Abstract
Purpose
This paper aims to examine the presence of impulsive and compulsive buying among consumers. It studies the various factors that affect and moderate the impulsiveness and compulsiveness of buying.
Design/methodology/approach
Literature review resulted in four constructs – social media influence, social media preferences, hedonic motivation and shop in COVID-19. On conducting factor analysis in statistical package for the social sciences, the variables were divided under the influence of social media, social commerce, electronic word of mouth (EWOM) of social commerce, hedonic happiness, hedonic fun and shopping in times of COVID-19. Structural equation modeling is conducted in AMOS (statistical software) for a diagrammatic representation of the relationship between the variables. Regression analysis is used to re-affirm the above relationship. Testing of hypotheses is done with the help of the chi-square test.
Findings
All six latent variables are significantly related to impulsive and compulsive buying. However, the regression analysis shows social media influence as the strongest predictor for impulse buying and hedonic happiness for compulsive buying. Also, the presence of the pandemic COVID-19 leads to impulsive buying as well as compulsive buying in the apparel and accessory segment.
Practical implications
Marketers should capitalize on spontaneous buying in both forms – impulsive buying and compulsive buying. Social media influencers, as well as more consumer engagement on social media, can promote impulsive buying. However, compulsive buyers will be more attracted towards great in-store experiences or hedonically driven advertisements, as they do not just shop for buying the product; they shop for the experience of shopping.
Originality/value
This study uncovers the difference in factors that affect impulsive and compulsive buying. Though both behaviours seem points of the same scale, they are inherently different and can be predicted with social media influence and hedonic happiness.
Keywords
Citation
Kshatriya, K. and Shah, P.S. (2023), "A study of the prevalence of impulsive and compulsive buying among consumers in the apparel and accessories market", Vilakshan - XIMB Journal of Management, Vol. 20 No. 1, pp. 2-24. https://doi.org/10.1108/XJM-12-2020-0252
Publisher
:Emerald Publishing Limited
Copyright © 2021, Kavita Kshatriya and Priyanka Sharad Shah.
License
Published in Vilakshan – XIMB Journal of Management. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode
Introduction
The Indian consumer has transitioned from being the cautious consumer to the indulgent one. Shopping has become a means of self-fulfillment. Consumers look for products that satisfy their wants and desires rather than needs. The increase in spending on non-necessities is seen across industries (Livemint: YouGov, 2019). Retailers and manufacturers are offered a paradoxical market of a modern western mindset of the consumer, along with the underlying Indianism (Mittal et al., 2016). There is much admiration for the lifestyle of western economically developed countries. As a result, materialism is justified (Gupta, 2011). With the radical transformation in the marketscape, tremendous increase in affluence, accessibility and easier modes of payment (Pradhan et al., 2018) fueled with rising disposable income; impulse buying would be more comfortable.
Literature review
Impulsive buying behaviour was introduced as a lifestyle trait, which involves materialism, sensation seeking and recreational aspects of shopping (Rook, 1987). It was further improvised as a personality trait comprising a spontaneous urge to buy immediately with disregard to consequences equating it to a toddler’s candy tantrum (Rook and Fisher, 1995). It is associated with both positive and negative feelings (Youn and Faber, 2000). Research on impulse buying has been based on varying conceptual definitions and has focussed primarily on in-store retailing (Madhavaram and Laverie, 2004). The Indian online, as well as the offline retail market, can provide a lot of scope for encouraging impulsive and compulsive buying (Bhakat and Muruganantham, 2013).
The advent of the internet makes the digital presence of brands inevitable. Global segments of online shoppers have been developed (Aljukhadar and Senecal, 2011). Little has been studied on the importance of social commerce as a tool for reshaping marketing techniques (Zhou et al., 2013). Social commerce has been created with the popularity of social networking sites (Hajli, 2015). The electronic word of mouth, along with customer-generated reviews, affects the decision-making of the consumer (Krishnamurthy and Kumar, 2018; Prasad et al., 2016). Social networks can have a significant impact on impulse buying (Aragoncillo and Orus, 2018). Online impulse buying is supported by a host of encouraging factors (Akram et al., 2018). Influencer marketing the internet micro-celebrities are changing the meaning of marketing communication (Jiménez-Castillo and Sánchez-Fernández, 2019). Today, much time is spent on social networking sites in India (Statistica Global Consumer Survey, 2019); hence, individuals who have a fear of missing out by viewing other experiences show a tendency to act impulsively and thus engage in impulse purchase (Çelik et al., 2019).
People buy to shop, not shop to buy (Langrehr, 1991). Shopping is no longer considered a task; it is mood altering and hedonic in nature (Arnold and Reynolds, 2003). A shift in the traditional cultural values towards consumerism (Yu and Bastin, 2010) can foster impulsive and compulsive buying. Hedonic shopping value differs across product categories. As it is more emotional in nature than utilitarian shopping value, it could be closely associated with impulsive and compulsive buying (Santini et al., 2019).
Compulsive buying is defined as addictive shopping behaviour, where the customer is unable to significantly moderate (Faber and O’Guinn, 1989). A social comparison could lead to compulsive buying tendency (Kukar-Kinney et al., 2016). Compulsive buying is not just a stronger version of impulsive buying (Pradhan et al., 2018). It is supported by low self-esteem, internet addiction, loneliness and anxiety. It is also used as a mechanism of negative coping (Zheng et al., 2020).
The unprecedented times of the pandemic COVID-19 have brought to light new aspects of shopping behaviour. Staying at home with daily information overload coupled with daily perceived uncertainty leads to spontaneous buying (Xian et al., 2020).“Revenge shopping” (Jamal, 2020) is seen in many Asian countries when lockdowns are lifted. Companies need to accept the change, for now, build networks and strategize for the next phase and transform all their business operations around attracting the customer once again for the beyond phase(COVID-19 Pandemic Radically Changing Consumer Behaviour In India: EY Survey, 2020). Retailers will have to aim for a seamless online–offline experience (Tandon, 2021). Online presence has become imperative for the smallest of brands (Ingaldi and Brozova, 2020; Jamunadevi et al., 2021). With prior behaviour no longer an indicator, a share of the consumers’ pocket is up for grabs (Tandon and Shuchi, 2020).
Rationale of the study
Based on the literature review, online shopping, social commerce and the hedonic motivation of shopping emerge as the most associable factors to impulsive and compulsive buying. Impulsive purchases account for a huge volume of products sold every year globally (Hausman, 2000). Retailers should try to augment impulsive buying behaviour (Kau et al., 2003). However, cultural differences shape developed and developing economies differently. The current Indian retail environment has a lot of scope for impulsive and compulsive purchases in India (Bhakat and Muruganantham, 2013). However, insufficient studies have been conducted on how retailers can augment impulsive and compulsive buying (Amos et al., 2014) and the moderating effect of demographic variables on impulsive and compulsive buying as well as the prevalence of the same in the Indian market. The pandemic COVID-19 has changed and will further transform the way shopping can happen. It has also accelerated the use of digital platforms in all sectors, which in turn may see the milestone of “digital billion” much before 2030 as forecasted earlier in the pre-COVID era (Positives of the Pandemic, 2020). It is imperative to understand the prevalence of impulsive and compulsive buying to further engage the customer with traditional and alternate channels of marketing. Social networking-enabled shopping (Zhou et al., 2013) can allow better leveraging of spontaneous purchases.
