Selected Differences and Relationships of Consumers’ Online Brand-Related Activities and Their Motives

Abstract

The main goal of the paper is to assess the statistical significance of the differences in the motives and activities of COBRAs in the context of the frequency of use of these platforms and also the driving forces and motivations behind the brand-related activities. The importance of consumers’ online brand-related activities (COBRAs) as relevant factors in managerial and marketing practice is undeniable. In addition, this area is only at the beginning of research coverage, and thus defines a research gap for us. The dataset used hereunder is based on data acquired from a standardized questionnaire discussing the above-mentioned activities and motives. In total, the questionnaire was filled in by 401 respondents. The analysis made use of the Kruskal–Wallis H test to detect differences and the Spearman’s rho coefficient to detect relationships. Based on this, several statistically significant differences and relationships were identified in all cases. The most important implications drawn pertain to differences in the social-interaction motives on all platforms studied, the highest rate of concurrence, at the level of 3–4 h, spent on these platforms, as well as relationships with a strong correlation coefficient in content-creation activities and content-contribution activities on all social media platforms. Even though these findings require further analysis, they prove a valuable insight into the topic.

1. Introduction

Social media is an increasingly important driving force in the world. According to data, up to 4.7 billion people [1] use on an average 7.4 different social media platforms every month. Moreover, despite possible prejudices, social interaction with family and friends still remains the most important reason for their use [1]. The rate of consumption closely related to social media platforms is also growing [2]. The issue of social media research is still relevant, as evidenced by the ever-increasing number of studies carried out and papers written on the topic. Social media can be of significant importance, in any industry, but only if they and their mechanics and laws are properly understood. The widespread adoption and use of social networking provides an enormous source of data that can be used to answer a plethora of research questions, and from a variety of disciplines [3]. Research focused on social media potentially allows for a better understanding of several social phenomena. We are also able to use the results to create various opportunities for businesses, where one of the main ones is the ability to gain a competitive advantage [4]. One of these laws is the consumers’ online brand-related activities (COBRAs), which classify and understand the activities of consuming, contributing and creating content. However, these activities also have their own motives, which makes the understanding of the whole concept even more complicated. However, it should be noted that, without a sufficient degree of knowledge in this area, marketers’ abilities to create marketing strategies that take into account the important laws of social media are greatly reduced [5]. While we already see a large body of scientific knowledge regarding social media, it is this combination of consumers’ online brand-related activities and motives that has only recently been introduced to the current state of exploration, and thus forms a significant research gap. This calls for research in a variety of settings for a sufficient understanding of the issue. The aim of the paper is to clarify certain aspects of the issue and assess the statistical significance of the differences in the motives behind COBRA with regard to the frequency of social media use and the driving forces and motivations behind the brand-related activities. The study is structured in a standard way, and thus we first present the current state of research and theoretical knowledge, where we present related studies on which this research is based, as well as those that can develop its results in a broader context. We then present the methodological section with the methods used for data analysis and collection and the formulation of the research hypotheses. In the results section, we present the outputs of the analytical processing of the collected data, and point out the most important results. Then, in the discussion section, we discuss these results in the context of research and theory and possible implications. We conclude by stating the fulfilment of the objective, and outline future research directions and limitations encountered in this investigation.

2. Current State of Research and Theoretical Background

Since the well-known uses and gratification theory can be put only to a limited use in the online space [6], it has been modified and updated several times over the years. One of its most advanced versions is the COBRA model—consumers’ online brand related activities, which works with three levels of activities and four categories of motivation for these activities [7]. Encouraging interactions among consumers and brands on the online environment is a very important ingredient for brand sustainability, which can have significant impact on various brand-performance indicators [8]. According to [9], the three levels of brand-related activities that best reflect the nature of consumer behaviour are content consumption, content contribution and content creation. Understanding consumers at individual stages is a very valuable know-how, in particular in the creation of sustainable and long-term marketing strategies, because at each stage the level of consumer engagement differs [10]. A similar issue (from the world of fashion) was studied by [11]. By studying these activities and the level of engagement, the success rate of specific tactics could be measured with greater precision [12]. Motivating consumers to engage with content allows the brand to interact with consumers directly—no other channel is able to do that [13]. Social media platforms could be seen as an endless source of user-generated content [14]. Studies that analyse this problem include [15,16,17].

