Product involvement, WOM and eWOM in the fast food industry: A young adult perspective in an emerging African economy

Abstract

This study investigates the relationship between product involvement, WOM, and eWOM in the fast food industry between two sub-groups of young adult consumers in South Africa. A quantitative design was followed, with primary data (n = 201) gathered from respondents using a self-administered questionnaire. The results suggest that product involvement exerts a positive and significant influence on WOM and eWOM in the case of both young adult sub-groups. There is also no difference in the perceptions of the two sub-groups of young adults concerning product involvement, WOM, and eWOM. Product involvement is found to play an important role in spreading WOM and eWOM in the fast food industry, and is proposed as an important focus area for managers.

Keywords:

• product involvement
• WOM
• eWOM
• young adults

PUBLIC INTEREST STATEMENT

In a dynamic business environment, fast food operators are forced to look for ways to win the hearts of young adult consumers. A possible approach to address this is through the implementation of word-of-mouth (WOM). WOM enables customers to develop positive feelings and spread positive reviews about a fast food operator. Due to the importance of WOM, it is essential to identify the influential factors that have an impact on electronic word-of-mouth (eWOM). For this reason, the study examined the interrelationship between product involvement, WOM, and eWOM in the fast food industry of South Africa. The findings indicated that product involvement has an effect on both WOM and eWOM. Interestingly, there was no difference in the perceptions of the two sub-groups of young adults about product involvement, WOM, and eWOM. At a practical level, the study provides insights into how the managers of fast food operators can cultivate product involvement in an effort to stimulate WOM and, in turn, lead to the spread of eWOM.

1. Introduction

In the 21^st century, the fast food industry has been established as one of the largest and most dynamic industries globally (Euromonitor, 2018). The fast food industry contributes to the world economy by generating revenue of $3,145 billion; in 2017 it grew by 3.17%, to reach a consumption volume of 506.3 billion transactions (Marketline, 2018a). Most of the growth emanating from this industry is led by emerging economies from the Asia-Pacific region, with $1,277 billion in sales, and accounting for 40.6% of the global market (Marketline, 2018a). This trend is similar to that in the emerging economy of South Africa, where the fast food industry is earmarked as a key generator of growth in the country, as it accounts for $35.1 billion in revenue and attracts more than 40 million customers (Marketline, 2018b). Marketline (2018b) established that the fast food industry in South Africa is dominated by a sizeable share (14.5 million) of young adult consumers, who tend to eat out more than other generations and require an instant response from fast food operators. In this competitive business environment, young adult consumers are increasingly price-conscious due to their declining disposable income and lower confidence levels, which in turn affects the profitability of the fast food industry in South Africa (Marketline, 2018b). This is particularly true because younger generations have lost trust in the promotion of offerings through traditional advertising, and are looking for more innovative ways to communicate and engage with fast food operators in order to receive value for their money (Huang et al., 2011; Marketline, 2018b). In response to this call, fast food operators have been forced to look for more persuasive ways to promote their products in the form of promotional pricing, targeted meal time deals, and loyalty programmes to attract young adult consumers (Euromonitor, 2018). A proven way in which fast food operators can enhance their competitiveness and fulfil customers’ requirements is through word-of-mouth (WOM) (Ahmadi, 2019; Van Tonder et al., 2018).

WOM is regarded as informal communication that takes place between interested parties (Tan & Lee, 2019). Yang et al. (2012) opine that WOM is an effective and credible tool for providing useful information that can influence the levels of consumers’ attitude, perception, and purchasing behaviour. Turki and Amara (2017) concur that 76% of consumers rely first on information from friends and family via WOM in order to gain knowledge and enhance their purchase intentions towards products and services in the marketplace. As a result, WOM is beneficial for businesses such as fast food operators, as it can spread positive reviews about their offering, leading consumers to persuade others to use a particular product or service, and can assist in developing favourable feelings toward the brand (Van Tonder et al., 2018). According to Friedmann and Lowengart (2019), products and services differ on the level of consumers’ involvement at the point of purchase. More specifically, consumers who are more involved with an offering tend to engage in a continuous search for information and have the ability to influence other consumers’ purchase decisions by engaging in WOM than those who are less involved (Roe & Bruwer, 2017). The empirical studies of Krishnamurthy and Kumar (2018) and Prasad et al. (2017) established that product involvement has an influence on WOM engagement among young adult consumers. With the advent of the internet and social media usage, this consumer segment has gained an opportunity for expanding WOM by sharing their experiences and purchasing behaviours in an online space, thus stimulating electronic word-of-mouth (eWOM) (Dechawatanapaisal, 2019; De Meyer & Petzer, 2014; Zhang et al., 2017).

Although the extant literature has confirmed the interlinkages between product involvement, WOM, and eWOM in different market settings, such research has not validated the interlinkages from an elaboration likelihood model (ELM) perspective or explored such interlinkages among young adult consumers in an emerging market context. For this reason, the majority of studies (Chun & Lee, 2016; Dechawatanapaisal, 2019; Menidjel et al., 2020; Roe & Bruwer, 2017; Wu & Wang, 2011) that have explored the theoretical connection between product involvement, WOM, and eWOM are concentrated in developed countries, with fewer studies been conducted in Africa (Eze et al., 2014; Narteh & Owusu‐Frimpong, 2011; Van Scheers & Prinsloo, 2014). This implies that previous research has not extensively explored this area of research and requires attention for understanding the linkages between these three constructs from an African perspective. In an emerging economy such as South Africa, most studies have concentrated on gaining insight into the different factors that contribute to eWOM behaviour within tourism, banking, and fast food industries (Beneke et al., 2016; De Meyer & Petzer, 2014; Sotiriadis & Van Zyl, 2013; Van Tonder et al., 2018). This suggest that limited research exist within a South African domain that offers a comprehensive account for how product involvement can have an effect on WOM and eWOM among young adult consumers. In addition, there is a presumed difference between the sub-groups of young adult consumers that has not been explored before. This study aims to close this research gap by investigating the relationship between product involvement, WOM, and eWOM in the fast food industry of South Africa, and examines significant differences between sub-groups of young adult consumers in relation to these three constructs. Notably, young adult consumers represent a sizeable share (33.5 million) of the population of South Africa, as they are well-educated, mostly hold middle and top management positions, and are inclined to spend more on fast-food brands than other generations (Euromonitor, 2020). Additionally, young adult consumers are known to be self-reliant, pragmatic, hardworking and tend to place priority on convenience, quality, and value for money (Euromonitor, 2020). Insight into the cultural uniqueness and the value systems of young adult consumers is thus important for studying how their level of product involvement towards fast-food brands can stimulate WOM activity within an emerging economy of South Africa. With this in mind, a focus on South Africa provides a unique setting for exploring how the perceptions and behaviours of young adult consumers can influence the cultivation of product involvement on enhancing the level of WOM and, in turn, lead to the spread of eWOM.

