Rodney Graeme Duffett (Department of Marketing, Cape Peninsula University of Technology, Cape Town, South Africa)

ISSN: 1066-2243

Article publication date: 3 August 2015

110398

## Abstract

### Purpose

The purpose of this paper is to investigate the influence of behavioural attitudes towards the most popular social medium in the world, Facebook, amongst Millennials in South Africa (SA), and to determine whether various usage and demographic variables have an impact on intention-to-purchase and purchase perceptions.

### Design/methodology/approach

Quantitative research was conducted by means of a survey among a sample of over 3,500 respondents via self-administered structured questionnaires in SA. A generalised linear model was used to analyse the data.

### Findings

The results confirm that advertising on Facebook has a positive influence on the behavioural attitudes (intention-to-purchase and purchase) of Millennials who reside in SA. The usage characteristics, log on duration and profile update incidence, as well as the demographic influence of ethnic orientation also resulted in more favourable perceptions of Facebook advertising.

### Research limitations/implications

Research on Facebook advertising was only conducted in SA, whereas other emerging countries warrant further investigation to establish if they share the slight positive sentiment towards intention-to-purchase and purchase. This inquiry only provides a “snap shot” of behavioural attitudes, usage and demographic factors towards social media advertising, whereas future research could consider the development of cognitive, affective and behavioural attitudes towards Facebook advertising by employing longitudinal and qualitative research designs.

### Practical implications

Organisations and managers should consider that their existing Facebook advertising strategies may only have a limited effect on intention-to-purchase and purchase in SA. However, certain usage characteristics, namely the more time spent logged on to Facebook and the greater frequency of profile update incidence, as well as the demographic variable, namely black and coloured Millennials, resulted in more favourable behavioural attitudes towards Facebook advertising. Hence, organisations and managers should be prepared to alter or adapt their Facebook advertising tactics accordingly when targeting the notoriously fickle Millennials.

### Originality/value

This investigation found that Facebook advertising has a nominal positive influence on behavioural attitudes among Millennials, which is in congruence with the communications of the effect pyramid model that was established through traditional advertising research. This paper also makes a noteworthy contribution to attitudinal research in emerging countries where there is a dearth of research in social media advertising.

## Citation

Duffett, R.G. (2015), "Facebook advertising’s influence on intention-to-purchase and purchase amongst Millennials", Internet Research, Vol. 25 No. 4, pp. 498-526. https://doi.org/10.1108/IntR-01-2014-0020

## Publisher

:

Emerald Group Publishing Limited

Copyright © 2015, Authors. Published by Emerald Group Publishing Limited. This work is published under the Creative Commons Attribution (CC BY 3.0) Licence. Anyone may reproduce, distribute, translate and create derivative works of the article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licenses/by/3.0/legalcode .

## 1. Introduction

Technological innovation has grown at an unprecedented rate over the past couple of decades, especially in terms of online social media platforms. Accordingly, Millennials (born between 1982 and 1994) have been exposed to an explosion of online technological applications since their advent, as these have been incorporated into nearly every facet of their daily existence. In fact, this cohort has not experienced the world without digital interactive technology. Moreover, technology diversification drives universal homogeneity among Millennials, resulting in a hypothetical global cohort that purportedly displays analogous attitudes and behaviour (Lingelbach et al., 2012; Moore, 2012). Yet, many articles have characteristically concentrated on the social media attitudes and usage of Millennials who reside in developed countries with unhindered access to social media and information technology. Hence, Bolton et al. (2013) maintain that social media attitudes and usage may differ among Millennials from emerging countries when compared to their wealthier counterparts owing to technological infrastructure and different cultures. Nonetheless, Millennials’ social media usage are of particular interest to organisations and managers’ since it may provide an indication of how these consumers will behave in the future, as well as what their perceptions are towards their brands (Bolton et al., 2013). Consequently, this study aims to establish if Facebook advertising is effective at realising the top communication of effects pyramid objectives, intention-to-purchase and purchase (the behavioural attitude), among the Millennial cohort.

However, Bolton et al. (2013) believes that previous research on social media among Millennial users results in more questions than answers. Bolton et al. (2013) suggest that prior research predominantly focused on US social media users, disregarding other emerging regions with rapidly growing Millennial populations, where the use of social media and its determinants might differ considerably. Accordingly, this study focused on social media users in South Africa (SA), where nearly 25 per cent of the population are deemed to be Millennials (Statistics South Africa (SA), 2012). Furthermore, Bolton et al. (2013) noted that a majority of studies focused on student populations whose behaviour may change as they progress though the different stages of their life cycle. A broad spectrum of the Millennial cohort was surveyed, which comprised of young working adults and individuals who were still seeking employment from both rural and urban regions. Moreover, participants from both advantaged (suburbs) and disadvantaged (townships) communities were also included, instead of only utilising a student population. Bolton et al. (2013) also questioned whether there were noticeable differences among Millennial subgroups in their use of social media. Thus, the influence of a range of usage characteristics and demographic factors within the Millennial subgroups were also investigated in this study. Furthermore, Facebook revenue from advertising has grown by 59 per cent during the past year to over $5.4 billion in 2014 (Facebook, 2014a), which is testament to the shift from traditional media advertising to digital interactive media advertising by organisations. It is estimated that Millennials will have a combined purchasing power of$2.45 trillion world wide by 2015. It can be assumed that social communications in the form on online reviews, posts and word-of-mouth (WOM) will play a large part in driving purchase decisions (Priyanka, 2013). Therefore, it is imperative that organisations have a complete understanding of the behavioural attitudes of this target market, especially in terms of usage characteristics and demographic factors that can be identified by Facebook Insight metrics (Facebook, 2014b) so that they can use their marketing communications budgets effectively by targeting those Millennials that yield the greatest behavioural response.

## 2. Literature review

### 2.1. Social media background

The rapid growth of social media platforms has permanently altered the way that numerous consumers interact with each other and organisations. Hence, this has changed the way that organisations attract and retain prospective consumers (Leung et al., 2015). Previously, marketers would create captivating advertising messages and purchase space in the mass media in the hope that consumers would become aware of and develop a preference to and purchase the brand. Social media has irrevocably altered marketing communications by shifting ways in which consumers select, share and appraise information. With the advent of social media, traditional media such as television and newspapers have lost uninterrupted viewership and readership, and their influence as advertising channels may have been weakened. The speed of online communication and numerous information sources make advertising on traditional sources less relevant. Furthermore, marketers quickly realised the influence of social community in terms of interactivity that comprises of personalised sections, shopping experiences, greater convenience and widespread information search (Chandra et al., 2012; Patino et al., 2012; He and Zha, 2014).