Research objective
To study the prevalence of impulsive and compulsive shopping among consumers.
Scope
To study the impulsive and compulsive shopping behaviour of consumers in the apparel and accessories market (online as well offline).
Research questions
What is the role of social commerce in promoting impulsive and compulsive shopping?
Does the hedonic motivation of shopping promote impulsive and compulsive shopping?
The impact of COVID-19 on impulsive and compulsive shopping.
Hypotheses development
In accordance with the research questions, the following hypotheses are developed:
Social media influence is significantly associated with impulsive buying.
Social media influence is significantly associated with compulsive buying.
Social media preferences are significantly associated with impulsive buying.
Social media preferences are significantly associated with compulsive buying.
Hedonic motivation is significantly associated with impulsive buying.
Hedonic motivation is significantly associated with compulsive buying.
Shopping in the times of COVID-19 is significantly associated with impulsive buying.
Shopping in the times of COVID-19 is significantly associated with compulsive buying.
Research methodology
Quantitative research methods have been used for the purpose of this study, involving the use of statistical procedures for analysis (Onwuegbuzie and Leech, 2005). A close-ended questionnaire (Rossi and AB, 1983) was used for data collection with some previously proven constructs of social media influence, hedonism, impulsive buying and compulsive buying, as well as shopping in times of COVID-19 developed by the researcher. A questionnaire is a reliable instrument that is simple to administer, and an extensive amount of data can be generated in a cost- and resource-effective manner. The anonymity and confidentiality of the respondent are also respected (Welman and Kruger, 1999). The physical absence of the researcher also leads to non-biased responses. The apprehension of being judged by others would lead to Social Desirability Response (Mittal et al., 2018).
The method used for data collection was an online distribution of the questionnaire via google forms through social media channels, considering the restriction on movement because of COVID-19 efficiency and economic feasibility. This method also allowed accessibility to a larger sample and made it easier to collect and compile data (Metzner and Mann, 1952). The target population selected was online and offline shoppers above the age of 18 in Ahmedabad. A non-probability convenience sampling technique is used to collect data (Takona, 2002). Respondents were selected based on accessibility. However, because of certain categorical questions, judgement was used in selecting the final data. A total of 200 respondents were approached, out of which 146 questionnaires are completely filled and are valid.
Statistical package for the social sciences (SPSS) version 23 was used to analyse the collected data. The data was appropriately coded and questions that were negative in nature were appropriately reverse coded. Also, disguised questions were appropriately calculated with the related variable. All such variables are included in (Table 1).
The data was collected on a five-point Likert scale of agreement where 1 is strongly agree and 5 is strongly disagree. Based on their overall mean score, respondents were classified on whether their mean score was above or below 3.
Statistical analysis
Descriptive statistics (Tables 2–4).
A test reliability of scale to measure the consistency of the scale (Table 5).
Followed by factor analysis with principal component analysis (PCA) method to find the latent variables (Tables 6–9).
Structural equation modeling (SEM) in AMOS (statistical software) – SPSS is used for a diagrammatic representation of the relationship between variables (Figure 1, Tables 10–13).
Regression analysis is further used to measure the relationship between predictor variables and dependent variables (Tables 14–21).
Instrument
A questionnaire is formulated using various sub-scales of impulse buying, social commerce, compulsive buying and hedonic motivation of shopping as well as shopping in the times of COVID-19. Each construct is referenced with classic papers in the area of consumer behaviour as shown in Table 1. Some of the questions are developed by the researcher based on the unique situation created by COVID-19.
The questionnaire is created in Google forms with multiple response grids for the Likert scale, where 1 = strongly agree, 2 = agree, 3 = neutral, 4 = disagree and 5 = strongly disagree. Questions falling under the same construct are put together. However, some questions are interchanged and reversed to get an unbiased response.
Data collection
The data is collected over a three-day period in the month of September 2020. Social media networks were used for getting the respondents to participate in the questionnaire (Wadhera and Sharma, 2019). Respondents were approached in accordance with the research methodology. A total of 200 respondents were approached, out of which 146 participated. All of them were valid with no missing fields.
Respondent characteristics
Table 2 shows the demographic profile of the respondents. They are mainly from the age group 30–39 (48%). These are the older millennials. About 75% were females, 85% married and 53.4% are postgraduates. About 53.4% of the respondents have a monthly family income of more than 200,000 and 38.4% are self-employed. These categories have relatively more engagement in shopping in general. The younger millennials (25–29 years) and Gen Z (less than 25 years) are 16.6%. The older millennials (30–39 years) have a higher income and as a result a higher spending capacity.
Descriptive statistics
The mean values of items used in the scale are illustrated in Tables 3 and 4. Table 3 contains the means values of four variables, namely, social media influence, social media preferences, hedonism and shopping in the time of COVID. Table 4 shows the mean values of impulsive and compulsive buying.
Reliability test
Straub (1989) states that constructs reliability shows the internal consistency of the scale items measuring the same construct for the data. Cronbach’s alpha is used to measure the reliability of the scale. Cronbach’s alpha was calculated for each construct. Here, the Cronbach’s alpha is 0.951, which is above the recommended value of 0.7 reflecting reliability of the scale as shown in Table 5. Thus, the measurement shows good reliability.
Factor analysis
Kaiser-Meyer-Olkin (KMO) measures the sampling adequacy, which should be close to 0.5 for a satisfactory factor analysis to proceed (Kaiser, 1974). It determines whether the responses given with the sample are adequate or not. A value of 0.5 is considered acceptable, 0.7–0.8 is considerate acceptable and above 0.9 is considered as outstanding. To test the sampling adequacy, KMO test was carried out and the resultant value is 0.871 as shown in Table 6. This is way above the recommended value of 0.5 and closer to outstanding value of 0.9. Thus, it can be considered as acceptable.
To remove the redundant variables and uncover the latent variables, all the 28 variables of factors influencing impulsive and compulsive buying are treated with PCA to identify closely related variables. Out of the 28 variables, six latent variables emerged on rotation of the variables using varimax method as shown in Table 8. This is done to make the interpretation of the analysis easier. Factor analysis shows that 66.87% of the total variance can be explained by classifying 28 variables into six components or factors as shown in Table 7. Only the variables with eigenvalue of more than 1 are accepted in the study. The six components are further named as shown in Table 8.