The current study contributes to theory and practice by providing an improved understanding of how product involvement can stimulate WOM and, in turn, lead to the spread of eWOM among young adults in the fast food industry of South Africa. From a theoretical point of view, the study enriches the existing body of literature on WOM in an emerging market context by illuminating insights from a consumer behaviour perspective on how young adult consumers can generate eWOM through product involvement and WOM. The study may also assist in demonstrating the role and relevance of the theory of elaboration likelihood model (ELM) in explaining the theoretical connection between product involvement, WOM, and eWOM among young adult consumers. This in turn, will generate an improved understanding of how the ELM theory provides a foundation for how the cultivation of product involvement can generate the spread of WOM and thereby leading to higher levels of eWOM. From a practical standpoint, the findings from this study can be of value to fast food operators by proposing strategies for strengthening their competitiveness and engaging with the young adult consumer segment through the use of eWOM. In addition to the proposed strategies, the study may also contribute to inspiring existing consumers to share their positive reviews about the offerings of fast food brands with other potential consumers and persuade them to engage in eWOM behaviour. Furthermore, the proposed model in this study may provide guidelines for how fast food operators can monitor their product involvement activities in an effort to stimulate WOM, and consequently improve the level of eWOM.

The paper is structured into a number of distinct sections. The first section commences with an insight into the fast food industry and explores a perspective on young adult consumers. This is followed by a discussion of the key constructs investigated in this study on which the research hypotheses are formulated, and a theoretical model is presented. The third section provides an explanation of the research methodology, the results, the findings, and the managerial implications. The final section uncovers the limitations and the directions for future research.

2. Literature review

2.1. An overview of the fast food industry of South Africa

The South African fast food industry is one of the key pillars of the country’s economic growth. More specifically, fast food restaurants experienced growth rates of 1.5% in 2018, and accumulated R13.9 million in sales due to the increased attractiveness of the market (Statistics South Africa, 2019). A report by Euromonitor (2018) established that the fast food industry recorded a value growth of 9%, and reached 825.6 million in consumption volume and 11,380 operational outlets across the country in 2017. In spite of this positive growth, the South African economy is increasingly faced with challenges related to a decline in the value of the currency, the general rise in the price of commodities, an unstable power supply, and higher levels of unemployment, all of which affect the sustainability of the fast food industry (Euromonitor, 2018). Notably, the fast food industry brings in more than R300 billion per year, and is projected to increase by 3% in value growth in the next five years due to a broadening middle class, a strong dining culture, high levels of meat consumption, and an increased number of women in the labour force (Holmes, 2016).

In this diverse fast food environment, there is increased competition among fast food brands in South Africa to satisfy customers’ needs and to protect their market shares (Euromonitor, 2018). For instance, Yum Brands maintained their leadership position with 29% market share due to having a strong heritage in the marketplace (Euromonitor, 2018). Famous Brands ranked second with a total share of 17%, followed by McDonald’s and Nando’s with value shares of 15% and 7% respectively (Euromonitor, 2018). Interestingly, the entrance of new players such as Burger King, Popeyes, and RocoMamas has attracted customer attention through aggressive promotional campaigns and niche market offerings (Euromonitor, 2018). In order to remain competitive, fast food brands are now expanding into niche markets (e.g., townships), are adjusting their menu to cater to health-conscious consumers, and are managing to limit price increases by putting pressure on suppliers to keep costs down (Bizcommunity, 2016).

2.2. A perspective on young adult consumers

Today’s young adults were born into a rapidly changing technological environment spanning the period between 1978 and 1994 (Messarra et al., 2016). This generation possesses the greatest purchasing power ($2.45 billion worldwide), is considered to be the most educated generation, prefers to socialise via the internet and social media, and consequently is attracted to fast food operators that embrace technological advancements (Messarra et al., 2016). Berraies et al. (2017) contend that the young adult market segment is made up of mobile lovers and extensive users of technology who love to stay in touch and engage in conversations with people in a social space through various forms of communication—such as eWOM. This implies that the young adult cohort of consumers are very connected to their friends and peers as they engage in social interactions, information sharing, and collaboration, thus influencing one another’s purchase intentions (Dechawatanapaisal, 2019).

Zhang et al. (2017) argue that there is a level of heterogeneity in the young adult cohort due to the presence of different behavioural patterns between the ages of 18 and 35 years, necessitating a further investigation within the cohort. Martin and Turley (2004) support this observation by suggesting that the young adult market segment should be sub-divided into younger (18–25 years) and older (26–35 years) groups to understand their behavioural responses to, and motivations for, purchasing products and services. The younger cohort of the young adult segment, known as “teens”, spend $56 billion per year, mostly live with their parents, attend colleges and universities, and are major influencers in product or service purchases across multiple households (Martin & Turley, 2004; Zhang et al., 2017). By contrast, the older cohort of young adults have a purchasing power of $200 billion, are more mature, have their own families, are employed between lower and middle management positions, and tend to be more influenced by their peers in order to gain their gratification (Martin & Turley, 2004; Zhang et al., 2017). For the purpose of this study, both younger and older cohorts of the young adult market segment of consumers are considered in order to understand their motives and potential differences when engaging in eWOM.