Consequently, marketers are increasing their social media budgets with digital interactive advertising forecasted to reach $138 billion in 2014, a growth rate of nearly 15 per cent in comparison to 2013 (eMarketer, 2014a). Furthermore, the Middle East and Africa are predicted to have the highest social media advertising spend growth (64 per cent) in 2014 (eMarketer, 2014c). Business-to-consumer (B2C) ecommerce revenue is expected to reach$1.5 trillion in 2014 (an increase of 20 per cent), with growth primarily coming from emerging markets (eMarketer, 2014b). Current figures reveal that the largest online social medium in the world is Facebook, with 1.32 billion active members, and it is also the largest social commerce site that accounts for 85 per cent of all orders from social media (Facebook, 2014a; Shopify, 2014). The aforementioned evidence necessitates research into behavioural attitudes towards Facebook in an emerging country, namely, SA, which will be of interest to managers and their organisations.

### 2.3. Millennials cohort

Millennial (Echo boomers, Generation Y, hip-hop, kwaito or Facebook generation) consumers are the children of the Baby Boomers or Generation X (Dotson and Hyatt, 2005; Berndt, 2007). Most of the discussion is based on international studies, with some commentary on Millennial consumers in SA, although cohort research is deemed to be transnational. Millennial consumers enjoy communication, since they are self-expressive and support freedom of speech, as well as accept change and are even deemed to be trendsetters (Lingelbach et al., 2012; Moore, 2012; Bolton et al., 2013). Millennials are always connected and connect with one another via the latest technologies (Goldenberg, 2007). The black Millennials cohort encompasses a significant portion of the South African market, especially those who are studying at tertiary institutions, as they represent a particularly lucrative target market owing to the fact that higher education is correlated with increased earning potential (Bevan-Dye et al., 2012). Most Millennial members would have first encountered computers as toddlers and embraced the interconnectedness of the internet, mobile devices and social media social network sites (SNS) as part of their interactive world. The duo of interconnectivity and being tech savvy reveals the huge influence of Millennials’ predisposition to connect continuously and easily to multiple social network channels that are embraced for purchase decisions and to initiate electronic WOM (Noble et al., 2009).

### 2.4. Attitudes and hierarchy response model

Belch and Belch (2012) assert that there are three attitudinal stages or components, which are encapsulated in the tricomponent attitude model: cognitive component (an individual’s beliefs regarding an object), affective component (an individual’s feelings towards the object that may be positive or negative) and the behavioural component (an individual’s readiness to respond to the object in the form of behaviour).

Lavidge and Steiner (1961) diverged from prior early hierarchy response model development, since they believed that immediate sales was an insufficient factor of advertising effectiveness, even if it was measurable. They posited that advertising was an enduring investment, which was mainly owing to the long-term nature of advertising effects that resulted in the development of the hierarchy-of-effects model. Hence, it was inconceivable that consumers moved from a stage of total disinterest to eager purchasers; but instead moved through a sequence of steps until purchase. These steps are as follows: unawareness of the brand’s existence, awareness, knowledge of what the brand offers (awareness and knowledge form the cognitive attitude component), consumers like the brand (a favourable affective attitude), consumers prefer the brand over others (a favourable affective predisposition) and have a desire to purchase the brand and conviction that it would be a wise purchase that leads to purchase intent, and finally culminating in the actual purchase (behavioural attitude component). The steps of the hierarchy-of-effects model are analogous to the communications of effect pyramid (also known as the purchase funnel) that was mentioned in prior text. It becomes progressively more difficult to achieve the upper level stages and, hence, the number of prospective consumers decreases as they progress through the latter stages of the pyramid (Safko, 2010; Belch and Belch, 2012).

In summary, there have been a number of recent studies that assessed behavioural attitudes towards SNA, but these were predominantly conducted in more developed nations; utilised students as the research population; used relatively small sample sizes; and few explored the effect of usage characteristics and demographic factors on Facebook advertising.

## 3. Research objectives

RQ1. Does Facebook advertising have an effect on intention-to-purchase among South African Millennials?

RQ2. What impact does advertising on Facebook have on purchase amid Millennials in SA?

Second, to establish if usage factors, which include how Facebook is accessed (as mentioned previously, 87 per cent of Facebook users in SA access this social medium via mobile phones; Wronski and Goldstruck, 2013), length of usage, log on duration, log on frequency and profile update incidence, have an influence on Millennials’ intention-to-purchase and purchase perceptions of advertising on Facebook. This research objective is of interest to both managers and academics, since it will provide insight into Millennials’ social media usage characteristics, and whether these affect their behavioural attitudes. Ultimately, this objective will provide a greater understanding of Millennials’ future consumer behaviour. Moreover, little research has been conducted to determine if the various usage characteristics have an influence on the impact of Facebook advertising behavioural attitudes among Millennial users, which will add to the conceptual framework of attitudinal research in social media. Chandra et al. (2012) determined that more frequent social media users exhibited a favourable attitude towards SNA, as it assisted with buying decisions; Punj (2011) found that internet usage levels influenced belief-behavioural responses; and Taylor et al. (2011) established that many social media users utilise SNS as part of their everyday routine, which may result in an elevated prospect of consumers perceiving SNA more favourably. Therefore, the RQ for the second objective are as follows:

RQ3. What influence do South African Millennial usage variables have on intention-to-purchase owing to Facebook advertising?

RQ4. Do usage characteristics of Millennials in SA have an impact on purchase as a result of Facebook advertising?

Third, to determine if demographic factors (gender, age and ethnic orientation) have an impact on Millennials’ intention-to-purchase and purchase perceptions of Facebook advertising. This objective will reveal whether there are noticeable differences within Millennial subgroups, as mandated by Bolton et al. (2013), with regard to their attitudes towards Facebook advertising. Additionally, the ethic orientation analysis is of particular interest to managers and academics owing to the well-known injustices of the past that took place in SA, which resulted in a substantial economic divide. Furthermore, few studies have investigated the effect of demographic factors, especially age (within a particular cohort) and ethnic orientation, on attitudinal research. Ruane and Wallace (2013) established that Facebook yielded favourable behavioural attitudinal responses among Millennial women; while Punj (2011) determined that different demographic characteristics influenced behavioural activities; and Wang and Sun (2010) revealed that ethnic factors had an impact on behavioural responses. Hence, the RQ for the third objective include the following:

RQ5. Do demographic factors have an effect on intention-to-purchase among South African Millennials owing to Facebook advertising?

