Research Article: 2024 Vol: 28 Issue: 1S
Asokan Durai, Bapuji Academy of Management and Research
Citation Information: Durai, A (2024). Product Involvement And Social Networking Engagement. International Journal of Entrepreneurship, 28(S1),1-11
Learning about consumption, brand preferences and purchase decision involves product involvement. Based on need and values, the involvement occurs during the buying decision process. Consumers having involvement are more likely to purchase product as a reaction to marketing and advertisement stimuli. Therefore, peer communication and product involvement impacts product attitude of a consumer and their purchase decision making process. Product involvement happens among consumers in social media through computer aided networks which entails the interaction among individual consumers on product or service. Thus product involvement decides on how individual consumer interacts about product or service in social media.
Peer Communication, Product Attitude, Product Involvement, Purchase Intention.
Marketing defines making beneficial relationships with customers, which includes acquiring new customers and retaining existing one (Kotler et al., 2013). WOM has a stronger impact on consumer behaviour than other types of advertisements (Engel et al., 1969). WOM consists of comments related to products and services (Arndt 1967). In the cyber world, it is defined as EWOM (Cheung et al., 2012) and is accessible to any customer (HennigThurau et al., 2004). This new form of WOM is more powerful in terms of triggering purchase intention. Thus this study is aimed at understanding the influence of product involvement on purchase decision in the context of social networking.
Consumers are empowered through a variety of platforms to post user-generated content (eg., blog, microblogs, forums, chat rooms, and SNS). Majority of the electronic communication studies have focused on consumer reviews. Here, the study has analysed the influence of product involvement in social networking communication. Electronic communication represents a new form of social communication content (stimuli) involving both information-seeking customers (receivers) and information sharing customers (communicators). This research has reviewed individual-level electronic communication and has identified variables related to the four key elements (responses, stimuli, receivers, and communicators) of social communication. The study further builds an integrated model and discusses the interrelationship among the various factors.
In recent studies, lack of information about the identity of authors of postings has been considered a weak side of product involvement (Keller, 2007). This is due to the fact that they have mostly focused on product communication which has been occurring in online forum sites and sites that have consumer reviews related to products. However, thanks to social networking sites, sharing of information not only occurs between unknown people but also between people who already know each other. This new way of product involvement might be more powerful in terms of triggering purchase intention. Therefore the aim of this study is to understand the influences of product involvement in purchase intention. The product involvement in the interactive medium affords opportunities to share experiences and keep each other updated about particular products or new ones. In other words, they use more emotional expressions.
Need for the Study
The consumer largely depends on how involved a consumer is. Not all consumers who consider buying are similar. The nature of consumers have already more knowledge, often review more than one source and spend more time considering buying a specific product based on the theory of the Elaboration Likelihood Model (ELM) developed by (Cacioppo et al., 1984). Consumer socialization theory (Moschis et al., 1978) is based on the premise that the behaviour of the learner is influenced by the norms, attitude and motivations. Modification in individual cognitive structure has been analysed by the cognitive development theory by Piaget. These two theories are the base for creating a model for understanding consumer socialization. The recent advance in online marketing is redefining behaviour of consumers and their evaluation process. The younger generation and computer savvy population entered into a new paradigm for evaluating the purchase process. This leads to the development various studies using product involvement and its influence in the redefinition of consumer behaviour. The recent models developed by (Wang et al., 2012) emphasize various models and conceptual frameworks throw light on the importance of product involvement and its influence on purchase intentions of customers.
Consumer socialization consists of tie strength with peers and identification with peer group as antecedent variables and relationships built through product involvement have strong influence on purchase decision (Kozinets, 1999; Okazaki et al., 2009; Zhang et al., 2009). The social setting in which learning takes place can directly and indirectly affect the learning process (Moschis et al., 1978). Tie strength refers to “the potency of the bond between members of a network” (Mittal et al., 2008). According to (Granovetter, 1973), social ties can be classified as strong or weak. Strong ties such as family and friends form stronger and closer relationships within an individual’s personal network and provide material and emotional support (Goldenberg et al., 2001). Weak ties are made of acquaintances and colleagues with different cultural and social backgrounds. In other words, social networking sites allow consumers to connect with both close personal contacts such as family members and close friends (strong ties) and less personal contacts that include acquaintances and colleagues (weak ties). Both types of personal contacts may lead to consumers’ social media peer communication as well as product involvement behaviour in social networking sites (Chen et al., 2008).