However, there is always some motivation behind these activities. There are four main categories of motivation: information, entertainment, remuneration and social interaction [18]. The information motive implies a search for brand-related information on the platform. This leads to predictable patterns of behaviour, a desire to use the platform [19], and, ultimately, shapes attitudes towards brands and their accounts on these platforms [9]. The entertainment motive implies the ability to provide emotional rest and relaxation. An increased rate of platform usage is often ascribed to this motive [20]. The clever use of the entertainment motive boosts positive brand-image [21]. The impact the entertainment motive has on all three levels of activities was studied by [9,22]. The social interaction motive implies the need to belong somewhere [23], to gain recognition and promote one’s professional competences [24]. The relationship between this motivation and an increased level of engagement was already observed by [25] on the social network Twitter. Remuneration as a motive was the last to be added to the concept, and represents situations where consumers engage at various levels expecting some form of added value, usually in the form of remuneration, whether of an economic or different nature [9].

The frequency of use of these platforms also plays an important role when discussing this issue. The educational benefit following from the increased use of these platforms was also identified by [26] on a sample of medical students. Similarly, the increased use of social media platforms had a positive impact on the attitudes towards entrepreneurship among the young generation [27]. The amount of time spent on these platforms is constantly growing, and in the last 10 years it has grown from an average of 90 min to 147 min in 2022 [28]. Consumers spend an average of 36.4% of their time online on social media platforms [29].

3. Materials and Methods

The aim of the research is to expand the knowledge and information regarding consumers’ online brand-related activities and the motives behind these activities. The first researchers to study this issue were [7]. The main aim of the research is to assess the statistical significance of the differences in the motives behind the COBRA with regard to the frequency of use of these platforms and the driving forces and motivations behind the brand-related activities. The related research-questions have a scientific basis in studies [7,9,11,12]. Following on from the above, the following research hypotheses were proposed:

H1: 

There are statistically significant differences in terms of brand-related activities and the motives behind them with regard to the frequency of use of the social media platform Facebook.

H2: 

There are statistically significant differences in terms of brand-related activities and the motives behind them with regard to the frequency of use of the social media platform Instagram.

H3: 

There are statistically significant differences in terms of brand-related activities and the motives behind them with regard to the frequency of use of the social media platform YouTube.

H4: 

There are statistically significant relationships in terms of brand-related activities and the motives behind them with regard to the frequency of use of the social media platform Facebook.

H5: 

There are statistically significant relationships in terms of brand-related activities and motives behind them with regard to the frequency of use of the social media platform Instagram.

H6: 

There are statistically significant relationships in terms of brand-related activities and the motives behind them with regard to the frequency of use of the social media platform YouTube.