RQ6. What effect do demographic variables have on purchases that are attributable to advertising on Facebook amongst Millennials in SA?

## 4. Methodology

### 4.1. Research design

A research design is a plan, structure and strategy of investigation, which is conceived to obtain answers to RQ or problems. A research design is a procedural plan that is adopted by the researcher to answer questions validly, objectively, accurately and economically (Kumar, 2011). Descriptive research is concerned with the current status of the phenomena to acquire a better understanding of the existing situation, but disregards the cause of the research problem (Tustin et al., 2005). As implied by its name, this research method describes the characteristics of groups and people (Zikmund and Babin, 2007). Descriptive research typically takes a cross-section of a population, in this instance Millennials who reside in the Western Cape, and reveals their predisposition at a given point in time (behavioural attitudes towards Facebook advertising) on which the research can be built. Survey methods are typically associated with descriptive research (Hair et al., 2009). A measurement instrument (typically a questionnaire) is employed to take a snap shot (cross-section) of independent (usage characteristics and demographic factors) and dependent (intention-to-purchase and purchase) variables of a given research population by means of a sample at a given point in time. The main advantage of a survey is its capability of collecting a large quantity of data (Bhattacherjee, 2012), whereas the main disadvantages are its high cost and that fieldworkers should be well trained (Maree, 2007). Hence, structured self-administered questionnaires were distributed on a face-to-face basis to collect the required data for this study.

### 4.2. Sampling

Young adults (Millennials) are the predominant users of online digital applications such as SNS (Du Chenne, 2011; Smith, 2012; Bolton et al., 2013; Wronski and Goldstruck, 2013). Students were selected to investigate attitudes towards SNA and attitudes by a majority studies (Molnár, 2011; Orpana and Tera, 2011; Vanden Bergh et al., 2011; Bannister et al., 2013; Persuad, 2013). Yet, the researcher believed that it was imperative to select a sample that included a broader spectrum of Millennials, as mandated by Bolton et al. (2013) owing to the consumer behavioural changes that occur as young adults pass though the phases of their natural life cycle. Hence, the research population comprised of young employed individuals, students and young adults who were still seeking employment. The unemployment rate in SA is in the region of 30 per cent, and is much higher among young adults (up to 50 per cent) (Statistics SA, 2012). Furthermore, young adults were surveyed in both rural and urban areas, which encompassed wealthy suburbs and disadvantaged township communities to ensure a representative sample.

A sample frame is a record of all the sample units that are available for selection at a given stage in the sampling process (Martins et al., 1996; Zikmund, 2003; Aaker et al., 2004). The Western Cape was selected to collect data, which represents a little over 11 per cent of the South African population (Statistics SA, 2012). The study utilised a quasi-probability sample in the form of a multi-stage sampling technique, whereby, as mentioned in prior text, the Western Cape was selected from the nine provinces in SA. Various geographic areas (clusters), which included suburban (characteristically wealthier areas) and townships (which includes informal settlements) in both urban and rural locations, were identified by means of a map. Thereafter, commercial and community organisations (sports clubs, youth groups, churches and other local groups), as well as tertiary education institutions, were randomly selected via listings in a regional telephone directory. Next, these organisations were telephoned to obtain approval to carry out the empirical research and to ascertain whether there were an adequate number of Millennials to survey. Systematic sampling is a process whereby a random starting place is determined, followed by every kth element being selected by moving through the sample frame (Maree, 2007; Bhattacherjee, 2012). This sampling technique was used to survey participants in the aforementioned organisations, with every third participant invited to voluntarily partake in the research.

### 4.4. Data analysis

Data analysis typically entails the editing and reduction of data into more manageable portions in order to create summaries, detect patterns and apply statistical methods with the express purpose of interpreted data to answer the RQ at hand (Blumberg et al., 2011; Bhattacherjee, 2012). The data were captured and examined via statistical software known as SPSS (version 21). However, all of the questionnaires were first meticulously examined in terms of correctness and completeness to establish whether they should be incorporated in the statistical analysis – the Likert scale statements were organised in such a manner that alternated positive and negative statements so as to circumvent participants from choosing a single column. These questions were reversed via SPSS before the reliability of the responses was established for the measurement scales. Reliability signifies the internal consistency of the items that were developed to measure a specific construct with a high level of reliability, in other words, the intention-to-purchase and purchase measurement scales. The coefficient mechanism that was used to determine reliability is known as Cronbach’s α, and reliability estimates of 0.7 and above are deemed to be acceptable (George and Mallery, 2003; Hair et al., 2009; Maree, 2007). Hence, items that are negatively worded in the scales must have their scores reversed; otherwise they would have an adverse effect on Cronbach’s α result (Field, 2009). Simple descriptive statistical analysis measures (means, standard deviations, frequencies and non-parametric standardised tests) were employed to provide a basic description of the results (Tables II, III and IV). Validity refers to the extent to which an instrument measures the construct that it is supposed to measure (Blumberg et al., 2011; Bhattacherjee, 2012). To ensure validity, existing measurement scales, as mentioned in prior text, were adapted and then tested before being utilised to assess the constructs. Furthermore, Pearson’s correlation coefficient analysis (Tables III and IV) was used to examine and measure the linear strength of relationships between quantitative variables (Maree, 2007, Field, 2009). Analysis of variance (ANOVA) is utilised when two independent variables or more need to be compared to an individual quantitative score (Maree, 2007). ANOVA used Wald’s χ2 and was conceptualised as a Generalised Linear Model (GLM) to establish if there were significant differences between the usage characteristics and demographic factors (predictor variables) and behavioural attitude components (dependent variables). The post-ad-hoc Bonferroni pairwise comparison was utilised to establish where the differences were, so that the findings could be interpreted conclusively (Field, 2009; Bhattacherjee, 2012).