The model is an over-identified model with df being 1. The goodness of fit indices are acceptable (chi square = 8.007, p value = 0.005, root mean square error of approximation = 0.22, goodness of fit index = 0.987, normed fit index = 0.987, comparative fit index = 0.988).
Social media influence, social commerce, social commerce-electronic word of mouth (EWOM), hedonic happiness, hedonic fun and shop in COVID-19 are exogenous variables that predict the endogenous variables impulsive buying and compulsive buying. Error variables e1 and e2 are unique variables that could affect the endogenous variables. The predictor variables can predict the dependent variables up to 45% for impulsive buying, whereas 59% for compulsive buying as shown in Figure 1. Both values are above 30%, hence are considered acceptable.
Maximum likelihood estimates
For the endogenous variable impulsive buying, the most important predictors are social media influence (0.52) and shopping in COVID-19 (0.515) with significance levels of less that 0.005 as given in Tables 10 and 11.
For the endogenous variable compulsive buying, the most important predictors are hedonic happiness (0.38) and shopping in COVID-19 (0.348) at significance levels of less than 0.005 as given in Tables 10 and 11.
All the values (p ≥ 0.05) are acceptable, so there exists co-variance between all exogenous variables as shown in Table 12. Hence, social media influence, social commerce, EWOM of social commerce, hedonic happiness, hedonic fun and shopping in COVID-19 reflect co-variance among each other (Table 13).
All Pearson’s correlations values are above the recommended value of 0.3, hence, independent variables (social media influence, social commerce, EWOM of social commerce, hedonic happiness, hedonic fun and shop in COVID-19) and dependent variable (impulsive buying) are correlated to each other as shown in Table 14.
Regression analysis
Regression analysis is used to test the significance and the relationship between dependent and independent variables. The model summary shows R = 0.667 and R2 = 0.445 as given in Table 15 . This shows dependent variable impulsive buying can be explained by the two factors by 66%. It also means social media influence and shop in COVID-19 contribute significantly and predict 44.5% of the variation in impulsive buying.
The F test states that the regression model predicts the outcome significantly as shown in Table 16. The level of significance is 0.000, which means the model can predict impulsive buying.
The t-values should be df − 2, which is 4 in this case. Table 17 shows social media influence (t = 5.749) and shop in COVID-19 (t = 3.9), which is almost 4. Social media influence and shop in COVID-19 have emerged as the strongest predictors for impulsive buying. This can also be re-affirmed with SEM shown in Figure 1.
All Pearson’s correlations values are above the recommended value of 0.3, hence, independent variables (social media influence, social commerce, EWOM of social commerce, hedonic happiness, hedonic fun and shop in COVID-19) and dependent variable (impulsive buying) are correlated to each other as shown in Table 18.
For all correlations above 0.3, endogenous and exogenous are correlated and independent and dependent are correlated.
Regression analysis is used to test the significance and the relationship between dependent and independent variables. The model summary shows R = 0.769 and R2 = 0.592 as shown in Table 19. This shows dependent variable compulsive buying can be explained by the two factors by 76%. It also means hedonic happiness and shop in COVID-19 contribute significantly and predict 59.2% of the variation in impulsive buying.
The F test states that the regression model predicts the outcome significantly as shown in Table 20. The level of significance is 0.000, which means the model can predict compulsive buying.
The t-values should be df − 2, which is 4 in this case. Table 21 shows hedonic happiness (t = 4.5) and shop in COVID-19 (t = 6.7). Both values are above the recommended value of 4. Hence, hedonic happiness and shop in COVID have emerged as the strongest predictors for compulsive buying. This can also be re-affirmed with SEM as shown in Figure 1.
Hypotheses testing
To test the hypotheses, chi-square test is conducted as well as phi and Crammer’s V are calculated. All results are displayed in Tables 22 and 23. The Pearson’s coefficients are highly significant with all (p = 0.05). Thus, all null hypotheses are rejected except H1b. Therefore, H1a, H2a, H3a, H4a, H5a, H6a and H2b, H3b, H4b, H5b and H6b are accepted. Thus, social media preferences, hedonic motivation and shopping in times of COVID-19 are significantly associated with impulsive and compulsive buying. However, social media influence significantly associates with impulsive buying but not with compulsive buying (H1b is rejected as p ≥ 0.05).
Findings and recommendations
All the variables of the six factors show a positive correlation with impulsive buying and compulsive buying. However, the regression analysis illustrated social media influence and presence of COVID-19 pandemic as the strongest predictors for impulse buying.
Impulsive buying is defined as unplanned purchases made on the spur of the moment (Rook and Fisher, 1995). Customers who feel inspired by social networks, purchases of their contacts, recommendations of friends and influencers on social media are most likely to go in for impulse purchases. Social media feeds many images on different platforms. The mentions and tags done by people on the contact list allows other users to see what brand they are purchasing. Tagging and retagging the mentions and pictures make the images available for more and more users (Çelik et al., 2019). After buying a particular brand that others on one’s contact list have purchased, users tend to mention and tag the same brand that others are tagging, thus keeping up with the trend. Such customers do not even need to read more about the brand. When someone from the social media friends buys it, the prospective customer reads this as an approval. The ease of clicking on a friend’s post takes the customer to a virtual shop – which allows buying in seconds facilitates impulsive buying. Online shopping has seen an upward trend since a few years now. With the current situation of COVID-19, online shopping has been a more preferred approach (Assomul, 2020; Ingaldi and Brozova, 2020). Most retailers have social media pages and transact through the same pages. Links for buying on the same page facilitates the impulse felt in that moment. Attractive marketing and promotion acts as an encouraging factor in such a situation to facilitate an unplanned purchase.
It is ideal for marketers to capitalize on this spontaneous shopping. Ease of payment provided by credit cards and other modes as well as fast and convenient home delivery further attracts the customers. Impulsive buying is significantly associated to influencers too. Micro-influencers have been sought after world over because of the niche groups they influence (Dhanesh and Duthler, 2019). Because of the small size of the groups, their reach and effectiveness could be more than celebrities. Marketers should identify and hire such influencers who are congruent to their brand to increase customer engagement leading sales. Retailers should further engage existing and prospective customers by reminding them to tag and mention their purchases with them. They could disguise it with a contest or give away alert. This would also help encourage others on their friends’ list to go in for an impulsive purchase. These spontaneous purchases should be shown as a new way of shopping as a complete contrast to planned researched shopping, highlighting the fact that when one trusts the retailer, one can buy anytime and not actually plan and research for it.
Compulsive buying is defined as an uncontrolled urge to buy regularly (Faber and O’Guinn, 1989). It is different from impulsive buying. It is also not a higher spectrum of the same scale (Flight et al., 2012). Hedonic motivation is seen as the main predictor for compulsive buying in this study. Impulse shopping and acquiring new products is a central activity in their lives. They shop to relive stress. Their happiness is highest when they shop. They feel it absolutely necessary to keep up with trends (Kukar-Kinney et al., 2016).