2.3. Theoretical lens

The current study is grounded on the theory of the elaboration likelihood model (ELM) with respect to the interrelationship between product involvement, WOM, and eWOM. ELM has its foundations in social psychology, and was theorised by Petty and Cacioppo (1984) to explain the influence of persuasion and attitude change in stimulating cognitive responses. Zha et al. (2016) posit that the purpose of ELM theory is to determine the different factors that have an effect on how consumers process information and on their attitude to respond favourably or unfavourably to products and services. ELM is considered one of the most widely used theories for evaluating how cues in the environment shape consumers’ level of processing information and attitude formation (Vashist, 2018). According to Zha et al. (2016), there are two routes to cognitive elaboration: the central route and the peripheral route. The central route suggests that cognitive elaboration results from an individual’s observation and continuous evaluation of the correct features of the messages presented in favour of involvement, while the peripheral route explains that cognitive evaluation is caused by an individual’s association with environmental cues (Vashist, 2018). In line with the theory of ELM, product involvement is considered an elaboration component that follows the central route for assessing the usefulness of information (Vashist & Pillai, 2017). Vashist (2018) concurs that individuals who possess higher levels of involvement are likely to engage in a rigorous process of evaluating and processing information, leading to the spread of WOM. In contrast, individuals who possess a lower level of involvement are not influenced by the content of the information and, as a result, will be less motivated to engage in WOM (Vashist & Pillai, 2017). Consequently, ELM provides theoretical support for examining the interlinkages between product involvement, WOM, and eWOM in the fast food industry of South Africa.

2.4. Product involvement among young adults towards fast food operators

Han and Kim (2017) describe product involvement as the extent to which an offering holds personal relevance and importance to the life of a consumer. In the context of this study, product involvement is measured as a consumer’s subjective evaluation of the perceived value of the offering from a fast food operator. On the basis of this notion, consumers experience different levels of product involvement, which affects their purchase decisions. In this way, the level of product involvement is based on the assessment of an individual’s needs and motives towards selecting their preferred product or service (Rokonuzzaman et al., 2020). The degree of product involvement can thus be classified into two categories: high involvement and low involvement (Hoonsopon & Puriwat, 2016; Kong & Zhang, 2013). Kong and Zhang (2013) argue that low involvement consumers are less motivated to seek more product information, as their purchase is less risky, and product attributes have no significant impact on their purchase choice. On the other hand, high involvement consumers are prone to higher levels of involvement and the greater risk associated with their purchase, and will require continuous information and interaction with the product in order to make a purchase decision (Auger et al., 2010). Therefore, product involvement is an indicator that is linked to product evaluation and purchase decisions, which in turn affect the level of involvement for a particular offering (Hochgraefe et al., 2012).

Parment (2013) argues that young adult consumers differ in their level of involvement when they evaluate various products and services. This finding is corroborated by Heinze (2010) who established that young adult consumers experienced different levels of involvement (high and low) after being exposed to a number of advertisements relating to different brands. Subsequently, Campbell et al. (2014) established that young adults possess greater levels of involvement towards local foods compared to foreign foods. Therefore, young adults that exhibit a higher level of involvement are motivated to engage in the purchase of wine and fine trade products than those with a lower degree of involvement (Aksoy & Ozsonmez, 2019); Roe & Bruwer, 2017). In the light of the foregoing discussion, it is reasonable to assume that the two sub-groups (sub-group 1: 18–25 years and sub-group 2: 26–35 years) of young adults will experience different levels of involvement in their purchase of fast food due to their preferences, attitudes, and behavioural responses. It is thus hypothesised that:

H₁: There is a significant difference in the level of product involvement with fast food operators between young adults aged 18–25 years and young adults aged 26–35 years

2.5. The interrelationship between product involvement and WOM

Product involvement has a relative influence on consumers’ rational and behavioural responses, including WOM (Xue & Zhou, 2011). The empirical work of De Meyer and Petzer (2014) and Loureiro and Kaufmann (2018) found that product involvement has a positive influence on the level of WOM among fast food restaurant operators. This finding is supported by the study of Voyer and Ranaweera (2015), which established that the promotion of product involvement leads to the development of WOM in the service arena. Therefore, it is likely that the more involved a customer is in the product category, the greater the likelihood that they will process and spread favourable information via WOM to make an informed purchase decision (Xue & Zhou, 2011). Considering the context of this study, it is possible that product involvement can influence the spread of WOM among the two sub-groups (sub-group 1: 18–25 years and sub-group 2: 26–35 years) of young adults. Young adult consumers who identify with fast-food brands and hold stronger feelings of involvement are prone to spread positive WOM about their offerings. On the basis of the foregoing empirical evidence, the following hypotheses are presented:

H_2a: There is a significant and positive relationship between the level of product involvement and the extent of WOM among sub-group 1 (18–25 years) of young adults in South Africa

H_2b: There is a significant and positive relationship between the level of product involvement and the extent of WOM among sub-group 2 (26–35 years) of young adults in South Africa

2.6. The interrelationship between product involvement and eWOM

Product involvement arises as an essential element in fostering the spread of eWOM (De Meyer & Petzer, 2014). The findings of Beneke et al. (2016) and Krishnamurthy and Kumar (2018) established that the cultivation of product involvement has a positive influence on the development of eWOM in product and service environments. Prasad et al. (2017) support these results by arguing that consumers’ involvement in the product decision-making process contributes to eWOM through sharing meaningful information and engaging with their peers on social media. Therefore, product involvement creates the foundation for engaging in conversations and the evaluation of product information among consumers, leading to eWOM (Chen, 2011). In line with the principles of this study, it is likely that product involvement can have an effect on stimulating eWOM behaviour among the two sub-groups (sub-group 1: 18–25 years and sub-group 2: 26–35 years) of young adults. This is due to the fact that young adult consumers who are attached to fast-food brands and possess favourable levels of involvement can contribute to the spread of eWOM. It is thus hypothesised that:

H_3a: There is a significant and positive relationship between the level of product involvement and the extent of eWOM among sub-group 1 (18–25 years) of young adults in South Africa

H_3b: There is a significant and positive relationship between the level of product involvement and the extent of eWOM among sub-group 2 (26–35 years) of young adults in South Africa

2.7. WOM among young adults towards fast food operators

WOM is described as the casual communication between people who have used goods or services to meet their desired goals (Jalilvand et al., 2017). In line with the principle of this study, WOM relates to an interpersonal communication between consumers about the provision of fast food from a desired operator. Furthermore, consumers are responsible for creating and spreading WOM amongst each other, because they consider it to be trustworthy than to the claims made by advertisements (See-To & Ho, 2014). This implies that consumers perceive WOM to be a credible outlet for accessing product and service information than advertisements, because they place more value on the opinions and suggestions made by their families and friends towards offerings in the marketplace (Terblanche, 2011). A study by Bughin et al. (2010) and Ngoma and Ntale (2019) concurs with this observation, arguing that WOM is the most influential force behind 20 to 50% of consumers’ buying behaviour. This argument is validated by Jalilvand et al. (2017), who established that 76% of all purchase decisions are impacted by WOM.