## 5. Results

The survey included 3,521 members of the Millennials cohort in the Western Cape. Facebook was accessed by a majority of respondents (64.5 per cent) via both PC and mobile device; over 60 per cent logged on to Facebook everyday; spent one (58.5 per cent) to two (22.8 per cent) hours per log on; and more than 72 per cent updated their profile at least of once a week. The sample included a slight majority of females (54.8 per cent); and the ethnic groups accurately portrayed the ethnicity of the Western Cape, including primarily black (35.2 per cent) and coloured (36.4 per cent) ethnic groups (Statistics SA, 2012). Table II offers a full overview of the usage characteristics and demographics of Millennials respondents that use Facebook.

As previously mentioned, the respondents’ behavioural attitude towards Facebook advertising was computed by nine-item scales for each of the hierarchy response levels (Tables III and IV).

Cronbach’s α was 0.843 for the Facebook advertising intention-to-purchase scale (Table III) and 0.742 for the Facebook advertising purchase scale (Table IV), which indicated good internal consistencies. A non-parametric one-sample bi-nominal standardised test was utilised to determine if there was a significant difference. The test showed that for both of the nine-item scales, there was a significant difference at p < 0.001 and p < 0.05, with the exception of one item in the intention-to-purchase scale. Pearson’s correlation coefficient analysis (Tables III and IV) showed a positive medium (r > 0.3) to strong (r > 0.5) relationship between a majority of the variables for the intention-to-purchase and purchase measurement scales, but there was weak positive correlation between a minority of the variables, especially in terms of the negatively reversed variables that were recoded.

The GLM ANOVA, as discussed in prior text, was used since the data contains a different number of observations for certain independent variables, which can be seen by the larger standard errors (an example of this is the low number of respondents that logged on to Facebook at least once a month). Van Schalkwyk (2012) discloses that the GLM takes this into consideration and “normalises” the outcomes. Tables V and VI show the effect in terms of Wald χ2 test, which is based on the Bonferroni correction pairwise post hoc test among the estimated marginal means.

The Wald χ2 test revealed that there was a significant difference at p < 0.001 for intention-to-purchase (M=2.94, SD=0.805) because of Facebook advertising. No significant differences were found for access, length of usage, log on frequency, gender and age, whereas Bonferroni correction pairwise comparisons of estimated marginal means disclosed the significant difference between the next variables.

Log on duration (p < 0.001): respondents who logged on for 1 hour (M=2.82, SE=0.033) resulted in lower intention-to-purchase levels in comparison to those who logged on for two hours (M=2.98, SE=0.039).

Profile update incidence (p < 0.001): respondents who updated their Facebook status daily (M=3.06, SE=0.041) resulted in greater intention-to-purchase compared to those who updated once a week (M=2.93, SE=0.044), two to four times a month (M=2.81, SE=0.050) and once a month (M=2.81, SE=0.042); those who updated their Facebook status two to four times a week (M=2.98, SE=0.043) showed an increase in intention-to-purchase compared to those who updated it two to four times a month (M=2.81, SE=0.050) and once a month (M=2.81, SE=0.042).

Ethnic group (p < 0.001): white respondents (M=2.79, SE=0.041) exhibited lower intention-to-purchase levels than black (M=3.01, SE=0.035) and coloured (M=2.96, SE=0.037) respondents.

The Wald χ2 test disclosed that there was a significant difference at p < 0.001 for purchase (M=2.94, SD=0.656), which was caused by Facebook advertising. No significant differences were found for access, length of usage, log on frequency, age, gender and race; however, Bonferroni correction pairwise comparisons of estimated marginal means showed significant difference amongst the following variables.

Log on duration (p < 0.001): respondents who logged on for one hour (M=2.85, SE=0.027) exhibited lower purchase levels compared to those who remained logged on for two hours (M=3.01, SE=0.032) and four hours (M=3.06, SE=0.056).

Profile update incidence (p < 0.001): respondents who updated their Facebook status daily (M=3.07, SE=0.034) resulted in higher purchase incidence in comparison to those who updated once a week (M=2.97, SE=0.036), two to four times a month (M=2.88, SE=0.041) and once a month (M=2.86, SE=0.035); those who updated their Facebook status two to four times a week (M=3.01, SE=0.035) showed increased intention-to-purchase levels compared to those who updated two to four times a month (M=2.88, SE=0.041) and once a month (M=2.86, SE=0.035).

## 6. Discussion and implications

### 6.1. Key findings

The second objective of the research was to determine if certain usage characteristics had an effect on Millennials behavioural attitudes towards Facebook advertising. The research revealed that advertising on Facebook was most effective when Millennials spent two or more hours on Facebook per log-in session, which is a logical perception, as they would have more opportunity to interact with the advertising. Young adults have a high propensity towards multi-tasking and mobile devices, which enable them to be continuously on the move, while accessing the internet; SNS; television; and communication via text, graphics and verbally; as well as searching for consumer-related information to make purchase decisions (Crosman, 2008).

This study confirmed that Millennial members who update their profile on Facebook more prolifically facilitated increased positive behavioural attitudes. This is a reasonable notion, since increased activity on Facebook should lead to greater activity with other elements such as advertising. This finding is also congruent with Chandra et al. (2012) who found that regular users displayed a positive attitude towards SNA, since it aided purchasing decisions.

No significant differences were revealed in terms of length of usage, log on frequency and how Facebook was accessed. This is an unexpected result, since Wronski and Goldstruck (2013) disclosed that almost nine out of ten Facebook users access Facebook via mobile phones. Facebook mobile advertising was launched in 2012 and received click-through rates of up to 13 times greater than other advertisements on Facebook (Bischoff, 2012). Dynamic Logic (2012) indicated that intention-to-purchase was almost four times higher for mobile advertising that resulted in higher average click-through rates. Hence, it is apparent that Millennials in SA have divergent sentiments in comparison to their international counterparts.

The third objective of this investigation was to examine whether particular demographic factors had an impact on Millennials behavioural attitudes towards Facebook advertising. This investigation discovered that the white population group exhibited lower levels of intention-to-purchase compared to the black and coloured ethnic groups. Internet access has grown significantly among the coloured (35.7 per cent) and black (29.4 per cent) ethnic groups in recent years, but they are still catching up to the white (70.3 per cent) ethnic group (Statistics SA, 2012). The proliferation of the black middle class, categorised as the Black Diamonds by TNS Research Surveys and the UCT Unilever Institute of Strategic Marketing (Olivier, 2007), has resulted in greater spending power (R400 billion per annum), with the monthly income of black households increasing by 34 per cent since 2004. SA’s black middle class has risen by nearly 250 per cent from 1.7 million in 2004 to 4.2 million in 2012 (Shevel, 2013). Consequently, a large proportion of black middle class young adults have gained internet access over the past decade, whereas many white young adults grew up with the internet and, subsequently, had more exposure and experience to SNS advertising.