For marketers, it is easiest to tap into this segment. They love shopping and are doing so on a regular bases; if such customers are buying a certain brand, they must be retained. Hedonic happiness is of maximum importance to them, hence, they would love a great in-store experience or an advertisement driven towards hedonism in online shopping scenario. They shop not for buying the product but for enjoying the experience (Langrehr, 1991). They feel delighted with the compliments they get online or in person when they use their newly purchased products. Artificial intelligence directed towards fit and virtual body avatars (Tandon, 2021) could further engage these shoppers.
Both the impulsive buyers and the compulsive buyers have shown interest in shopping during these uncertain times. The presence of COVID-19 makes people find solace in shopping. They find it prudent to spend instead of saving. They shop in spite of a lot of things unused from last purchases. A small purchase also gives them happiness – the lipstick effect (Jamal, 2020). The continued presence of the pandemic has mandated customers to stay home to stay safe. Social media platforms have allowed them to wear and flaunt new shopping via pictures and posts even from the comfort of their homes.
Though many governments are trying to normalise the offline shopping experience, most companies had to pivot to online shopping to save the day. Ease of payment and contact-less delivery further encourage online shopping. COVID-19 has put social media and online shopping in the driver’s seat in the marketing game. To further leverage the situation and maximise sales, customers should be encouraged to make impulsive and compulsive purchases. Shopping in the time of COVID-19 should be promoted as comforting and way to cope with the uncertainty of the pandemic.
Conclusion
After an in-depth analysis of various factors that can affect impulsive and compulsive buying, the influence of social media (for impulsive buying), hedonic happiness (for compulsive buying) and the pandemic COVID (for both) have emerged as the strongest predictors. Hence, social media presence, active influence on prospective buyers and EWOM through their contact lists urges buyers to go in for an unplanned purchase. Compulsive buyers have uncontrolled urge regularly and are most likely to move by hedonic happiness. A good shopping experience online or offline moves them towards a compulsive purchase.
Limitations and further scope of research
Research is conducted in the city of Ahmedabad among SEC A and B in the age group of 25–70, though a large number of respondents fall under the age group of 30–50. Similar research can be conducted with the younger millennials and Gen Z. Most of the respondents are from the cities of Ahmedabad and Mumbai, however, research can be replicated for other cities of India. Apparels and accessions being global products, research can be conducted in any city around the world. A larger sample can be studied as well. The influences and preferences of social media have been studied in this paper. A focussed study on the moderating role of influencers and the impact of different platforms of social media can be conducted.
Because of the presence of the pandemic COVID-19, meeting the respondents was not possible for safety. However, in-depth interviews with the respondents could lead to more in-depth understanding of the same.
Figures
Figure 1.
AMOS output for structural equation modelling (SEM)
Constructs and items with their references
| References | Construct | Label |
|---|---|---|
| Social media influence | ||
| Aragoncillo and Orus (2018) | Social networks inspire my purchases of clothing and accessories | INFL1 |
| Aragoncillo and Orus (2018) | Sometimes, when I see an apparel/accessory on social media, I often search for it online to buy it | INFL2 |
| Aragoncillo and Orus (2018) | Sometimes, I feel attracted to the apparels and accessory shared by my contacts on social networks | INFL3 |
| Badgaiyan and Verma (2014) | Attractive marketing and promotional offers motivate me to purchase more than my scheduled purchase | INFL5 |
| Social media preferences | ||
| Own development | I buy through the social media page of the retailer | SMP1 |
| Prasad and Garg (2019) | I use social media to communicate with retailers | SMP2 |
| Prasad and Garg (2019) | My relationship with brands is enhanced because of social media | SMP3 |
| Prasad and Garg (2019) | I am proud to tell/show/tag the brand I buy | SMP4 |
| Prasad and Garg (2019) | I often read online about the brand/products | SMP5 |
| Jiménez-Castillo and Sánchez-Fernández (2019) | I follow the purchase recommendations of influencers I follow on social media sites | SMP6 |
| Jiménez-Castillo and Sánchez-Fernández (2019) | I buy a brand based on the advice given by an influencer I follow | SMP7 |
| Own development | I buy a brand based on what my friends from my contact list have mentioned | SMP8 |
| Atulkar and Kesari (2018), Rook and Fisher (1995) | Purchases of my friends mentioned on social media site make me go in for unplanned, spontaneous purchase | SMP9 |
| Hedonic motivation | ||
| Badgaiyan and Verma (2014) | Shopping is a fun and enjoyable activity to me | HEDO1 |
| Badgaiyan and Verma (2014) | I obtain pleasure in buying something attractive | HEDO2 |
| Arnold and Reynolds (2003) | To me, shopping is a way to relieve stress | HEDO3 |
| Arnold and Reynolds (2003) | I shop to keep up with trends. | HEDO4 |
| Arnold and Reynolds (2003) | Shopping makes me feel like I am in my own universe | HEDO5 |