Furthermore, WOM can be expressed as either negative or positive (Ngoma & Ntale, 2019). Positive WOM focuses on generating a favourable recommendation about products within a consideration set, while negative WOM centres on the disapproval of products that do not align with customers’ requirements. Delafrooz et al. (2019) add that positive WOM increases the probability of purchase intentions towards a particular offering, while negative WOM reduces the chances of buying any product or service. Sivadas and Jindal (2017) further argue that WOM is characterised into two categories: volume and valence. Volume relates to the total number and intensity of interactions, while valence explains the degree of their intensity—that is, whether the message is perceived as positive or negative (Loureiro and Kaufmann (2018). Lang (2011) is of the opinion that WOM has a long-lasting impact on the financial performance of organisations such as fast food operators by enhancing the likelihood of sales, customer life-time value, and higher share prices. WOM is therefore an effective way to disseminate an opinion, because it is usually given via a face-to-face interaction between people who are familiar with each other (Jalilvand et al., 2017).

Buksa and Mitsis (2011) discovered that young adult consumers engaged in positive WOM recommendation for brands endorsed by an athlete role model. This finding is supported by the previous studies of Dechawatanapaisal (2019) and Uen et al. (2015) who established that the proactive facilitation of WOM referrals through young adult consumers is an important factor that can contribute to boosting the reputation and image of a business. Zhao et al. (2014) contend that young adult consumers are motivated to spread positive WOM about a product or service after experiencing a satisfactory service. On the basis of the empirical discussion, it is reasonable to assume that the two sub-groups (sub-group 1: 18–25 years and sub-group 2: 26–35 years) of young adults will experience different levels of WOM in their purchase of fast food due to their preferences, attitudes, and behavioural patterns. It is thus proposed that:

H_4: There is a significant difference in the level of WOM towards fast food operators between young adults aged 18–25 years and young adults aged 26–35 years

2.8. eWOM among young adults towards fast food operators

According to Chu and Sung (2015), eWOM is refers to extent to which consumers generate and share favourable or unfavourable brand-related content through the internet. In line with the purpose of this study, eWOM is measured as the extent to which consumers share experiences, feelings, and attitudes about the offerings of fast food operators in a virtual space. Interestingly, consumers value eWOM for several reasons, including: to obtain useful product information, to engage in a social conversation by processing information, and to develop a sense of belonging to a community (Bao & Chang, 2014). In addition, eWOM provides a new pathway for organisations to reach consumers and to influence their opinions in the marketplace strategically (Zha et al., 2016). Furthermore, eWOM provides consumers with the ability to engage with each other and share their opinions on multiple online forums (e.g., internet communities, newsgroups, and bulletin boards) (Prendergast et al., 2010; Vashist, 2018). Conversely, eWOM information, compared with WOM, tends to diffuse faster because a wider audience has a greater chance of accessing and sharing the message via an online interactive media (e.g., social media) (Vashist, 2018). Therefore, eWOM is beneficial to consumers, as it reduces product uncertainty, stimulates the sharing of useful information, and builds trust in businesses, which in turn increases the probability of product purchases (Hajli et al., 2014; Zha et al., 2016).

Zhang et al. (2017) found that young adult consumers had stronger intentions to engage in eWOM after experiencing a good service. Prasad et al. (2017) and Velazquez et al. (2015) support this notion by suggesting that young adult consumers are willing to spread positive eWOM about hotels who invest in the use of information and communication technology to enhance their service delivery. Serra-Cantallops et al. (2018) conclude that young adult consumers are prone to engage in eWOM after receiving an emotional service experience. In the light of the foregoing discussion, it is reasonable to assume that the two sub-groups (sub-group 1: 18–25 years and sub-group 2: 26–35 years) of young adults will experience different levels of eWOM in their purchase of fast food due to their perceptions, feelings, and behavioural responses. It is thus hypothesised that:

H_5: There is a significant difference in the level of eWOM towards fast food operators between young adults aged 18–25 years and young adults aged 26–35 years

Figure 1 presents a proposed theoretical model that illustrates the interrelationship between the key constructs explored in this study.

Figure 1. Proposed theoretical model.

Source: Researchers’ own construct.

3. Research methodology

3.1. Research context and sample

The current study adopted a quantitative descriptive approach to validate the association between the constructs in the proposed theoretical model (see Figure 1). The target population included young adult customers of fast food operators who reside in the Gauteng Province, who have visited a fast food operator in the past six months, and who have gone online to share their content and opinions. The province is ideally suited for the study because it is a cornerstone to the economic development of South Africa, attracting a multitude of fast food operators, and because it provides a diverse population, which assists with meeting the goal of this study (Statistics South Africa, 2019). Thus young adult consumers represent the sampling units that align with the purpose of this study.

The study was aimed at customers who possessed favourable levels of involvement with their fast food operators, as product involvement is considered an essential driver of WOM and eWOM (Delafrooz et al., 2019). As a result, this would assist the researchers in determining consumers’ level of involvement towards fast food operators; screening questions were thus used to invite respondents who fulfilled the requirements of this study. With this in mind, respondents from major metropolitan areas in the Gauteng Province (Johannesburg, Pretoria, and Ekurhuleni) were invited to participate in this study; they were requested to provide their responses on any fast food operator with whose products they had experienced a favourable involvement.