No significant differences were found in terms of age and gender having an impact on Millennials’ intention-to-purchase and purchase perceptions of Facebook advertising. Bannister et al. (2013) reported that women had a slightly more positive attitude to Facebook advertising, whereas Taylor et al. (2011) found that young adults (aged 19-24 years old) maintained the most positive attitudes to SNS advertisements. Hence, it is apparent that there are not many noticeable differences within the South African Millennial cohort besides ethnic orientation.

### 6.2. Implications for theory

This inquiry found that log on duration and profile update incidence had an influence on South African Millennials’ intention-to-purchase and purchase perceptions of advertising on Facebook, whereas how Facebook was accessed, length of usage and log on frequency had no influence. Punj (2011) established that internet usage frequency affected behavioural activities; and Taylor et al. (2011) observed that many consumers use SNS to overcome boredom or to use up time between activities; they also frequently use SNS as part of their everyday routine. This habitual activity may increase the prospect that consumers would perceive would SNA positively, since it may provide an added diversion and an extra means of form of time structuring, which is in consensus with the results of this investigation. Wang and Sun (2010) determined that ethnic variables had an influence on behavioural attitudinal responses; and Jordaan et al. (2011) advocate that different ethnic groups in SA should be investigated to establish whether there was a difference in terms of online intent and purchase. Consequently, this study found that ethnic orientation had a positive impact on the behavioural attitudes of black Millennials in SA, but no effect on age and gender. These results make a noteworthy contribution to the theoretical framework of attitudinal research in SNS marketing communications, since there is a dearth of research on the effect of abovementioned usage and demographic factors on the upper levels of communications of effect pyramid model.

### 6.3. Managerial implications

Vis-à-vis the second research, this investigation revealed that advertising on Facebook resulted in a diminutive, but noteworthy favourable attitudes towards purchase among the Millenial cohort. Marketers should take into consideration that cheap costs, fast service, high quality and an “experience” are important factors that influence Millennials’ purchase considerations. Facebook advertisements that are connected to a physical in store promotion may actively draw SA Millennials who are not inclined to make online purchases to the actual store to purchase. Facebook’s location point tracking systems can also be used to display local stores’ promotions based on the interests of the Millennials’ location. Chandra et al. (2012) revealed that SNS assists in making the final purchase decision and resulted in lowing prices. However, it should be noted that Millennials have generally not yet established enduring consumer behaviour patterns and do spend freely, since many are students or unemployed who have limited resources, with 45 per cent agreeing that they purchase brands on sale as opposed to their preferred brands, which would dampen their purchase sentiments (Symphony, 2013). This finding vindicates the decision by South African organisations and managers to spend large percentages of their advertising budgets on Facebook marketing communications.

SNS is a rapidly growing marketing communication tool, but it is up to marketers to recognise that the expectations, needs and wants of Millennial consumers are continually changing and hence this should be taken into consideration when using SNA to favourably influence this cohort’s behavioural predisposition.

## 7. Limitations and directions for future research

This investigation has some limitations and also lends itself to additional research. The inquiry did not take the various types of Facebook advertising into consideration; therefore, it is suggested that further research should be conducted to determine whether there was a difference in attitudinal effectiveness between the various advertising forms. Only the behavioural attitude and a single SNS was surveyed, whereas future studies could examine other attitude components, as well as other widely used SNS such as YouTube, Google+, LinkedIn and Twitter. Cognitive and affective attitudes warrant further research, since consumers’ first need to become aware and be informed of an organisation’s products and develop favourable emotional bonds before they can progress to behavioural activities. Like this study, surveys, which constituted a cross-section of attitudes were previously used in cognitive and affective attitudinal research. Hence, a longitudinal approach would yield more complete results, as inferred by Kalampokis et al. (2013) and Schoen et al. (2013). This study utilised quantitative data, as have past inquiries on cognitive and affective attitudinal components, whereas qualitative research would provide greater insight into Millennials’ attitudes. Future research could also take other countries into consideration, since a developing country with a diverse and rich culture such as SA, may differ from other developing nations.

### Table I

Summary of recent Facebook media literature that investigated behavioural attitudinal research

### Table VI

Dr Rodney Graeme Duffett is a Senior Lecturer at the Faculty of Business, Cape Peninsula University of Technology (CPUT), Cape Town, South Africa. He is currently reading for the Doctor Technologiae Degree in Marketing at CPUT. He has published in African Journal of Business Management, Southern African Business Review and Journal of Contemporary Management. His research interests focus on any form of e-advertising, social media and black economic empowerment in the advertising industry. Dr Rodney Graeme Duffett can be contacted at: duffetr@cput.ac.za

## References

Aaker, D.A. , Kumar, V. and Day, G.S. (2004), Marketing Research , 8th ed., John Wiley, New York, NY.

Associated Press and CNBC (2012), “Is there a problem with Facebook advertising?”, available at: www.emarketer.com/Article.aspx?R=1009065 (accessed 1 July 2013).

Bannister, A. , Kiefer, J. and Nellums, J. (2013), “College students’ perceptions of and behaviours regarding Facebook advertising: an exploratory study”, The Catalyst , Vol. 3 No. 1, pp. 1-20.

Barreto, A.M. (2013), “Do users look at banner ads on Facebook”, Journal of Research in Interactive Marketing , Vol. 7 No. 2, pp. 119-139.

Belch, G.E. and Belch, M.A. (2012), Advertising and Promotion: An Integrated Marketing Communication Perspective , 9th ed., McGraw-Hill, New York, NY.

Berndt, A. (2007), “Media habits among generation Y consumers”,Proceedings of the 19th Annual Conference of the Southern African Institute of Management Scientists, Johannesburg, 19-21 September, pp. 3-16.

Bevan-Dye, A.L. , Garnett, A. and de Klerk, N. (2012), “Materialism, status consumption and consumer ethnocentrism amongst black generation Y students in South Africa”, African Journal of Business Management , Vol. 6 No. 16, pp. 5578-5586.

Bhattacherjee, A. (2012), Social Science Research: Principles, Methods, and Practices , USF Tampa Bay Open Access Textbooks, Tampa, FL.