| Dey and Srivastava (2017) | Finding unique things makes me excited | HEDO6 |
| Dey and Srivastava (2017) | I enjoy compliments and words of praise when I show/tag something I shopped | HEDO7 |
| Arnold and Reynolds (2003) | Much of my life centres around shopping | HEDO8 |
| Arnold and Reynolds (2003) | I have a lot of things that I still have not used | HEDO9 |
| Shopping in COVID-19 | ||
| Xian et al. (2020) | Shopping makes me happy in the dull and grim times of COVID-19 | SHCV1 |
| Xian et al. (2020) | After spending many hours working/reading online, I feel relaxed to shop online | SHCV2 |
| Jamal (2020) | These unprecedented times influence me to spend more and save less | SHCV3 |
| Own development | I buy products even though I may not need them immediately | SHCV4 |
| Xian et al. (2020) | A small purchase regularly also makes me happy | SHCV5 |
| Own development | I have been buying apparels/accessories during the COVID times | SHCV6 |
| Impulsive buying | ||
| Rook and Fisher (1995), Elizabeth Ferrell and Beatty (1998) | I often buy spontaneously | IMPL1 |
| Rook and Fisher (1995), Elizabeth Ferrell and Beatty (1998) | “Just do it,” describes the way I shop | IMPL2 |
| Rook and Fisher (1995), Elizabeth Ferrell and Beatty (1998) | I often buy things without thinking | IMPL3 |
| Rook and Fisher (1995), Elizabeth Ferrell and Beatty (1998) | “I see it. I buy it,” describes my shopping behaviour | IMPL4 |
| Rook and Fisher (1995), Elizabeth Ferrell and Beatty (1998) | Sometimes I buy things on the spur of the moment | IMPL5 |
| Rook and Fisher (1995), Elizabeth Ferrell and Beatty (1998) | I carefully plan most of my purchases (reversed item) | IMPL6R |
| Rook and Fisher (1995), Elizabeth Ferrell and Beatty (1998) | Sometimes, I am a bit reckless about what I buy | IMPL7 |
| Aragoncillo and Orus (2018) | Sometimes, when I see an apparel/accessory on social media, I feel like buying it immediately (disguised) | IMPL8D |
| Compulsive buying | ||
| Edwards (1992) Edwards (1993) | I feel anxious/nervous on the days I do not shop | CMPL1 |
| Edwards (1992) Edwards (1993) | I buy things even though I cannot really afford them | CMPL2 |
| Edwards (1992) Edwards (1993) | I go on buying binges | CMPL3 |
| Edwards (1992) Edwards (1993) | I buy things even when I do not need them | CMPL4 |
| Faber and O’Guinn (1989) | I think others would be horrified if they knew of my shopping habits | CMPL5 |
Demographic profile of the respondents
| Measure | Items | Frequency | (%) |
|---|---|---|---|
| Age | Less than 25 years | 8 | 5.5 |
| 25–29 years | 17 | 11.6 | |
| 30–39 years | 71 | 48.6 | |
| 40–55 years | 42 | 28.8 | |
| 56–75 years | 8 | 5.5 | |
| Gender | Male | 36 | 24.8 |
| Female | 109 | 75.2 | |
| Marital status | Single | 20 | 13.7 |
| Married | 125 | 85.6 | |
| Separated | 1 | 0.7 | |
| Education | Graduate | 66 | 45.2 |
| Postgraduate | 78 | 53.4 | |
| PhD | 2 | 1.4 | |
| Monthly family income | Less than 25,000 | 8 | 5.5 |
| 25,000–50,000 | 12 | 8.2 | |
| 50,000–100,000 | 33 | 22.6 | |
| 100,000–200,000 | 18 | 12.3 | |
| More than 200,000 | 75 | 51.4 | |
| Occupation | Student | 5 | 3.4 |
| Self-employed | 56 | 38.4 | |
| Corporate job | 15 | 10.3 | |
| Freelancer | 14 | 9.6 | |
| Professional | 28 | 19.2 | |
| Homemaker | 28 | 19.2 | |
| I have bought an apparel recently | Don’t know | 1 | 0.7 |
| Yes | 79 | 54.1 | |
| No | 66 | 45.2 | |
| I intend to buy soon | Don’t know | 51 | 34.9 |
| Yes | 46 | 31.5 | |
| No | 49 | 33.6 | |
| I have bought and follow the brand on social media | Don’t Know | 7 | 4.8 |
| Yes | 61 | 41.8 | |
| No | 78 | 53.4 |
Descriptive statistics of factors influencing impulsive buying and compulsive buying
| Social media influence | Mean | Std. deviation |
|---|---|---|
| Social networks inspire my purchases of clothing and accessories | 2.79 | 1.103 |
| Sometimes, when I see an apparel/accessory on social media, I often search for it online to buy it | 2.55 | 1.038 |
| Sometimes, I feel attracted to the apparels and accessory shared by my contacts on social networks | 2.78 | 1.014 |
| Attractive marketing and promotional offers motivates me to purchase more than my scheduled purchase | 2.71 | 1.144 |
| Social media preferences | ||
| I buy through the social media page of the retailer | 2.79 | 1.039 |
| I use social media to communicate with retailers | 2.92 | 1.105 |
| My relationship with brands is enhanced because of social media | 2.34 | 1.079 |
| I am proud to tell/show/tag the brand I buy | 3.22 | 1.171 |
| I often read online about the brand/products | 2.27 | 1.091 |
| I follow the purchase recommendations of influencers I follow on social media sites | 3.04 | 1.101 |
| I buy a brand based on the advice given by an influencer I follow | 3.12 | 1.01 |
| I buy a brand based on what my friends from my contact list have mentioned | 2.66 | 1.006 |
| Purchases of my friends mentioned on social media site makes me go in for unplanned spontaneous purchase | 3.46 | 1.157 |
| Hedonic motivation | ||
| Shopping is a fun and enjoyable activity to me | 2.27 | 0.978 |
| I obtain pleasure in buying something attractive | 2.23 | 0.962 |
| To me shopping is way to relive stress | 2.95 | 1.258 |
| I shop to keep up with trends | 3.1 | 1.188 |
| Shopping makes me feel like I am in my own universe | 3.24 | 1.039 |
| Finding unique things makes me excited | 2.27 | 0.942 |
| I enjoy compliments and words of praise when I show/tag something I shopped | 3.03 | 1.111 |
| Much of my life centres around shopping | 3.9 | 0.942 |
| I have lot of things that I still have not used | 3.36 | 1.069 |