The data collection was facilitated by trained field workers who live in different parts of the Gauteng Province. Twenty field workers were asked to invite their friends, families, and colleagues who met the sample requirements for participation. Since a sample frame could not be obtained, the researchers followed a non-probability sampling technique in the form of convenience sampling to collect information from respondents. Field workers thus invited respondents to take part in this study on the basis of convenience and availability. A convenience sampling method was selected, as it facilitates the gathering of a large number of responses within a short period of time and at a cheaper rate, thereby enhancing sampling adequacy (Malhotra, 2018). For this reason, respondents were required to fill in and complete the self-administered questionnaires, and to hand them over to field workers once all the responses were made to the different sections in the questionnaire. After the field work was had been completed, a realised sample of 201 valid responses was used to analyse the results.

The researchers made use of self-administered questionnaires to collect the data from the respondents. The questionnaire was made up of screening questions and direct structured questions to obtain responses from the sample. The questionnaire was categorised into four sections: A, B, C, and D. Section A gathered information about the demographic profile of the respondents. Section B analysed the online patronage habits of the respondents. Sections B, C, and D included multi-item scale items to measure the perceptions of respondents about product involvement, WOM, and eWOM. The measurement items were sourced from previous studies that were considered to be reliable and valid. A five-point Likert scale and semantic differential scales ranging from 1 (strongly disagree/never) to 5 (strongly agree/always) were used to evaluate the perceptions and attitudes of respondents about the constructs investigated in this study. Product involvement, WOM, and eWOM were measured using the scale of De Meyer and Petzer (2014), and included 10 items, nine items, and 17 items respectively. A pre-test was conducted among a representative sample of 20 respondents prior to the collection of the data to ensure that the questionnaire was free from errors and addressed the purpose of this study.

Table 1 provides insight into the different statements that were used to describe the three constructs in this study.

Table 1. Summary of measurement constructs and scale items

Construct	Scale items
Product involvement (B)	B1 Unimportant———→ Important B2 Boring—————-→ Interesting B3 Irrelevant————-→ Relevant B4 Unexciting———–→ Exciting B5 Means nothing to me–→ Means a lot to me B6 Unappealing———→ Appealing B7 Dull——————-→ Fascinating B8 Worthless————→ Valuable B9 Uninvolving———→ Involving B10 Not needed———-→ Needed
WOM (C)	C1 I speak of my brand of fast food restaurant much more frequently than about any other brand of fast food restaurant C2 I speak of my brand of fast food restaurant much more frequently than about brands of any other type C3 I speak of my brand of fast food restaurant to many individuals C4 I recommended my brand of fast food restaurant C5 I speak of the good side of my brand of fast food restaurant C6 I am proud to say to others that I am a customer of this fast food restaurant brand C7 I strongly recommend that people buy products from my brand of fast food restaurant C8 I mostly say positive things to others C9 I speak favourably of my brand of fast food restaurant to others
eWOM (D)	D1 I talk to friends online about fast food restaurants D2 I send/forward emails about fast food restaurants D3 I talk about fast food restaurants in a chat room/forum D4 I meet other people online who like fast food restaurants D5 I write about fast food restaurants on a personal page, blog, or website D6 I write reviews/stories or rate fast food restaurants online D7 I post messages on Facebook/MySpace/Twitter about fast food restaurants D8 I share content related to fast food restaurants online D9 I comment online when people specifically ask me for my opinion D10 I comment online when a fast food restaurant is much better than I expected D11 I comment online when I know a lot about the topic D12 I comment online when a fast food restaurant is much worse than I expected D13 I comment online to ask or post a question D14 I comment online when I am bored and have nothing to do D15 I comment online when I disagree with a review or rating D16 I comment online when friends are talking about it D17 I comment online when I want to meet other people who share my interests

Source: Adapted from De Meyer and Petzer (2014).

The data obtained from the questionnaires was then coded, cleaned, and edited on SPSS version 25 in order to conduct further statistical analysis. Descriptive frequencies were analysed by evaluating the frequency percentages, means, and standard deviations of the sample profile. Exploratory factor analysis was conducted to evaluate the structure validity of the three constructs investigated in this study. Cronbach’s alpha (α) values were used to measure the overall reliability and consistency of the measurement scales. The researchers followed a 95% confidence level, and a subsequent significance level of 5% (p = 0.05), to validate the research hypotheses. Finally, an independent samples t-test and multiple regression techniques were used for hypothesis testing.

4. Results

4.1. Demographic profile of the respondents

Table 2 provides insights into the demographic information of the sample. Most of the respondents for sub-group 1 (18–25 years, n = 125) of young adult consumers were female (37.4%), African (40.8%), and English-speaking (22.7%). For sub-group 2 (26–35 years, n = 78), most of the sample of young adult consumers were also female (22.2%), African (24.4%), and spoke English (31.5%). Overall, this indicates that the two sub-groups shared similar characteristics related to gender, race, and language.

Table 2. Respondents’ demographic information

Item	Sub-group 1 (18–25 years)		Sub-group 2 (26–35 years)
Gender	Frequency (n)	Valid percentage (%)	Frequency (n)	Valid percentage (%)
Male	49	24.1	33	16.3
Female	76	37.4	45	22.2
Total	125	61.5	78	38.5
Race	Frequency (n)	Valid percentage (%)	Frequency (n)	Valid percentage (%)
African	82	40.8	49	24.2
Asian	2	1.0	0	0.0
Coloured	7	3.5	9	4.5
Indian	16	8.0	3	1.5
White	17	8.5	16	8.0
Total	124	61.8	77	38.2
Home language	Frequency (n)	Valid percentage (%)	Frequency (n)	Valid percentage (%)
Afrikaans	3	1.5	11	5.4
English	46	22.7	18	8.9
Nguni	29	14.3	15	7.4
Sotho	33	16.3	20	9.9
Venda/Tsonga	7	3.4	11	5.4
Other	7	3.4	3	1.4
Total	125	61.6	78	38.4

4.2. Descriptive information

Table 3 provides a summary of the mean and standard deviations for each scale item on a five-point Likert scale and on semantic differential scales. The respondents rated their level of product involvement for the two sub-groups on the higher spectrum (sub-group 1: mean = 4.98, standard deviation = 1.077; and sub-group 2: mean = 4.98, standard deviation = 1.198). It is also evident that the overall mean for product involvement was equal for the two sub-groups of young adults. With respect to WOM, respondents assigned positive ratings above the mid-point value of 2.50 for the two sub-groups (sub-group 1: mean = 3.40, standard deviation = 0.770; sub-group 2: mean = 3.20, standard deviation = 0.946). Moreover, the overall mean of sub-group 1 of young adult consumers was slightly higher than the one for sub-group 2. For eWOM, the respondents from sub-group 2 assigned positive ratings to fast food operators (mean = 2.52, standard deviation = 0.908) above the mid-point, followed by sub-group 1 (mean = 2.50, standard deviation = 0.796).