Bischoff, W. (2012), “Mobile advertising now on Facebook in South Africa”, available at: www.bizcommunity.com/Article/196/12/78197.html (accessed 10 April 2014).

Blasco-Arcas, L. , Hernandez-Ortega, B. and Jimenez-Martinez, J. (2014), “The online purchase as a context for co-creating experiences. Drivers of and consequences for customer behaviour”, Internet Research , Vol. 24 No. 3, pp. 211-242.

Blumberg, B. , Cooper, D.R. and Schindler, P.S. (2011), Business Research Methods , 3rd ed., McGraw-Hill, London.

Bolton, R.N. , Parasuraman, A. , Hoefnagels, A. , Migchels, N. , Kabadayi, S. , Gruber, T. , Loureiro, Y.K. and Solnet, D. (2013), “Understanding Generation Y and their use of social media: a review and research agenda”, Journal of Service Management , Vol. 24 No. 3, pp. 245-267.

Burns, A.C. and Bush, F.R. (2012), Basic Marketing Research , 3rd ed., Pearson Education, Upper Saddle River, NJ.

Carrillat, A.F. , d’Astous, A. and Grégoire, E.M. (2014), “Leveraging social media to enhance recruitment effectiveness: a Facebook experiment”, Internet Research , Vol. 24 No. 4, pp. 86-123.

Chandra, B. , Goswami, S. and Chouhan, V. (2012), “Investigating attitude towards online advertising on social media – an empirical study”, Management Insight , Vol. 8 No. 1, pp. 1-14.

Crosman, P. (2008), “Attracting young investors – financial firms are embracing mobile technology, Web 2.0 tools and social networking principles to reach Gen X and Gen Y”, Wall Street and Technology , Vol. 26, p. 16.

Denscombe, M. (2010), The Good Research Guide , 3rd ed., McGraw-Hill, Berkshire.

Digital Fire (2012), “Social media marketing in Africa”, available at: www.bizcommunity.com/Article/196/16/73947.html (accessed 3 July 2013).

Dotson, M.F. and Hyatt, E.M. (2005), “Major influence factors in children’s consumer socialisation”, Journal of Consumer Marketing , Vol. 22 No. 3, pp. 35-42.

Du Chenne, S. (2011), “High on aspiration, but cynical”, AdReview , Vol. 28, April, pp. 48-51.

Du Plooy, G.M. (2009), Communication research: Techniques, Methods and Applications , Juta, Cape Town.

Dynamic Logic (2012), “Mobile video bumps up health brand metrics”, available at: www.emarketer.com/Article.aspx?R=1009490 & ecid=a6506033675d47f881651943c21c5ed4 (accessed 4 July 2013).

Edwards, S.M. (2011), “A social media mindset”, Journal of Interactive Advertising , Vol. 12 No. 1, pp. 1-3.

eMarketer (2012), “Social media key influencer in multi-exposure purchase path”, available at: www.emarketer.com/Article.aspx?R=1008845 & ecid=a6506033675d47f881651943c21c5ed4 (accessed 4 July 2013).

eMarketer (2014a), “Digital ad spending worldwide to hit $137.53 billion in 2014”, available at: www.emarketer.com/Article/Digital-Ad-Spending-Worldwide-Hit-3613753-Billion-2014/1010736/8 (accessed 7 April 2014). eMarketer (2014b), “Global B2C ecommerce sales to hit$1.5 trillion this year driven by growth in emerging markets”, available at: www.emarketer.com/Article/Global-B2C-Ecommerce-Sales-Hit-15-Trillion-This-Year-Driven-by-Growth-Emerging-Markets/1010575 (accessed 7 April 2014).

eMarketer (2014c), “Social ad spending per user remains highest in North America”, available at: www.emarketer.com/Article/Social-Ad-Spending-per-User-Remains-Highest-North-America/1010505 (accessed 7 April 2014).

Facebook (2014a), “Company info”, available at: https://newsroom.fb.com/company-info/ (accessed 6 August 2014).

Facebook (2014b), “Platform”, available at: https://newsroom.fb.com/Platform (accessed 3 March 2014).

Facebook and ComScore (2012), “Can Facebook go beyond earned media success?”, available at: www.emarketer.com/Article/Facebook-Go-Beyond-Earned-Media-Success/1009127 (accessed 30 June 2013).

Field, A. (2009), Discovering Statistics using SPSS , 3rd ed., Sage, London.

George, D. and Mallery, P. (2003), SPSS for Windows Step by Step: A Simple Guide and Reference , 4th ed., Allyn & Bacon, Boston, MA.

Goldenberg, B. (2007), “The rise of the digital client”, Customer Relationship Management , Vol. 11, p. 12.

Greenlight (2012), “Facebook sponsored advertisements – 44% of people say they would ‘never’ click on them”, available at: www.bizcommunity.com/Article/196/12/75429.html (accessed 10 April 2014).

Ha, H. and Janda, S. (2014), “The effect of customized information on online purchase intentions”, Internet Research , Vol. 24 No. 4, pp. 124-165.

Hadija, Z. , Barnes, S.B. and Hair, N. (2012), “Why we ignore social networking advertising”, Qualitative Market Research: An International Journal , Vol. 15 No. 1, pp. 19-32.

Haigh, M.M. , Brubaker, P. and Whiteside, E. (2013), “Facebook: examining the information presented and its impact on stakeholders”, Corporate Communications: An International Journal , Vol. 18 No. 1, pp. 52-69.

Hair, J.F. , Bush, R.P. and Ortinau, D.J. (2009), Marketing Research , McGraw Hill/Irwin, New York, NY.

Hamidizadeh, M.R. , Yazdani, N. Tabriz, A.A. and Latifi, M.M. (2012), “Designing and validating a systematic model of e-advertising”, International Journal of Marketing Studies , Vol. 4 No. 2, pp. 130-149.

Haydam, N. and Mostert, T. (2013), Marketing Research for Managers , African Paradigms Marketing Facilitators, Cape Town.

He, W. and Zha, S. (2014), “Insights into the adoption of social media mashups”, Internet Research , Vol. 24 No. 2, pp. 21-42.

Hsu, M. , Chuang, L. and Hsu, C. (2014), “Understanding online shopping intention: the roles of four types of trust and their antecedents”, Internet Research , Vol. 24 No. 3, pp. 106-139.