| Shopping in COVID-19 | ||
| Shopping makes me happy in the dull and grim times of COVID-19 | 3.02 | 1.148 |
| After spending many hours working/reading online, I feel relaxed to shop online | 3.32 | 1.137 |
| These unprecedented times influence me to spend more and save less | 3.81 | 1.059 |
| I buy products even though I may not need them immediately | 3.45 | 1.102 |
| A small purchase regularly also makes me happy | 2.97 | 1.101 |
| I have been buying apparels/accessories during the COVID times | 3.05 | 1.188 |
Descriptive statistics of impulsive and compulsive buying
| Mean | Std. deviation | |
|---|---|---|
| Impulsive buying | ||
| I often buy spontaneously | 2.62 | 1.128 |
| “Just do it,” describes the way I shop | 2.99 | 1.151 |
| I often buy things without thinking | 3.54 | 0.99 |
| “I see it. I buy it,” describes my shopping behaviour | 3.3 | 1.072 |
| Sometimes I buy things on the spur of the moment | 2.93 | 1.118 |
| I carefully plan most of my purchases (reversed item) | 3.527 | 1.02517 |
| Sometimes, I am a bit reckless about what I buy | 2.86 | 1.102 |
| Sometimes, when I see an apparel/accessory on social media, I feel like buying it immediately (disguised) | 2.95 | 1.185 |
| Compulsive buying | ||
| I feel anxious/nervous on the days I do not shop | 4.29 | 0.863 |
| I buy things even though I cannot really afford them | 4.29 | 0.832 |
| I go on buying binges | 3.93 | 0.987 |
| I buy things even when I do not need them | 3.68 | 1.002 |
| I think others would be horrified whether they knew of my shopping habits | 4.07 | 0.959 |
Reliability statistics
| Cronbach’s alpha | Cronbach’s alpha based on standardized items | N of items |
|---|---|---|
| 0.951 | 0.951 | 41 |
KMO and Bartlett’s test
| Kaiser–Meyer–Olkin measure of sampling adequacy | 0.871 | |
|---|---|---|
| Bartlett’s test of sphericity | Approx. chi square | 2,447.067 |
| df | 378 | |
| Sig. | 0.000 | |
Total variance explained
| Initial eigenvalues | Extraction sums of squared loadings | Rotation sums of squared loadings | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Component | Total | (%) of variance | Cumulative (%) | Total | (%) of variance | Cumulative (%) | Total | (%) of variance | Cumulative (%) |
| 1 | 10.511 | 37.538 | 37.538 | 10.51 | 37.538 | 37.538 | 4.205 | 15.017 | 15.017 |
| 2 | 2.496 | 8.915 | 46.453 | 2.496 | 8.915 | 46.453 | 3.829 | 13.675 | 28.692 |
| 3 | 1.854 | 6.622 | 53.074 | 1.854 | 6.622 | 53.074 | 3.101 | 11.075 | 39.768 |
| 4 | 1.429 | 5.103 | 58.177 | 1.429 | 5.103 | 58.177 | 2.88 | 10.287 | 50.055 |
| 5 | 1.263 | 4.509 | 62.686 | 1.263 | 4.509 | 62.686 | 2.706 | 9.663 | 59.718 |
| 6 | 1.173 | 4.19 | 66.876 | 1.173 | 4.19 | 66.876 | 2.004 | 7.158 | 66.876 |
Extraction method: principal component analysis
Rotated component matrixa
| Component | ||||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | |
| INFL1 | 0.105 | 0.574 | 0.079 | 0.124 | 0.425 | 0.062 |
| INFL2 | 0.111 | 0.246 | 0.240 | 0.255 | 0.696 | 0.076 |
| INFL4 | 0.129 | 0.746 | 0.231 | 0.058 | 0.255 | −0.155 |
| INFL5 | 0.286 | 0.680 | 0.161 | 0.090 | 0.289 | 0.079 |
| SMP1 | 0.429 | 0.112 | −0.034 | 0.133 | 0.629 | 0.221 |
| SMP2 | 0.221 | 0.133 | 0.139 | 0.044 | 0.726 | 0.030 |
| SMP3 | 0.206 | 0.410 | −0.105 | 0.174 | 0.525 | 0.324 |
| SMP4 | −0.081 | 0.699 | 0.298 | 0.192 | 0.060 | 0.056 |
| SMP5 | −0.072 | 0.141 | 0.119 | 0.113 | 0.408 | 0.626 |
| SMP6 | 0.047 | 0.388 | 0.302 | 0.254 | 0.066 | 0.666 |
| SMP7 | 0.253 | 0.562 | 0.047 | 0.301 | −0.064 | 0.492 |
| SMP8 | 0.084 | 0.747 | 0.030 | 0.099 | 0.021 | 0.187 |
| SMP9 | 0.211 | 0.502 | 0.441 | 0.052 | 0.280 | 0.236 |
| HEDO1 | 0.364 | 0.141 | 0.217 | 0.662 | 0.208 | −0.029 |
| HEDO2 | 0.194 | 0.113 | 0.136 | 0.858 | 0.143 | 0.090 |
| HEDO3 | 0.335 | 0.089 | 0.622 | 0.509 | 0.089 | −0.001 |
| HEDO4 | 0.291 | 0.140 | 0.595 | 0.314 | 0.019 | 0.213 |
| HEDO5 | 0.273 | 0.192 | 0.526 | 0.495 | 0.237 | 0.102 |
| HEDO6 | 0.077 | 0.238 | 0.149 | 0.624 | 0.081 | 0.298 |
| HEDO7 | −0.036 | 0.231 | 0.743 | 0.184 | 0.068 | 0.043 |
| HEDO8 | 0.328 | 0.216 | 0.700 | 0.015 | 0.104 | 0.253 |
| HEDO9 | 0.450 | −0.184 | 0.208 | −0.049 | 0.111 | 0.556 |
| SHCV1 | 0.771 | 0.111 | 0.115 | 0.233 | 0.269 | −0.009 |
| SHCV2 | 0.669 | 0.195 | 0.297 | 0.327 | 0.217 | 0.073 |
| SHCV3 | 0.631 | −0.033 | 0.402 | −0.161 | 0.247 | 0.200 |
| SHCV4 | 0.735 | 0.236 | 0.188 | 0.137 | −0.104 | 0.247 |
| SHCV5 | 0.592 | 0.188 | 0.213 | 0.281 | 0.233 | −0.056 |
| SHCV6 | 0.768 | 0.072 | −0.048 | 0.185 | 0.169 | −0.011 |
Extraction method: principal component analysis.
Rotation method: varimax with Kaiser normalization.
aRotation converged in 10 iterations
Nomenclature for latent variables
| Social media influence | |
|---|---|
| INFL1 | Social networks inspire my purchases of clothing and accessories |
| INFL4 | Sometimes I feel attracted to the apparels and accessories shared by my contact list |
| INFL5 | Attractive marketing and promotional offers motivates me to purchase more |
| SMP4 | I am proud to tell/show/tag the brand I buy |
| SMP7 | I buy a brand based on the advice given by an influencer I follow |
| SMP8 | I buy a brand based on what my friends from my contact list have mentioned |
| SMP9 | Purchases of my friends mentioned on social media site makes me go in for an unplanned purchase |
| Social commerce | |
| INFL2 | Sometimes when I see an apparel/accessory on social media I often search for it online |
| SMP1 | I buy through the social media page of the retailer |
| SMP2 | I use social media to communicate with retailers |