Table 3. Descriptive results

Construct	Sub-group 1 (18–25 years)		Sub-group 2 (26–35 years)
Product involvement	Mean	Standard deviation	Mean	Standard deviation
	4.98	1.077	4.98	1.198
WOM	Mean	Standard deviation	Mean	Standard deviation
	3.40	0.770	3.20	0.946
eWOM	Mean	Standard deviation	Mean	Standard deviation
	2.50	0.796	2.52	0.908

4.3. Reliability of the measurement instrument

Cronbach’s alpha (α) was used to determine the reliability of the measurement scales for product involvement, WOM, and eWOM. According to Malhotra (2018), Cronbach’s alpha values of above 0.70 are considered to be reliable and valid. Table 4 shows the results for the reliability of the constructs explored in this study.

Table 4. Reliability results of the measurement scales

Construct	Number of items	Cronbach’s alpha (α) coefficients
Product involvement	10	0.915
WOM	9	0.933
eWOM	17	0.931

Table 4 indicates that the Cronbach’s alpha values for the scales measuring product involvement, WOM, and eWOM were all above the recommended threshold of 0.70, ranging between 0.915 and 0.933. The measurement scales for these three constructs can be considered to be reliable, and can thus be used to validate the hypothesised relationships in this study.

4.4. Hypothesis testing

An independent samples t-test was used to assess whether a significant difference exists between the sub-groups of young adults in response to the level of product involvement, WOM, and eWOM. In order to validate the extent of this difference between the sub-groups of young adults, a p-value (two-tailed) within the recommended threshold of 5% indicates a significant difference in the mean scores on the dependent variable for each of the two sub-groups (Malhotra, 2018). Table 5 provides evidence of the results from the independent samples t-test pertaining to H₁, H₄, and H₅.

Table 5. Independent samples t-test results

Construct		Levene’s test of equality of variances		T-test for equality of the means					95% confidence interval of the difference
Product involve-ment		F	Sig	T	Df	Sig (2- tailed)	Mean differ-ence	Std. error of differ-ence	Upper	Lower
	Equal variances assumed	5.267	0.023	-0.05	201	0.964	-0,007	0.162	-0.327	0.313
	Equal variances not assumed			-0.04	151	0.965	-0.007	0.166	-0.336	0.321
WOM		F	Sig	T	Df	Sig (2- tailed)	Mean differ-ence	Std. error of differ-ence	Upper	Lower
	Equal variances assumed	3.871	0.051	1.62	200	0.106	0.197	0.122	-0.043	0.437
	Equal variances assumes			1.55	139	0.124	0.197	0.127	-0.055	0.449
eWOM		F	Sig	T	Df	Sig (2- tailed)	Mean differ-ence	Std. error of differ-ence	Upper	Lower
	Equal variances assumed	2.601	0.108	-0.21	200	0.833	-0.026	0.122	-0.265	0.214
	Equal variances not assumed			-0.21	148	0.838	-0.026	0.125	-0.273	0.222

Table 5 indicates that there is no statistical evidence to suggest a difference between the mean scores of product involvement, WOM, and eWOM among the sub-groups of young adults. The results show that there is no significant difference (t = −0.05, p = 0.964) between the mean scores (sub-group 1: mean = 4.98, standard deviation = 1.077; sub-group 2: mean = 4.98, standard deviation = 1.198) of the two sub-groups for product involvement. The magnitude of the difference in the means (mean difference = 0.162, 95 C.I %: −0.327 to 0.313) was relatively small, and thus H₁ is not supported. Second, the results suggest that the mean scores (sub-group 1: mean = 3.40, standard deviation = 0.770; sub-group 2: mean = 3.20, standard deviation = 0.946) for the two sub-groups are not statistically different (t = −1.62, p = 0.197) for WOM, and thus H₄ cannot be accepted. This further implies that the actual difference in the means (mean difference = 0.197, 95 C.I %: −0.043 to 0.437) was fairly small. Last, the results revealed that there is no significant difference (t = −0.05, p = 0.964) between the mean scores (sub-group 1: mean = 2.50, standard deviation = 0.796; sub-group 2: mean = 2.52, standard deviation = 0.908) of the two sub-groups for eWOM. The magnitude of the difference in the means (mean difference = 0.122, 95 C.I %: −0.265 to 0.214) was very small, and thus H₅ is not supported.

Further statistical tests were carried out to validate the interplay between product involvement, WOM, and eWOM using a multiple regression technique. The results provided evidence for the existence of a positive and significant relationship between product involvement and WOM among the sub-groups (sub-group 1: β = 0.346, t = 4.075, p = 0.000; sub-group 2: β = 0.367, t = 3.445, p = 0.001) of young adults. This is congruent with H_2a-b, and so the hypothesis is supported. Last, product involvement (sub-group 1: β = 0.190, t = 2.140, p = 0.034; sub-group 2: β = 0.449, t = 4.386, p = 0.000) significantly exerted a positive impact on the level of eWOM, and thus H_3a-b is accepted. Notably, product involvement contributed to 71% of the variation in WOM. Product involvement also explained 64% of the variation in eWOM. Table 6 provides insights into the results about the significance of the association between product involvement, WOM, and eWOM.