The dataset of this analysis consists of data obtained during the period of the first 6 months of 2021. The dataset reflects the conditions of the Slovak market, making our sample qualitatively representative of the demographics and behavioural aspects of this market. The Slovak market was chosen because the analysis in this study is only one part of a broader investigation that confirms the exploratory analysis carried out on a similar sample of the German market, which is similar in many, especially cultural, aspects to the Slovak one. The analysis made use of 401 valid questionnaires. This standardized-form questionnaire was presented in the study [7]. This questionnaire was distributed electronically, and completion was preceded by mandatory informed consent from respondents. The questionnaire had a total of 90 questions, 6 of which dealt with demographics and the behaviour of respondents in the social-networking space, and 28 questions related to the concept COBRA (Consumer online brand related activities) on one social network. The same series of questions was applied to all three social networks. Based on the initial analysis of data, the Kruskal–Wallis H test was used to test differences (because the measurement variable does not meet the normality assumption), followed by the Spearman’s rho coefficient (also because of the normality-assumption issue), which tested correlation relationships. The coefficients according to [30] served as a basis for evaluation of the questionnaire: negligible (0.00–0.20); weak (0.21–0.40); moderate (0.41–0.60); strong (0.61–0.80); very strong (0.81–1.00). In the case of the factor of time spent on the analysed social media, these were interval scales determining the given frequency of use (less than 30 min; 30–60 min; 1–2 h; 2–3 h; 3–4 h; more than 4 h). The motives and activities analysed here acted as latent variables (7 in total), which were made up of a set of manifest variables (25 in total). These were assessed on the basis of the attitude of the re-respondents, which they indicated on a 5-point Likert agreement scale for each manifest variable.

4. Results

This section outlines the results of the analysis, in particular the assessment of brand-related activities and the motives behind them in terms of time spent by users on these platforms. Below are outlined the most relevant results for all three analysed platforms (see

Table 1. A differential test of factors in the context of time spent on the social media platform Facebook.

Factor	Kruskal–Wallis H	df	Asymp. Sig
Information (motive)	3.957	5	0.556
Entertainment (motive)	2.850	5	0.723
Social interaction (motive)	12.654	5	0.027
Remuneration (motive)	7.519	5	0.185
Content consumption (activity)	12.011	5	0.035
Content contribution (activity)	27.184	5	0.000
Content creation (activity)	17.166	5	0.004

).

In the case of the social media platform Facebook, four statistically significant differences were identified in the context of time spent on this platform (see Table 1), where one was observed in the case of motives and three in the case of activities. The social-interaction motive showed the highest rate of agreement ($\tilde{\mathrm{x}}$ = 3.333; $\overline{\mathrm{x}}$ = 3.446) at 3–4 h spent on the platform every day. In the case of activities, the observations yielded similar results—the highest rate of agreement for content consumption ($\tilde{\mathrm{x}}$ = 3.000; $\overline{\mathrm{x}}$ = 3.146), content contribution ($\tilde{\mathrm{x}}$ = 2.667; $\overline{\mathrm{x}}$ = 2.566) and content creation ($\tilde{\mathrm{x}}$ = 2.333; $\overline{\mathrm{x}}$ = 2.297) was identified at 3–4 h spent on Facebook. Based on the findings, the hypothesis H1 on the existence of such differences is thus accepted.

With regard to correlation, statistical significance was present in all observations (see

Table 2. Factor-correlation matrix for the social media platform Facebook.

Spearman’s Rho
		Inform.	Entertain.	Social Int.	Remun.	Content Cons.	Content Contr.	Content Creat.
Information	Correlation	1.000
	Sig. (2-tailed)
Entertainment	Correlation	0.561	1.000
	Sig. (2-tailed)	0.000
Social interaction	Correlation	0.478	0.466	1.000
	Sig. (2-tailed)	0.000	0.000
Remuneration	Correlation	0.326	0.290	0.426	1.000
	Sig. (2-tailed)	0.000	0.000	0.000
Content consumption	Correlation	0.376	0.506	0.412	0.296	1.000
	Sig. (2-tailed)	0.000	0.000	0.000	0.000
Content contribution	Correlation	0.233	0.252	0.365	0.482	0.463	1.000
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000
Content creation	Correlation	0.142	0.167	0.226	0.453	0.324	0.715	1.000
	Sig. (2-tailed)	0.005	0.001	0.000	0.000	0.000	0.000

). The largest coefficient (r = 0.715) was identified in the case of the pair-content contribution and content creation, followed by the pair-information motive and entertainment motive (r = 0.561) and the pair-entertainment motive and content consumption (r = 0.506). Moderate strength of the coefficient was observed in a few more cases; however, the numbers were close to the limit of weak correlation. All other cases, although statistically significant, achieved only weak or negligible levels of correlation coefficient. Based on the findings, the hypothesis H4 about the existence of such relationships is accepted.

