, Z., Chang, C. and Lee, Y., 2020. Linking hedonic and utilitarian shopping values to consumer skepticism and green consumption: the roles of environmental involvement and locus of control. Review of Managerial Science, 14, pp. 61-85.
  8. Chowdhury, T.G., Murshed, F. and Khare, A., 2018. Flexible flexibility! Food categorization flexibility and utilitarian preference. Journal of Consumer Marketing, 35(1), pp. 1-10.
  9. Chronison, M., 2012. Dissecting the LSM 7-10 market. 18 December. [online] Available at: https://themediaonline.co.za/2012/12/dissecting-the-lsm-7-10-market/ [Accessed on 6 June 2019].
  10. Collins, A., Kavanagh, E., Cronin, J. and George, R., 2014. Money, mavens, time, and price search: Modeling the joint creation of utilitarian and hedonic value in grocery shopping. Journal of Marketing Management, 30(7-8), pp. 719-746.
  11. Cornish, L.S., 2019. Why did I buy this? Consumers’ post-impulse-consumption experience and its impact on the propensity for future impulse buying behavior. Journal of Consumer Behaviour, 19, pp. 36-46.
  12. Dave, D.R. and Patel, B.M., 2016. Impulsive Buying Behaviour in organized Retail stores – with specific reference to FMCGs in Gujarat. Prestige Journal of Management and Research, 8/9(2/1), pp. 21-27.
  13. Dey, D.P. and Srivastava, A., 2017. Impulse buying intentions of young consumers from a hedonic shopping perspective. Journal of Indian Business Research, 9(4), pp. 266-282.
  14. Docrat, S.H., 2007. An investigation of shopping centres as situational influences on consumer behaviour in the greater Durban area. Management Dissertation. University of KwaZulu-Natal, South Africa.
  15. Garbe, L., Rau, R. and Toppe, T., 2020. Influence of perceived threat of Covid-19 and HEXACO personality traits on toilet paper stockpiling. PLoS ONE, 15(6), e0234232. https://doi.org/10.1371/journal.pone.0234232
  16. George, K., 2010. Benefits and Concerns of Panels. RMS Research & Marketing Strategies Inc. [online] Available at: https://rmsresults.com/2010/09/29/benefits-and-concerns-of-panels-market-research-agency-in-syracuse-ny/ [Accessed on 1 March 2021].
  17. Gupta, S. and Gentry, J.W., 2019. ‘Should I Buy, Hoard or Hide?’ – Consumers’ responses to perceived scarcity. The International Review of Retail, Distribution and Consumer Research, 29(2), pp.178-197.
  18. Haas, A. and Kenning, P., 2014. Utilitarian and Hedonic Motivators of Shoppers’ Decision to Consult with Salespeople. Journal of Retailing, 90(3), pp. 428-441. https://doi.org/10.1016/j.jretai.2014.05.003.
  19. Hair, J.F. Jr., Babin, B., Money, A.H. & Smouel, P., 2003. Essentials of Business Research Methods. Hoboken NJ: Wiley.
  20. Harahap, D.A., Ferine, K.F., Irawati, N., Nurlaila and Amanah, D., 2021. Emerging Advances in E-Commerce: Panic and Impulse Buying During the Covid-19 Pandemic. Systematic Reviews in Pharmacy, 12(3), pp. 224-230. http://hdl.handle.net/123456789/29974.
  21. Hasmi, H.B.A., Shu, C. and Haider, S.W., 2020. Moderating effect of hedonism on store environment-impulse buying nexus. International Journal of Retail and Distribution Management, 48(5), pp. 465-483.
  22. Houston, M.J. and Rothschild, M.L., 1978. Conceptual and methodological perspectives on involvement. In Research Frontiers in Marketing: Dialogues and Directions, ed. S. Jain, Chicago: American Marketing Association, pp. 184-187.
  23. Ivkovic, N., 2021. Beyond the pandemic – a new era of consumer behavior. 65th International Scientific Conference on Economic and Social Development – Online Conference, 19 February, pp. 6-17.
  24. Kantar TNS., 2019. The Establishment Survey. The Broadcast Research Council of South Africa. [online] Available at: https://secureservercdn.net/ [accessed 9 July 2019].
  25. Kaur, R. and Sharma, B., 2020. Impulsive behavior for essential goods: COVID-19. Journal of Critical Reviews, 7(8), pp. 3126-3135.
  26. Koch, J., Frommeyer, B. and Schewe, G., 2020. Online Shopping Motives during the COVID-19 Pandemic – Lessons from the Crisis. Sustainability, 12(24), 10247, pp. 1-20. https://doi.org/10.3390/su122410247
  27. Langschmidt, P., 2017. ‘New SEM socio-economic segmentation tool explained’, Strategic Marketing, 2, pp. 30-34. [online] Available at: https://www.prc.za.com/wp-content/uploads/2017/08/New-SEM-socio-economic-segmentation-tool-explained.pdf [Accessed on 6 July 2019].