Table 6. The interrelationship between product involvement, WOM, and eWOM

Hypotheses	Direct path estimates (β)	T-statistic (t)	Probability value (p)	Hypothesis result (p < 0.05)	Variance explained (R^2)
H2a Product involvement→ WOM (sub-group 1: 18–25 years)	0.346	4.075	0.000	Supported	0.710
H2b Product involvement→ WOM (sub-group 2: 26–35 years)	0.367	3.445	0.001
H3a Product involvement→ eWOM (sub-group 1: 18–25 years)	0.190	2.140	0.034	Supported	0.640
H3b Product involvement→ WOM (sub-group 2: 26–35 years)	0.449	4.386	0.000

5. Discussion

The results proved that the mean scores of product involvement, WOM, and eWOM are not statistically different between the two sub-groups of young adult consumers in the fast food industry of South Africa. This suggests that there is a lack of sufficient evidence for the mean scores of product involvement (t = −0.05, p = 0.964), WOM (t = −1.62, p = 0.197), and eWOM (t = −0.05, p = 0.964) being significantly different between the sub-groups of young adults, and so H₁, H₄, and H₅ cannot be supported. These findings are inconsistent with the work of Martin and Turley (2004) and Zhang et al. (2017), who contend that there is a level of heterogeneity among young adult consumers that requires the group to be sub-divided according to their attitudes and behavioural responses to products and services in the marketplace. A possible reason is that the sub-groups of young adults in South Africa share similar experiences and opinions about fast food operators, and should be treated as one group in order to understand their purchase behaviour and the probability that they will spread eWOM.

Notably, empirical evidence was found for the theoretical connection between product involvement and WOM among the two sub-groups of young adult consumers in the fast food industry of South Africa. This indicates that product involvement (sub-group 1: β = 0.346, t = 4.075, p = 0.000; sub-group 2: β = 0.367, t = 3.445, p = 0.001) has a positive influence on the level of WOM, thus validating H_2a-b. The findings of De Meyer and Petzer (2014) and Voyer and Ranaweera (2015) support this observation by suggesting that an improvement in product involvement can lead to the spread of favourable WOM. In addition, Delafrooz et al. (2019) contend that consumers must first be involved with an offering before engaging in WOM. Therefore, product involvement is an essential element in fostering WOM among young adult consumers.

The study established that there is a direct linkage between product involvement and engagement in eWOM among the two sub-groups of young adult consumers in the fast food industry of South Africa. Consistent with H_3a-b, the results from Table 5 contend that product involvement (sub-group 1: β = 0.190, t = 2.140, p = 0.034; sub-group 2: β = 0.449, t = 4.386, p = 0.000) exerts a significant impact on eWOM. This is congruent with the work of Krishnamurthy and Kumar (2018) and Prasad et al. (2017), who found that increased levels of product involvement promote the spread of eWOM. Zha et al. (2016) concur that consumers who are more involved in their purchase decision concerning a fast food operator are more inclined to engage in eWOM behaviour. This suggests that the creation of favourable product involvement can elicit positive impressions, leading to the sharing of eWOM among young adult consumers in the fast food arena.

In the light of the preceding discussion, Table 7 provides a summary of the findings for the different hypotheses that were explored in this study. On the basis of these findings, it is evident that only two hypotheses (H_2a-b and H_3a-b) were supported, while three hypotheses (H₁, H₄, and H₅₎ were rejected.

Table 7. Summary of findings from hypothesis testing

Hypothesis	Variable	Variable	T	Sig.	Finding
H1	Product involvement (sub-group 1 and sub-group 2)		−0.05	0.964	Not supported
H2a	Product involvement	WOM	4.075	0.000	Supported
H2b	Product involvement	WOM	3.445	0.001	Supported
H3a	Product involvement	eWOM	2.140	0.034	Supported
H3b	Product involvement	eWOM	4.386	0.000	Supported
H4	WOM (sub-group 1 and sub-group 2)		−1.62	0.197	Not supported
H5	eWOM (sub-group 1 and sub-group 2)		−0.05	0.964	Not supported

6. Theoretical and practical contributions

The current study offers both theoretical and practical contributions by providing insights from a young adult’s perspective on how product involvement can lead to WOM and, in turn, promote the spread of eWOM in the fast food industry of an emerging economy such as South Africa. The research findings confirm the direct relationship between product involvement, WOM, and eWOM among the two sub-groups of young adult consumers, pointing to the importance of cultivating product involvement in an effort to stimulate WOM and promote the spread of eWOM. The study also extends the knowledge on the current debate and understanding of how generational differences affect the perceptions and behaviour of consumers about product involvement, WOM, and eWOM. Furthermore, the study provides reliable and valid measurement items that can be used in future research to investigate the linkages between product involvement, WOM, and eWOM. Last, the proposed theoretical model has been empirically validated, and can be tested in different market settings to exploit the interrelationship between the three constructs in this study.

From a practical standpoint, the study contributes by generating an understanding of how fast food operators can approach and improve the levels of product involvement in an effort to stimulate WOM and, in turn, promote the spread of eWOM. The outcome of this will depend on whether the customer perceives product involvement and WOM as positive. Delafrooz et al. (2019) postulate that fast food operators must acknowledge that customers’ needs are increasingly changing due to a complex business environment, fuelled by competition and market changes, and that their preferences shape how they evaluate product information and become involved in product purchases. It is therefore important for fast food operators to create a personalised and memorable experience before and after the consumption of their offerings in order to induce customers to develop favourable feelings about the business and to stimulate their level of product involvement.

From a theoretical standpoint, the study presents three contributions. First, the current research findings enrich the ELM theory by offering a greater clarification on how the cultivation of product involvement can stimulate eWOM behaviour among young adults. The results align with the central route of the ELM, where young adult consumers exhibit favourable levels of product involvement through a continuous review of product information related to the offerings of fast food operators, thereby creating positive impressions in their minds, and ultimately leading to the spread of eWOM. Second, it was established that there are insignificant differences between the two sub-groups of young adult consumers in terms of product involvement, WOM, and eWOM. This finding differs from previous studies, and suggests that young adult consumers share similar experiences, needs, and behavioural responses about fast-food operators in an emerging economy such as South Africa. It can therefore be argued that young adult consumers should be treated as a homogeneous group, and that marketing communications and product offerings can be tailored to their specific requirements in order to establish closer ties with them. Further to this, research on young adults’ perceptions about product involvement, WOM, and eWOM is lacking in the fast food industry in an emerging economy such as South Africa. This finding is important, as it demonstrates that future researchers should explore the perceptions of young adults by developing useful measurement scales that assesses their level of product involvement, WOM, and eWOM in emerging markets.