Hudson, S. and Hudson, R. (2013), “Engaging with consumers using social media: a case study of music festivals”, International Journal of Event and Festival Management , Vol. 4 No. 3, pp. 206-223.

Hudson, S. and Thal, K. (2013), “The impact of social media on the consumer decision process: implications for tourism marketing”, Journal of Travel & Tourism Marketing , Vol. 30 Nos 1-2, pp. 156-160.

Internet World Stats (2012), “Internet users and population statistics for Africa”, available at: www.internetworldstats.com/stats1.htm (accessed 28 June 2013).

Jalilvand, M.R. and Samiei, N. (2012), “The impact of electronic word-of-mouth on a tourism destination choice: testing the theory of planned behaviour (TPB)”, Internet Research , Vol. 22 No. 5, pp. 591-612.

Jordaan, Y. , Ehlers, L. and Grove, J.M. (2011), “Advertising credibility across media channels: perceptions of generation Y consumers”, Communicare , Vol. 30 No. 1, pp. 1-20.

JWT Intelligence (2012), “Women’s influence on purchase decisions on the rise”, available at: www.emarketer.com/Article.aspx?R=1008807 (accessed 29 June 2013).

Kalampokis, E. , Tambouris, E. and Tarabanis, K. (2013), “Understanding the predictive power of social media”, Internet Research , Vol. 23 No. 5, pp. 544-559.

Kodjamanis, A. and Angelopoulos, S. (2013), “Consumer perception and attitude towards advertising on social networking sites: the case of Facebook”, Proceedings of International Conference on Communication, Media, Technology and Design, Famagusta, 2-4 May, pp. 53-58.

Kumar, R. (2011), Research Methodology , Sage, Pretoria.

Lavidge, R.J. and Steiner, G.A. (1961), “A model of predictive measurement of advertising effectiveness”, Journal of Marketing , Vol. 25 No. 6, pp. 59-62.

Leung, X.Y. , Bai, B. and Stahura, K.A. (2015), “The marketing effectiveness of social media in the hotel industry: a comparison of Facebook and Twitter”, Journal of Hospitality & Tourism Research , Vol. 39 No. 2, pp. 147-169.

Levin, J. (2013), “Youth marketing”, available at: www.bizcommunity.com/Article/196/347/88005.html (accessed 2 July 2013).

Lingelbach, D. , Patino, A. and Pitta, D.A. (2012), “The emergence of marketing in Millennial new ventures”, Journal of Consumer Marketing , Vol. 29 No. 2, pp. 136-145.

McCarthy, J. , Rowley, J. , Ashworth, C.J. and Pioch, E. (2014), “Managing brand presence through social media: the case of UK football clubs”, Internet Research , Vol. 24 No. 2, pp. 43-75.

Maree, K. (2007), First Steps in Research , Van Schaik, Pretoria.

Martinez-Lopez, F.J. , Luna, P. and Martinez, F.J. (2005), “Online shopping, the standard learning hierarchy, and consumers’ internet expertise”, Internet Research , Vol. 15 No. 3, pp. 312-334.

Martins, J.H. , Loubser, M. and Van Wyk, H. (1996), Marketing Research: A South Africa Approach , Unisa, Pretoria.

Maxwell, J. (2013), “Demystifying the online shopper 10 myths of multichannel retailing”, PWC’s Multichannel Retail Survey , January, pp. 3-35.

Millward Brown (2012), “Rich display and video ads boost purchase intent”, available at: www.marketingmag.com.au/news/rich-display-and-video-ads-boost-purchase-intent-14282/#.U85FBBGKBMs (accessed 8 August 2014).

Mir, I.A. (2012), “Consumer attitudinal insights about social media advertising: a South Asian perspective”, The Romanian Economic Journal , Vol. 15 No. 45, pp. 265-288.

Molnár, G. (2011), “Social technographics profiles of students at University of Pecs”, master dissertation, University of Pecs, Pécs.

Moore, M. (2012), “Interactive media usage among Millennial consumers”, Journal of Consumer Marketing , Vol. 29 No. 6, pp. 436-444.

Noble, S.M. , Haytko, D.L. and Phillips, J. (2009), “What drives college-age generation Y consumers?”, Journal of Business Research , Vol. 62 No. 6, pp. 617-628.

Okazaki, S. and Taylor, R.T. (2013), “Social media and international advertising: theoretical challenges and future directions”, International Marketing Review , Vol. 30 No. 1, pp. 56-71.

Olivier, D. (2007), “South Africa poised to become a loyalty marketing gem”, Journal of Consumer Marketing , Vol. 24 No. 3, pp. 180-181.

Orpana, J. and Tera, J. (2011), “Facebook marketing – what do users think of it?”, bachelor thesis University of Applied Sciences, Turku.

Patino, A. , Pitta, D.A. and Quinones, R. (2012), “Social media’s emerging importance in market research”, Journal of Consumer Marketing , Vol. 29 No. 3, pp. 233-237.

Patwardhan, P. and Ramaprasad, J. (2005), “Rational integrative model of online consumer decision making”, Journal of Interactive Advertising , Vol. 6 No. 1, pp. 2-13.

Persuad, C. (2013), “The effects of interactivity and involvement on users’ attitude toward and perception of brands and purchase intent on Facebook”, master thesis, Louisiana State University, Baton Rouge, LA.

Petzer, D.J. and De Meyer, C.F. (2013), “Trials and tribulations: marketing in modern South Africa”, European Business Review , Vol. 25 No. 4, pp. 382-390.

Powers, T. , Advincula, D. , Austin, M.S. , Graiko, S. and Snyder, J. (2012), “Digital and social media in the purchase decision process”, Journal of Advertising Research , Vol. 52 No. 4, pp. 479-489.

Priyanka, S. (2013), “A study of online advertising on consumer behaviour”, International Journal of Engineering and Management Science , Vol 3 No. 4, pp. 461-465.

Punj, G. (2011), “Effect of consumer beliefs on online purchase behaviour: the influence of demographic characteristics and consumption values”, Journal of Interactive Marketing , Vol 25 No. 3, pp. 134-144.

Putrevu, S. and Lord, R.K. (1994), “Comparative and noncomparative advertising: attitude effects under cognitive and affective involvement conditions”, Journal of Advertising , Vol. 23 No. 2, pp. 77-90.