  28. Laurent, G. and Kapferer, J-N., 1985. Measuring Consumer Involvement Profiles. Journal of Marketing Research, 22(1), pp. 41-53. https://doi.org/10.2307/3151549.
  29. Lehberger, M., Kleih, A. and Sparke, K., 2021. Panic Buying in times of coronavirus (COVID-19): Extending the theory of planned behavior to understand the stockpiling of non-perishable food in Germany. Appetite, 161, pp. 1-8.
  30. Lessne, G. and Venkatesan, M., 1989. Reactance Theory in Consumer Research: The Past, Present and Future. Advances in Consumer Research, 16(1), pp. 76-78.
  31. Li, J., Abbasi, A., Cheema, A. and Abraham, L.B., 2020. Path to Purpose? How online customer journeys differ for Hedonic versus Utilitarian purchases. Journal of Marketing, 84(4), pp. 127-146.
  32. Living Standards Measure., 2017. South African Audience Research Foundation. [online] Available at: http://www.saarf.co.za/lsm/lsms.asp [Accessed on 6 June 2019].
  33. Mehta, S., Saxena, T. and Purohit, N., 2020. The new Consumer Behaviour paradigm amid COVID-19: Permanent or transient?. Journal of Health Management, 22(2), pp. 291-301.
  34. Mamuaya, N.C.I. and Pandowo, A., 2018. The effect of situational factor, store atmosphere, ad sales promotion on hedonic shopping motivation and its implication on supermarket consumer impulsive buying in Manado city. Journal of Business and Retail Management Research, 13(2), pp. 1-11.
  35. Michaelidou,N. and Dibb, S., 2008. Consumer involvement: A new perspective. The Marketing Review, 8(1), pp. 83-99. https://doi.org/10.1362/146934708X290403
  36. Milaković, I.K., 2021. Purchase experience during the COVID-19 pandemic and social cognitive theory: The relevance of consumer vulnerability, resilience, and adaptability for purchase satisfaction and repurchase. International Journal of Consumer Studies. 45, pp. 1425-1442.
  37. Mittal, B., 1995. A comparative analysis of four scales of consumer involvement. Psychology & Marketing, 12(7), pp. 663-682. https://doi.org/10.1002/mar.4220120708.
  38. Muller, G., 2017. ‘Demystifying SEM with me and Bobby McGee’. The Media Online. [online] Available at: https://themediaonline.co.za/2017/05/sem-me-and-bobby-mcgee/ [Accessed on 6 July 2019].
  39. Naeem, M., 2020. Understanding the customer psychology of impulse buying during COVID-19 pandemic: implications for retailers. International Journal of Retail and Distribution Management, 49(3), pp. 377-393.
  40. Nie, W., Medina-Lara, A., Williams, H. and Smith, R., 2021. Do Health, Environmental and Ethical Concerns Affect Purchasing Behavior? A Meta-Analysis and Narrative Review. Social Sciences, 10, pp. 413. https://doi.org/10.3390/socsci10110413
  41. Omar, N.A., Nazri, M.A., Ali, M.H. and Alam, S.S., 2021. The panic buying behavior of consumers during the COVID-19 pandemic: Examining the influences of uncertainty, perceptions of severity, perceptions of scarcity, and anxiety. Journal of Retailing and Consumer Services, 62, pp. 1-12. https://doi.org/10.1016/j.jretconser.2021.102600
  42. Overby J.W. and Lee E., 2006. The effects of utilitarian and hedonic online shopping value on consumer preference and intentions. Journal of Business Research, 59(10-11), pp. 1160-1166.
  43. Pandita, S., Mishra, H.G. and Chib, S., 2021. Psychological impact of Covid-19 crisis on students through the lens of Stimulus-Organism-Response (SOR) model. Children and Youth Services Review, 120, pp. 1-9. https://doi.org/10.1016/j.childyouth.2020.105783
  44. Puccinelli, N.M., Goodstein, R.C., Grewal, D., Price, R., Raghubir, P. and Stewart, D., 2009. Customer Experience Management in Retailing: Understanding the Buying Process. Journal of Retailing, 85(1), pp.15-30.
  45. Qin, X., Niu, C., Huang, Z. L., and Xu, M.Y., 2011. The familiarity of influenza A (H1N1), perception of vaccine safety, vaccination behaviour and their influential mechanism. Acta Psychologica Sinica, 43, pp. 684–695. https://doi.org/10.3724/SP.J.1041.2011.00684.
  46. Reidon, B., 2018. Getting to know the ES SEMs [online] Available at:  https://themediaonline.co.za/2018/02/getting-to-know-the-es-sems-8-10-part-1/[Accessed on 6 July 2019].
  47. Rodrigues, G.V., Soares, E.D., Loes, E.V. and De Lamônica Freire, O.B.L., 2019. Reactance Theory: A Bibliometric Review. UNIMEP Management Magazine, 17(3), pp. 47-69.