With regard to the social media platform Instagram, we observed statistically significant differences except for one case of motives (see

Table 3. A differential test of factors in the context of time spent on the social media platform Instagram.

Factor	Kruskal–Wallis H	df	Asymp. Sig
Information (motive)	22.280	5	0.000
Entertainment (motive)	15.222	5	0.009
Social interaction (motive)	35.129	5	0.000
Remuneration (motive)	6.590	5	0.253
Content consumption (activity)	23.916	5	0.000
Content contribution (activity)	30.473	5	0.000
Content creation (activity)	24.900	5	0.000

). In the category of activity motives, the highest level of agreement was observed at the level of 2–3 h spent on the platform every day for the informational motive ($\tilde{\mathrm{x}}$ = 4.000; $\overline{\mathrm{x}}$ = 3.844) and the entertainment motive ($\tilde{\mathrm{x}}$ = 4.000; $\overline{\mathrm{x}}$ = 3.932), and at the level of 3–4 h spent on the platform every day for the motive of social interaction ($\tilde{\mathrm{x}}$ = 4.000; $\overline{\mathrm{x}}$ = 3.761). The highest level of agreement was observed at the level of more than 4 h, in the context of content-consumption activity ($\tilde{\mathrm{x}}$ = 4.000; $\overline{\mathrm{x}}$ = 4.013). The other two activities of content contribution ($\tilde{\mathrm{x}}$ = 3.000; $\overline{\mathrm{x}}$ = 2.992) and content creation ($\tilde{\mathrm{x}}$ = 3.000; $\overline{\mathrm{x}}$ = 2.709) showed the highest level of agreement at the level of 3–4 h spent on this platform every day. Based on the findings, the hypothesis H2 about the existence of such differences is accepted.

In this case too, the statistical significance of the relationships was generally confirmed, but there was also considerable variability in the strength of these coefficients (see

Table 4. Factor-correlation matrix for the social media platform Instagram.

Spearman’s Rho
		Inform.	Entertain.	Social Int.	Remun.	Content Cons.	Content Contr.	Content Creat.
Information	Correlation	1.000
	Sig. (2-tailed)
Entertainment	Correlation	0.549	1.000
	Sig. (2-tailed)	0.000
Social interaction	Correlation	0.541	0.531	1.000
	Sig. (2-tailed)	0.000	0.000
Remuneration	Correlation	0.299	0.210	0.347	1.000
	Sig. (2-tailed)	0.000	0.000	0.000
Content consumption	Correlation	0.535	0.597	0.491	0.262	1.000
	Sig. (2-tailed)	0.000	0.000	0.000	0.000
Content contribution	Correlation	0.262	0.301	0.529	0.445	0.418	1.000
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000
Content creation	Correlation	0.108	0.116	0.390	0.433	0.207	0.794	1.000
	Sig. (2-tailed)	0.034	0.021	0.000	0.000	0.000	0.000

). In one case only, a strong coefficient (r = 0.794) was observed, and that was in the case of the pair-content contribution and content creation. The relations between information motive and entertainment motive (r = 0.549), information motive and social-interaction motive (r = 0.541), entertainment motive and social-interaction motive (r = 0.531) also reached a moderate strength of the coefficient. Moderate strength of the coefficient was identified also in the relationships between motives and activities, namely content consumption and information motive (r = 0.535), content consumption and entertainment motive (r = 0.597), content contribution and social-interaction motive (r = 0.529), content contribution and renumeration motive (r = 0.445) and content creation and renumeration motive (r = 0.433). In the case of activities, a moderate strength of the coefficient was observed in content consumption and content contribution (r = 0.418). All other coefficients proved to be weak or negligible. Based on the findings, the hypothesis H5 about the existence of such relationships is confirmed.