Rau, P.L.P. , Gao, Q. and Ding, Y. (2008), “Relationship between the level of intimacy and lurking in online social network services”, Computers in Human Behaviour , Vol. 24 No. 6, pp. 2757-2770.

Reuters and Ipsos (2012), “Can Facebook go beyond earned media success?”, available at: www.emarketer.com/Article/Facebook-Go-Beyond-Earned-Media-Success/1009127 (accessed 1 July 2013).

RichRelevance (2013), “On Facebook, retailers tackle how best to drive sales”, available at: www.emarketer.com/Article/On-Facebook-Retailers-Tackle-How-Best-Drive-Sales/1009793 (accessed 2 July 2013).

Rodgers, S. and Thorson, E. (2000), “The interactive advertising model: how users perceive and process online ads”, Journal of Interactive Advertising , Vol. 1 No. 1, pp. 26-49.

Rohm, A. , Kaltcheva, V.D. and Milne, G.R. (2013), “A mixed-method approach to examining brand-consumer interactions driven by social media”, Journal of Research in Interactive Marketing , Vol. 7 No. 4, pp. 295-311.

Ruane, L. and Wallace, E. (2013), “Generation Y females online: insights from brand narratives”, Qualitative Market Research: An International Journal , Vol. 16 No. 3, pp. 315-335.

Safko, L. (2010), The Social Media Bible: Tactics, Tools & Strategies for Business Success , 2nd ed., Wiley, Hoboken, NJ.

Saxena, A. and Khanna, U. (2013), “Advertising on social network sites: a structural equation modelling approach”, Vision , Vol. 17 No. 1, pp. 17-25.

Schoen, H. , Gayo-Avello, D. , Metaxas, P.T. , Mustafaraj, E. , Strohmaier, M. and Gloor, P. (2013), “The power of prediction with social media”, Internet Research , Vol. 23 No. 5, pp. 528-543.

Shevel, A. (2013), “Black Diamonds outshine whites”, available at: www.bdlive.co.za/national/2013/04/28/black-diamonds-outshine-whites (accessed 20 November 2013).

Shopify (2014), “Facebook is no. 1 for social commerce”, available at: www.emarketer.com/Article/Facebook-No-1-Social-Commerce/1010721 (accessed 7 April 2014).

Shu, W. and Chuang, Y. (2011), “The perceived benefits of six-degree-separation social networks”, Internet Research , Vol. 21 No. 1, pp. 26-45.

Smith, K.T. (2012), “Longitudinal study of digital marketing strategies targeting Millennials”, Journal of Consumer Marketing , Vol. 29 No. 2, pp. 86-92.

Smith, S. (2013), “Conceptualising and evaluating experiences with brands on Facebook international”, Journal of Market Research , Vol. 55 No. 3, pp. 357-374.

Statistics South Africa (SA) (2012), Census 2011: In Brief, Statistics , Statistics SA, Pretoria.

Stevenson, J. , Bruner, G. and Kumar, A. (2000), “Webpage background and viewer attitudes”, Journal of Advertising Research , Vol. 40 No. 1, pp. 29-34.

Stokes, R. (2013), eMarketing: The Essential Guide to Marketing in a Digital World , 5th ed., Quirk Education, Cape Town.

Symphony (2013), “Digital-first Millennials put a premium on value engagement”, available at: www.emarketer.com/Article/Digital-First-Millennials-Put-Premium-on-Value-Engagement/1009946 (accessed 1 July 2013).

Taylor, D.G. , Lewin, J.E. and Strutton, D. (2011), “Friends, fans, and followers: do ads work on social networks”, Journal of Advertising Research , Vol. 51 No. 1, pp. 258-275.

Taylor, S.A. and Hunter, G.L. (2002), “The impact of loyalty with e-CRM software and e-services”, International Journal of Service Industry Management , Vol. 13 No. 5, pp. 452-478.

Tham, A. , Croy, G. and Mair, J. (2013), “Social media in destination choice: distinctive electronic word-of-mouth dimensions”, Journal of Travel & Tourism Marketing , Vol. 30, pp. 144-155.

Tustin, D.H. , Ligthelm, A.A. , Martins, J.H. and Van Wyk, H. (2005), Marketing Research in Practice , Unisa, Pretoria.

Van Schalkwyk, D. (2012), Quantitative Statistics , CPUT, Cape Town.

Vanden Bergh, B.G. , Lee, M. , Quilliam, E.T. and Hove, T. (2011), “The multidimensional nature and brand impact of user-generated as parodies in social media”, Interactive Journal of Advertising , Vol. 30 No. 1, pp. 103-131.

Wang, Y. and Sun, S. (2010), “Assessing beliefs, attitudes, and behavioural responses toward online advertising in three countries”, International Business Review , Vol. 19 No. 1, pp. 333-344.

Wolin, L.D. , Korgaonkar, P. and Lund, D. (2002), “Beliefs, attitudes and behaviour towards web advertising”, International Journal of Advertising , Vol. 21 No. 1, pp. 87-113.

Wronski, M. and Goldstruck, A. (2013), SA Social Media Landscape , World Wide Worx & Fuseware, Johannesburg.

Wu, S.I. , Wei, P.L. and Chen, J.H. (2008), “Influential factors and relational structure of internet banner advertising in the tourism industry”, Tourism Management , Vol. 29 No. 1, pp. 221-236.

Yaakop, A. , Anuar, M.M. and Omar, K. (2013), “Like it or not: issue of credibility in Facebook advertising”, Asian Social Science , Vol. 9 No. 3, pp. 154-163.

Yadav, M.S. , de Valck, K. , Hennig-Thurau, H. , Hoffman, D.L. and Spann, M. (2013), “Social commerce: a contingency framework for assessing marketing potential”, Journal of Interactive Marketing , Vol. 27 No. 4, pp. 311-323.

Yang, T. (2012), “The decision behaviour of Facebook users”, Journal of Computer Information Systems , Vol. 52 No. 3, pp. 50-59.

Yoo, C.Y. , Kim, K. and Stout, P.A. (2010), “Assessing the effects of animation in online banner advertising: hierarchy effects model”, Journal of interactive advertising , Vol. 4 No. 2, pp. 49-60.

Zikmund, W.G. (2003), Business Research Methods , 7th ed., Thomson South-Western, Oklahoma, OK.

Zikmund, W.G. and Babin, B.J. (2007), Essentials of Marketing Research , 3rd ed., Thomson, Mason, OH.