  48. Ross, J., 2021. Pandemic research ‘must prioritise human and political behaviour. Times Higher Education. [online]Available at: https://www.timeshighereducation.com/news/pandemic-research-must-prioritise-human-and-political-behaviour [Accessed on 12 July 2021].
  49. Scacchi, A., Catozzi, D., Boietti. E., Bert. F. and Siliquini, R., 2021. COVID-19 Lockdown and Self-Perceived Changes of Food Choice, Waste, Impulse Buying and Their Determinants in Italy: QuarantEat, a Cross-Sectional Study. Foods, 10(2), pp.306. https://doi.org/10.3390/foods10020306
  50. Sekaran, U. and Bougie, R., 2013. Research Methods for Business: A Skill-Building Approach. 6th ed. Chichester: Wiley.
  51. Schmidt, S., Benke, C. and Pané-Farré, 2021. A. Purchasing under threat: Changes in shopping patterns during the COVID-19 pandemic. PLoS ONE, 16(6), pp. 1-15.
  52. Sharma, P., Sivakumaran, B. and Marshall, R., 2014. Exploring impulse buying in services: toward an integrative framework. Journal of the Academy of Marketing Science, 42, pp. 154–170. https://doi.org/10.1007/s11747-013-0346-5
  53. Sharma, V.M. and Klein, A., 2020. Consumer perceived value, involvement, trust, susceptibility to interpersonal influence, and intention to participate in online group buying. Journal of Retailing and Consumer Services, 52(2), pp. 1-11.
  54. StatsSA., 2020. People indicators. [online] Available at:  http://www.statssa.gov.za/?page_id=593 [Accessed on 2 Nov. 2020].
  55. The World Bank Group, 2019. GINI index (World Bank estimate) [online] Available at: https://data.worldbank.org/indicator/SI.POV.GINI?locations=CO [Accessed on 9 July 2019].
  56. Vazquez, D., Wu, X., Nguyen, B., Kent, A., Gutierrez, A. and Chen, T., 2020. Investigating narrative involvement, parasocial interactions, and impulse buying behaviours within a second screen social commerce context. International Journal of Information Management, 53, pp. 1-12.
  57. Verma, M. and Naveen, B.R., 2021. COVID-19 Impact on Buying Behaviour. The Journal for Decision Makers, 46(1), pp. 27-40.
  58. Vinerean, S., 2020. Understanding Consumers’ Online Shopping Behavior during the COVID-19 Pandemic – Empirical Research. Expert Journal of Marketing, 8(2), pp. 140-150.
  59. Wang, Y., Lu, H. and Wang, D., 2020. Buy or not: how the presence of others affects the occurrence of consumers’ impulsive buying behavior. Journal of Contemporary Marketing Science, 3(2), pp.207-224.
  60. Wright, O. and Blackburn, E., 2020. How COVID-19 will permanently change consumer behavior: Fast-changing consumer behaviors influence the future of the CPG industry. Accenture [online] Available at: https://www.accenture.com/_acnmedia/PDF-123/Accenture-COVID19-Pulse-Survey-Research-PoV.pdf [Accessed on 26 April 2021].
  61. Xiao, S.H. and Nicholson, M., 2013. A Multidisciplinary Cognitive Behavioural Framework of Impulse Buying: A Systematic Review of the Literature. International Journal of Management Reviews, 15, pp. 333-356.
  62. Yang, Y., Li, O. Peng, X. and Wang, L., 2020. Consumption Trends during the Covid-19 crisis: How awe, coping, and social norms drive Utilitarian purchases. Frontiers in Psychology, 11, pp. 1-10. https://doi.org/10.3389/fpsyg.2020.588580.
  63. Yim, M.Y-C. Yoo, S-C., Sauer, P. and Seo, J.H., 2014. Hedonic Shopping Motivation and Co-shopper Influence on Utilitarian Grocery Shopping in Superstores. Journal of the Academy of Marketing Science, 42(5), pp. 528-544. DOI: 10.1007/s11747-013-0357-2
  64. Zaichkowsky, J.L., 1985. Measuring the Involvement Construct. Journal of Consumer Research, 12(3), pp.341-52.
  65. Zheng, L., Miao, M., Lim, J., Li, M., Nie, S. and Zhang, Z., 2020. Is lockdown bad for Social Anxiety in COVID-19 regions? A National Study in the SOR perspective. International Journal of Environmental Research and Public Health, 17(4561), pp.1-12.

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© 2022 The Authors. Published by Sprint Investify. ISSN 2359-7712. This article is licensed under a Creative Commons Attribution 4.0 International License. Creative Commons License
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Roger B. Mason, Durban University of Technology – Department of Marketing, Retail, South Africa, ORCID 0000-0001-7927-1767
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Durban University of Technology, South Africa

Roger B. MASON
Durban University of Technology, South Africa

Durban University of Technology, South Africa
Baden-Württemberg Cooperative State University, Germany