| SMP3 | My relationship with brands is enhanced because of social media |
| EWOM of social commerce | |
| SMP5 | I often read online about the brand products |
| SMP6 | I follow the purchase recommendations of influencers I follow on social media |
| Hedonism (happiness) | |
| HEDO3 | To me shopping is way to relive stress |
| HEDO4 | I shop to keep up with trends |
| HEDO5 | Shopping makes me feel like I am in my own universe |
| HEDO7 | I enjoy compliments and words of praise when I show/tag/ something I shopped |
| HEDO8 | Much of my life centers around shopping |
| Hedonism (fun) | |
| HEDO1 | Shopping is a fun and enjoyable activity to me |
| HEDO2 | I obtain pleasure in buying something attractive |
| HEDO6 | Finding unique things makes me excited |
| Shopping during COVID | |
| SHCV1 | Shopping makes me happy in the dull and grim times of COVID-19 |
| SHCV2 | After spending many hours working/reading/online I feel relaxed to shop online |
| SHCV3 | These unprecedented times influence me to spend more and save less |
| SHCV4 | I buy products even though I may not need them immediately |
| SHCV5 | A small purchase regularly also makes me happy |
| SHCV6 | I have been buying apparels/accessories during the COVID times |
| HEDO9 | I have lot of things that I still haven’t used |
Regression weights: (group number 1 – default model)
| Estimate | SE | CR | P | |||
|---|---|---|---|---|---|---|
| Impulsive | ← | SocialMediaInfluence | 0.512 | 0.087 | 5.871 | *** |
| Compulsive | ← | SocialMediaInfluence | 0.075 | 0.072 | 1.044 | 0.296 |
| Impulsive | ← | SocialCommerce | −0.01 | 0.082 | −0.116 | 0.908 |
| Compulsive | ← | SocialCommerce | 0.018 | 0.068 | 0.257 | 0.797 |
| Impulsive | ← | SocialCommEWOM | −0.086 | 0.066 | −1.312 | 0.19 |
| Compulsive | ← | SocialCommEWOM | 0.003 | 0.055 | 0.05 | 0.96 |
| Impulsive | ← | HedoHappiness | −0.196 | 0.085 | −2.317 | 0.02 |
| Compulsive | ← | HedoHappiness | 0.329 | 0.07 | 4.679 | *** |
| Compulsive | ← | HedoFun | −0.157 | 0.068 | −2.296 | 0.022 |
| Impulsive | ← | HedoFun | 0.176 | 0.082 | 2.139 | 0.032 |
| Compulsive | ← | ShopinCovid19 | 0.471 | 0.068 | 6.921 | *** |
| Impulsive | ← | ShopinCovid19 | 0.329 | 0.082 | 4.006 | *** |
Standardized regression weights: (group number 1 – default model)
| Estimate | |||
|---|---|---|---|
| Impulsive | ← | SocialMediaInfluence | 0.521 |
| Compulsive | ← | SocialMediaInfluence | 0.079 |
| Impulsive | ← | SocialCommerce | −0.01 |
| Compulsive | ← | SocialCommerce | 0.019 |
| Impulsive | ← | SocialCommEWOM | −0.103 |
| Compulsive | ← | SocialCommEWOM | 0.003 |
| Impulsive | ← | HedoHappiness | −0.219 |
| Compulsive | ← | HedoHappiness | 0.38 |
| Compulsive | ← | HedoFun | −0.166 |
| Impulsive | ← | HedoFun | 0.18 |
| Compulsive | ← | ShopinCovid19 | 0.515 |
| Impulsive | ← | ShopinCovid19 | 0.348 |
Covariances: (group number 1 – default model)
| Estimate | SE | CR | P | |||
|---|---|---|---|---|---|---|
| SocialMediaInfluence | ↔ | ShopinCovid19 | 0.303 | 0.06 | 4.991 | *** |
| HedoFun | ↔ | ShopinCovid19 | 0.347 | 0.06 | 5.547 | *** |
| HedoHappiness | ↔ | ShopinCovid19 | 0.446 | 0.07 | 6.281 | *** |
| SocialCommEWOM | ↔ | ShopinCovid19 | 0.262 | 0.07 | 3.839 | *** |
| SocialCommerce | ↔ | ShopinCovid19 | 0.389 | 0.07 | 5.909 | *** |
| HedoHappiness | ↔ | HedoFun | 0.446 | 0.07 | 6.434 | *** |
| SocialCommEWOM | ↔ | HedoFun | 0.311 | 0.07 | 4.592 | *** |
| SocialCommerce | ↔ | HedoFun | 0.315 | 0.06 | 5.136 | *** |
| SocialCommEWOM | ↔ | HedoHappiness | 0.385 | 0.08 | 5.103 | *** |
| SocialCommerce | ↔ | HedoHappiness | 0.323 | 0.07 | 4.878 | *** |
| SocialMediaInfluence | ↔ | HedoHappiness | 0.4 | 0.07 | 5.959 | *** |
| SocialCommerce | ↔ | SocialCommEWOM | 0.378 | 0.07 | 5.254 | *** |
| SocialMediaInfluence | ↔ | HedoFun | 0.315 | 0.06 | 5.293 | *** |
| SocialMediaInfluence | ↔ | SocialCommEWOM | 0.419 | 0.07 | 5.866 | *** |
| SocialMediaInfluence | ↔ | SocialCommerce | 0.394 | 0.06 | 6.13 | *** |
***= less than 0.005
Variances: (group number 1 – default model)
| Estimate | SE | CR | P | |||
|---|---|---|---|---|---|---|
| SocialMediaInfluence | 0.641 | 0.075 | 8.515 | *** | ||
| SocialCommerce | 0.691 | 0.081 | 8.515 | *** | ||
| SocialCommEWOM | 0.879 | 0.103 | 8.515 | *** | ||
| HedoHappiness | 0.771 | 0.091 | 8.515 | *** | ||
| HedoFun | 0.647 | 0.076 | 8.515 | *** | ||
| ShopinCovid19 | 0.691 | 0.081 | 8.515 | *** | ||
| e1 | 0.343 | 0.04 | 8.515 | *** | ||
| e2 | 0.235 | 0.028 | 8.515 | *** |
Pearson’s correlation (for impulsive buying)
| ImpulsiveBuying | SocialMediaInfluence | SocialCommerce | SocialCommerceEWOM | HedoHappiness | HedoFun | ShopinCovid19 | |
|---|---|---|---|---|---|---|---|
| ImpulsiveBuying | 1 | ||||||
| SocialMediaInfluence | 0.579 | 1 | |||||
| SocialCommerce | 0.432 | 0.591 | 1 | ||||
| SocialCommerceEWOM | 0.271 | 0.558 | 0.485 | 1 | |||
| HedoHappiness | 0.351 | 0.569 | 0.443 | 0.468 | 1 | ||
| HedoFun | 0.43 | 0.489 | 0.472 | 0.413 | 0.63 | 1 | |
| ShopinCovid19 | 0.504 | 0.455 | 0.563 | 0.336 | 0.61 | 0.52 | 1 |
Correlation is significant at the 0.000 level (one tailed)
Model summary
| Model | R | R2 | Adjusted R2 | Std. error of the estimate |
|---|---|---|---|---|
| 1 | 0.667a | 0.445 | 0.421 | 0.60013 |
aPredictors: (Constant), ShopinCovid19, SocialCommerceEWOM, HedoFun, SocialMediaInfluence, SocialCommerce, HedoHappiness
Analysis of variancea
| Model | Sum of squares | df | Mean square | F | Sig. | |
|---|---|---|---|---|---|---|
| 1 | Regression | 40.163 | 6 | 6.694 | 18.586 | 0.000b |
| Residual | 50.061 | 139 | 0.36 | |||
| Total | 90.224 | 145 | ||||
aDependent variable: ImpulsiveBuying.