Third, the study provides an enriched understanding of the effect of product involvement on WOM and eWOM among fast food operators in an emerging African economy. The research study offers an updated model that uncovers how the cultivation of product involvement can have a direct impact on WOM and, in turn, lead to the spread of eWOM. This model also contributes to the development of the WOM literature by providing useful measurement constructs that could be used by researchers to understand the extent of the interrelationship between product involvement, WOM, and eWOM. Additionally, insight into these unique set of interrelated factors is essential for exploring eWOM behaviours from a young adult perspective and has important implications for marketing theory. These implications broadly encompass a deeper understanding of the role and importance of product involvement, the relevance of its theoretical grounding within the ELM theory, and its underlying impact on stimulating the spread of WOM and eWOM among young adults. The positive influence of product involvement on WOM and eWOM can thus assist fast food operators to retain profitable customers who will buy repeatedly and spread favourable information about their offerings.

7. Managerial implications

First, the research findings indicated that there is no statistical difference in the perception levels of the sub-groups of young adult consumers about product involvement, WOM, and eWOM. This implies that the two sub-groups perceived the levels of product involvement, WOM, and eWOM in the same way, suggesting that young adults should be treated as a homogeneous group. Fast food operators should invest in conducting focus groups with a representative panel of young adult consumers to understand their attitudes and behaviours, in order to offer them products that fulfil their expectations and promote their level of product involvement. This can be further achieved by establishing a panel discussion with a targeted group of young adult consumers every six months to gain insights in improving the menu, quality, and variety of meals and the pricing and delivery system in order to create favourable impressions in the minds of consumers, thus leading to increased levels of product involvement, which promotes positive WOM and, in turn, the spread of eWOM. Product involvement can be further strengthened by stimulating interest in, and commitment to, the offerings of a fast food operator by providing a consistent service at all times, and by using loyalty programmes (e.g., “thank you” emails, customer appreciation week posts on social media, meal vouchers, etc.) to reward loyal customers, thus soliciting WOM and eWOM behaviours. The use of focus groups and panel discussions and increasing customers’ interest levels will thus guide the owners of fast food operators on how this younger generation perceive the extent of product involvement, WOM, and eWOM so that policies and procedures are put in place to ensure that their requirements are fully met.

Finally, the study found that product involvement is positively related to WOM and eWOM among the sub-group of young adults in the fast food environment. This indicates that fast food operators should increase product involvement by creating an atmosphere that enables consumers to develop positive feelings about their offerings, leading to positive WOM and, in turn, stimulating eWOM. Fast food operators can address this by engaging with customers both in-store and online. From an in-store perspective, employees of fast food operators must display a caring attitude when serving customers by ensuring that they always greet them with a smile whenever they visit the store, offer empathic advice, and deliver quality meals within the required time-frame. This will result in customers developing favourable impressions about the fast food, and thus engaging in WOM by sharing their experiences with their peers and families. In an online setting, fast food operators must ensure that they employ well-trained online personnel who will engage with customers about their queries, complaints, and suggestions on their social media pages and website, and in email communication. Through this level of engagement, customers will feel more valued as their service requests are addressed by fast food operators, improving their level of product involvement and encouraging the spread of positive eWOM. As a result, these two elements for fostering product involvement are essential, and need to be present at all times, as their influence can stimulate greater positive WOM intention, leading to higher levels of eWOM.

8. Limitations and future research opportunities

The results of this study were based on the perceptions of young adult consumers in the fast food industry of an emerging economy (South Africa), and cannot be generalised to other industries in that country or in other parts of the world. The proposed theoretical model is limited to understanding the interrelationship between product involvement, WOM, and eWOM in a generational context. Future studies could exploit this model by including other factors that might be relevant and influential in the spread of eWOM. The study could also be expanded to include other generational groups in major provinces in South Africa to achieve a greater generalisation from the results, or it could be tested in other emerging markets across the globe.

9. Conclusion

The purpose of this study was to investigate the linkage between product involvement, WOM, and eWOM from a generational perspective in the fast food industry of South Africa. The results indicated that there is a lack of statistical evidence to suggest a difference in the perceptions of the two sub-groups of young adults about product involvement, WOM, and eWOM; thus H₁, H₄, and H₅ were not supported. Interestingly, the study found that product involvement exerted a positive and significant influence on the levels of WOM and eWOM, and so H_2a-b and H_3a-b were accepted. This implies that product involvement is an essential catalyst for generating WOM and, in turn, promoting the spread of eWOM in the fast food arena. Furthermore, the impact of this study was to provide practical strategies for how fast food operators can understand the interrelationship between product involvement, WOM, and eWOM. The application of the recommended strategies geared to understanding the motives and behaviours of young adults, and the cultivation of product involvement, could create a competitive advantage by stimulating positive WOM, and so encourage the spread of eWOM in the fast food industry of an emerging economy such as South Africa.

Ethical clearance

Ethical clearance (Ethics approval No: EWM001/15) was granted by the Department of Marketing Management Research and Ethics Committee (DREC) at the University of Johannesburg for the purpose of conducting a research involving human participants.

Originality statement

This research paper has not been published elsewhere and has not been submitted simultaneously for any other publication purposes.

Additional information

Funding

The authors received no funding for this research.

Notes on contributors

Aobakwe Ledikwe

Mr Aobakwe Ledikwe is a lecturer and PhD candidate at the Department of Marketing Management at University of Johannesburg in South Africa. He primarily lectures Sales Management and Digital Marketing at Bunting Road campus. His research interests are focused on branding and omni-channel management. He can be reached via [email protected].

Beate Stiehler-Mulder

Dr Beate Stiehler-Mulder is a Senior lecturer, Marketing Specialist and Researcher and the Marketing Coordinator for the UJ Department of Marketing Management. She holds a PhD in Industrial Economics and Management from the Royal Institute of Technology (KTH) in Stockholm, Sweden. She can be reached via [email protected].

Mornay Roberts-Lombard

Prof Mornay Roberts-Lombard is a Professor in Marketing Management at the University of Johannesburg in South Africa. His areas of specialisation are Relationship Marketing and Customer Relationship Management (CRM). He has published numerous articles in peer reviewed journals and delivered conference papers at various international platforms in his fields of expertise. He can be reached via [email protected].