Only three statistically significant differences on the social media platform YouTube were observed (see

Table 5. A differential test of factors in the context of time spent on the YouTube social media platform.

Factor	Kruskal–Wallis H	df	Asymp. Sig
Information (motive)	3.436	5	0.633
Entertainment (motive)	2.878	5	0.719
Social interaction (motive)	18.581	5	0.002
Remuneration (motive)	18.227	5	0.003
Content consumption (activity)	7.782	5	0.169
Content contribution (activity)	5.527	5	0.355
Content creation (activity)	18.595	5	0.002

). In the case of the social-interaction motive, the highest level of agreement was observed at the level of 3–4 h spent on the platform every day ($\tilde{\mathrm{x}}$ = 3.333; $\overline{\mathrm{x}}$ = 3.252), with the numbers being the same for the content-creation activity ($\tilde{\mathrm{x}}$ = 2.000; $\overline{\mathrm{x}}$ = 2.170). In the case of the remuneration motive, the highest level of agreement was identified at the level of 30–60 min ($\tilde{\mathrm{x}}$ = 2.000; $\overline{\mathrm{x}}$ = 2.394). Based on the findings, the hypothesis H3 about the existence of such differences is accepted.

With regard to YouTube, two statistically insignificant relationships were observed (see

Table 6. Factor-correlation matrix for social media platform Youtube.

Spearman’s Rho
		Inform.	Entertain.	Social Int.	Remun.	Content Cons.	Content Contr.	Content Creat.
Information	Correlation	1.000
	Sig. (2-tailed)
Entertainment	Correlation	0.649	1.000
	Sig. (2-tailed)	0.000
Social interaction	Correlation	0.380	0.319	1.000
	Sig. (2-tailed)	0.000	0.000
Remuneration	Correlation	0.183	0.014	0.488	1.000
	Sig. (2-tailed)	0.000	0.782	0.000
Content consumption	Correlation	0.392	0.358	0.461	0.430	1.000
	Sig. (2-tailed)	0.000	0.000	0.000	0.000
Content contribution	Correlation	0.156	0.087	0.497	0.640	0.533	1.000
	Sig. (2-tailed)	0.000	0.086	0.000	0.000	0.000
Content creation	Correlation	0.027	−0.102	0.407	0.668	0.376	0.756	1.000
	Sig. (2-tailed)	0.034	0.043	0.000	0.000	0.000	0.000

). Here, however, as many as four strong coefficients were observed, where the strongest was found for content creation and content contribution (r = 0.756), followed by content creation and the remuneration motive (r = 0.668), the entertainment motive and the information motive (r = 0.649) and content contribution and the remuneration motive (r = 0.640). A moderate strength of the coefficient was observed for the remuneration motive and social-interaction motive (r = 0.488), for the remuneration motive and content consumption (r = 0.430), for the motive of social interaction and content consumption (r = 0.461) and also for content contribution and the motive of social interaction (r = 0.497). In the case of activities, a moderate strength was observed in content consumption and content contribution (r = 0.533). Based on the findings, the hypothesis H6 about the existence of such relationships is accepted.

5. Discussion and Implications

Based on the analysis, the paper made an assessment of the statistical significance of the differences and relationships studied herein. The results yielded important future implications and shed light on the current situation. With regard to the statistically significant differences, the motive of social interaction should be pointed out, as it proved to be the most significant in the case of all three analysed social media platforms, acquiring the highest rate of concurrence at the level of 3–4 h spent on these platforms. In the context of recent data on consumer behaviour and the time they spend on platforms [28], this is a far above-average rate. This suggests that if consumers do not spend a lot of time on social media platforms, they should be engaged in different ways (focusing on other motives). In practice, this can mean significant savings in the marketing budget. Social-interaction motivation begins to dominate only after a certain time-threshold is exceeded. According to [31], entrepreneurs can actually reduce the effect their marketing activities have if they invest too much in communication tools that focus too heavily on this motive. Therefore, it is important to know which typology of the consumer (in terms of time) is the most suitable for this chosen type of communication. This will ensure that a company’s human capital does not waste valuable time on actions that will not add any significant value to the business.