bPredictors: (constant), ShopinCovid19, SocialCommerceEWOM, HedoFun, SocialMediaInfluence, SocialCommerce, HedoHappiness
Coefficientsa
| Model | Unstandardized coefficients | Standardized coefficients | ||||
|---|---|---|---|---|---|---|
| B | Std. Error | Beta | t | Sig. | ||
| 1 | (Constant) | 0.988 | 0.234 | 4.229 | 0 | |
| 0.512 | 0.089 | 0.521 | 5.749 | 0 | ||
| SocialMediaInfluence | ||||||
| −0.01 | 0.084 | −0.01 | −0.113 | 0.91 | ||
| SocialCommerce | ||||||
| −0.086 | 0.067 | −0.103 | −1.285 | 0.201 | ||
| SocialCommerceEWOM | ||||||
| −0.196 | 0.087 | −0.219 | −2.269 | 0.025 | ||
| HedoHappiness | ||||||
| 0.176 | 0.084 | 0.18 | 2.095 | 0.038 | ||
| HedoFun | ||||||
| 0.329 | 0.084 | 0.348 | 3.922 | 0 | ||
| ShopinCovid19 | ||||||
aDependent variable: ImpulsiveBuying.
t value should be more than 4,
As n − 2 = df − 2 = 4
Pearson’s correlation (for compulsive buying)
| CompulsiveBuying | SocialMediaInfluence | SocialCommerce | SocialCommerceEWOM | HedoHappiness | HedoFun | ShopinCovid19 | |
|---|---|---|---|---|---|---|---|
| CompulsiveBuying | 1 | ||||||
| SocialMediaInfluence | 0.462 | 1 | |||||
| SocialCommerce | 0.448 | 0.591 | 1 | ||||
| SocialCommerceEWOM | 0.34 | 0.558 | 0.485 | 1 | |||
| HedoHappiness | 0.645 | 0.569 | 0.443 | 0.468 | 1 | ||
| HedoFun | 0.391 | 0.489 | 0.472 | 0.413 | 0.632 | 1 | |
| ShopinCovid19 | 0.709 | 0.455 | 0.563 | 0.336 | 0.611 | 0.519 | 1 |
Correlation is significant at the 0.000 level (one tailed)
Model summary
| Model | R | Adjusted R square | Std. error of the estimate |
|---|---|---|---|
| 1 | 0.769a | 0.574 | 0.49733 |
aPredictors: (constant), ShopinCovid19, SocialCommerceEWOM, HedoFun, SocialMediaInfluence, SocialCommerce, HedoHappiness
Analysis of variancea
| Model | Mean square | F | Sig. | |
|---|---|---|---|---|
| 1 | Regression | 8.314 | 33.613 | 0.000b |
| Residual | 0.247 | |||
| Total | ||||
aDependent variable: CompulsiveBuying.
bPredictors: (Constant), ShopinCovid19, SocialCommerceEWOM, HedoFun, SocialMediaInfluence, SocialCommerce, HedoHappiness
Coefficientsa
| Model | Unstandardized coefficients | Standardized coefficients | ||||
|---|---|---|---|---|---|---|
| B | Std. Error | Beta | t | Sig. | ||
| 1 | (Constant) | 1.519 | 0.194 | 7.846 | 0 | |
| 0.075 | 0.074 | 0.079 | 1.023 | 0.308 | ||
| SocialMediaInfluence | ||||||
| 0.018 | 0.07 | 0.019 | 0.252 | 0.802 | ||
| SocialCommerce | ||||||
| 0.003 | 0.056 | 0.003 | 0.049 | 0.961 | ||
| SocialCommerceEWOM | ||||||
| 0.329 | 0.072 | 0.38 | 4.581 | 0 | ||
| HedoHappiness | ||||||
| −0.157 | 0.07 | −0.166 | −2.248 | 0.026 | ||
| HedoFun | ||||||
| 0.471 | 0.069 | 0.515 | 6.777 | 0 | ||
| ShopinCovid19 | ||||||
aDependent variable: CompulsiveBuying
Chi-square tests for impulsive buying
| Pearson’s | Asmp. sig | ||||||
|---|---|---|---|---|---|---|---|
| Hypothesis | chi square | df | (two sided) | Phi | Cramer’s V | Approx sig | Outcome |
| H1a | 711.896 | 400 | 0 | 2.208 | 0.552 | 0 | Reject null H |
| H2a | 1,101.278 | 750 | 0 | 2.746 | 0.549 | 0 | Reject null H |
| H3a | 1,201.84 | 750 | 0 | 2.869 | 0.574 | 0 | Reject null H |
| H4a | 815.174 | 550 | 0 | 2.363 | 0.504 | 0 | Reject null H |
Chi-square tests for compulsive buying
| Hypothesis | Pearson’s | df | Asmp. sig | Phi | Cramer’s V | Approx sig | |
|---|---|---|---|---|---|---|---|
| chi square | (two sided) | Outcome | |||||
| H1b | 315.363 | 256 | 0.007 | 1.47 | 0.367 | 0.007 | Retain null H |
| H2b | 625.186 | 480 | 0 | 2.069 | 0.517 | 0 | Reject null H |
| H3b | 690.989 | 480 | 0 | 2.176 | 0.544 | 0 | Reject null H |
| H4b | 674.703 | 352 | 0 | 2.15 | 0.537 | 0 | Reject null H |
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Further reading
Covid-19 Pandemic Radically Changing Consumer Behaviour in India: EY Survey (2020).
Schouten, A.P., Janssen, L. and Verspaget, M. (2019), “Celebrity vs Influencer endorsements in advertising: the role of identification, credibility, and product-endorser fit”, International Journal of Advertising, Vol. 39 No. 2, pp. 1-24, doi: 10.1080/02650487.2019.1634898.
Corresponding author
About the authors
Dr Kavita Kshatriya, PhD, MBA (Mktg), MCom (HRM), DCA, has a total of 22 years of experience. Out of this years, four years has been in corporate field in senior management capacity. She has more than 18 years of academic experience as of date. She is PhD guide at Kadi Sarva Vishwavidyalaya, Gandhinagar, and Gujarat Law Society in Management Area. She is having 18 years of academic experience as Professor and HOD in Integrated MBA Department. She is associated as an Academic Consultant at JG University, Ahmedabad. Her area of interest includes subjects like retailing, integrated marketing communication, services relationship marketing, human resource management and marketing management. She has done her doctorate in rural marketing focusing FMCG products under the able guidance of Vice Chancellor Dr B.A. Prajapati, North Gujarat University. Dr Kavita has also published and presented 42 national and international papers in reputed journals and magazines. She has written Text Book on Management – I, Books India Publication in February 2010 for the Engineering syllabus as per GTU norms.
Ms Priyanka Sharad Shah has an MBA in Marketing (Nirma University). She is Research Scholar (PhD) at School of Doctoral Research and Innovation, Gujarat Law Society, GLS University, Ahmedabad. She started her career with HDFC Ltd, after which she moved to academics. She has 8 years of experience in teaching at undergraduate and post graduate level. She is associated as Visiting Faculty with premier institutes such as H, L.I.C – Ahmedabad University, KS School of Business Management – Gujarat University, HL Centre for Professional Excellence – Ahmedabad Education Society and many more. Her areas of interest are marketing, services marketing, consumer behaviour, market research, international marketing and human resource management.