Differences in content-creation activity across all analysed platforms are also worth noting. In all cases, it was a time interval of 3–4 h that showed the highest level of agreement. This is unsurprising, since content creation is the most demanding of the activities, and therefore short time-intervals are not enough. However, time is just one of the possible factors. There is a parallel with study [32], which states that such platforms must continuously create new functions to keep providing their users, as content creators, with relevant added value. If users want to make use of the added value, they must invest more time. Some authors [33] claim that the concept of content co-creation in the online space is gaining in popularity and that it will also result in an increase in mutual interactions between consumers.

However, the most important finding of the study is the strong correlation between the activities content creation and content contribution on all social media platforms. This is an important finding for a practice, especially if it is performing an activity on multiple platforms simultaneously. Addressing these activity levels first and foremost is a form of streamlining work, saving money and achieving set KPIs faster. The strong correlation between the two in terms of brand-related content was also observed by [34] in their study analysing Polish consumers. However, [6] observed that data varied in terms of individual social media platforms. Therefore, further research is required. The strong influence of socialization and self-expression in terms of both activities was observed by [35,36].

6. Conclusions, Limitations and Further Research

The aim of the study was to assess the statistical significance of differences in the motives and activities of COBRA in terms of the frequency of use of these platforms and the relationships between the brand-related activities and the motives behind them. This aim was reached, and the results pointed to areas where further research is necessary to better understand the potential implications, in particular research into the established relationships, the implementation of research into other markets using the same research design, and the inclusion of other social media platforms.

The state of knowledge of social media as such is no longer in its infancy, but given the complexity of this virtual space, new concepts are constantly emerging to be explored and their relevance verified (or dismissed). Currently lagging behind the most in this context is social media research in the context of personal, behavioural or psychological penetration to consumers. This knowledge is not yet at a level that managers can easily translate into practice. They need to know where and how the effect of specific motivations show up, because the future of marketing, according to [37] and [38], lies in a personal, even intimate approach, and with communication with the consumer. This is not helped much by the hard data provided by analytics software. Our study advances the level of understanding of the four identified types of motivations that can help with this need for personal communication between the brand and the consumer.

A potential limitation of this study is that the data collection, although conducted electronically, took place during the period of the widespread pandemic COVID-19, when consumers’ normal behaviour differed to some extent from their routine, and hence the question of a time-lagged comparative study is also relevant. We also did not consider the aspect of the device through which respondents visit these platforms, in the study. Each device tends to lead to a different pattern of behaviour and use of these tools, and so this is an important limitation to consider. The method of data collection by questionnaire on such a large scale is challenging, and limits or hinders the possibilities of investigating the above-mentioned patterns on a large scale, internationally or globally. However, the current state of the questionnaire allowed for better moderation of the occurrence of potential errors and mistakes in completing the questionnaire. The study is reliant on self-reported measurements, which must also be seen as a form of its limitation, as, despite extensive instructions on how to complete the questionnaire, we cannot fully control for the factor of subjective judgement.

Future research should answer the question of what role cultural factors play in these relationships and differences and, as noted above, what role the access device plays in these patterns. Therefore, a planned dimension of the research is also the implementation of an investigation of the impact of the factor of the device used (mobile, tablet, PC). Even though the research results cannot be generalized, nor to be conducted globally, due to the demographics of the sample, future research will address this challenge by sequentially examining the given context on a market-by-market basis. As we gain more knowledge on the topic, we also expect to be able to streamline the conduct of research and open up opportunities for larger-scale research. this is one of the first steps towards understanding the problem as a whole. Given the increasing uptake of social media, there is a need to identify individual differences that predict the different consequences of social media, as well as to incorporate the findings into an integrative framework or broader conceptualization.