Brown et al. (1987) found that macro level weak ties (e.g. flows of communication across groups) allowing information to disseminate and spread among distinct groups. On the other hand, micro level strong ties (e.g flows within dyads or small groups) are activated for the flow of referral behaviour. Consumers’ product choices may be influenced by both stable and intimate ‘strong tie’ interactions and randomly or remotely connected ‘weak ties’. Strong ties exert a more significant impact at the individual and small group level. However, the asynchronous and connective characteristics of social media allow weak ties to expand their potential influence by extending consumers’ personal networks to external communities or groups. This accelerates conversations to a large-scale network. The perceived tie strength based on both strong and weak ties developed via social media stimulates consumers to communicate with one another and disseminate product-related information.
Identification with the peer group is defined as the conception of the self with the features of self-inclusive social category, which renders self stereotypically interchangeable with other in-group members (Hogg, 1992). Identification with peer group develops we-intention with the group members and willingness to engage in community activities and places value on relationship with the community (Algesheimer et al., 2005). Ties between the individual and group members proceeds and contributes to their identification with the peer group. Harmonious relationship with group members entails interaction of consumers with other similar members on product or service related consumption.
Tie strength with peers is the degree of relationship an individual has with peers through social media. The relationship may be very close, such as with friends or casual, such as with strangers and acquaintance. Peer communication can be influenced by significant tie strength (Brown et al., 2007; De Bruyn et al., 2008). Useful knowledge can be transferred better by strong ties than weak ties (Smith et al., 2005; De Bruyn et al., 2008).
Peer communication is one of the information cues that would significantly influence consumer search process and choice decisions. Consumers are “adaptive decision makers” to specific environments and tasks (Payne et al., 2008). In information-intensive environments, consumers seek others’ opinions as a means of managing the perceived risks associated with cognitively demanding tasks. Information provided by peers is used as predominant source of pre-purchase information for consumers (Beatty & Smith., 1987). Past research suggests that consumers prefer to rely on peer communication than marketer provided product attributes because peer communication is easier to use, or is perceived (Herr et al., 1991) as being more trustworthy as it is based on peer experiences (Smith et al., 2005). Therefore, the present study builds on this link and explores the roles of product involvement.
Identification with peer group merits research in the area of social media communication. It refers to the degree to which individuals who interact with one another are similar in certain attributes (Bhowmik et al., 1970). (Festinger, 1957 & Gilly et al., 1998) found that members of social networks are tends to be similar in certain characteristics such as beliefs and attitudes apart from age, race and gender. Interpersonal communications are more likely to occur between them and the group; as a result exchange of information most frequently occurs between those who shares common qualities (Bhowmik & Rogers, 1970). The greater the similarity between communicators better is the perceived ease of communication, which facilitates the flow of information (Price & Feick, 1984). Hence, individuals with high levels of identification with peer group are more likely to engage in peer communication when making product choices.
Consumers steer their social interaction towards consumers similar to themselves (Best & Krueger, 2006) despite the diversity of internet users. Identification with peer group plays a significant role in determining credibility perceptions and influencing the persuasive process on social media. Social media excels at attracting identical consumers and this phenomenon increases the likelihood of consumer engagement in peer communication. Hence, there exists a relationship between tie strength with peers and the identification with peer group on peer communication adopted by an individual during purchase decision process.
Product Involvement
Product involvement sensitises consumers to advertising and marketing stimuli based on consumer needs, interest or values (Zaichkowsky, 1985). Two forms of peer influence have been identified, normative influence and informative influence (Bearden et al., 1989). Peer groups affiliated to some social group are pushed by normative influence and modify their behaviours and attitudes based on peer’s expectations (Bearden et al., 1989). Social media group members face conformity pressure when they make purchase decisions. On the other hand, informational influences drive people to seek information from peers by observing others’ behaviour and may seek product or service related information form knowledgeable peers. The influence may be by positive and negative reviews, comments, discussions, suggestions or in written messages.
Attitudes are learned through past associations or experience as well as through information processing. Attitude has three elements – cognitive, affective, and conative. Cognitive refers to knowledge or the awareness of a brand affective is the positive or negative feeling associated with a brand and conative is the intention to purchase (Smith et al., 2005). Positive and negative experience learned with a product, usually by an individual, is attitude. Attitude based on the indirect experience depends on the expertise and the credibility of information.
Product information through social media is provided by peer communication. Information provided by peers is used as predominant source of pre-purchase information for consumers (Beatty & Smith, 1987). Interaction between the consumer and socialization agent results in acquisition of learning for consumption related behaviours or attitudes. Learning process through social media involves modeling, reinforcement and social interaction (Lueg et al., 2006). An Individual is socialized to adopt a particular behaviour or intention (Moschis et al. 1978). A mechanism of imitating or mimicking the socialization agent exists because it is desirable or meaningful to the learner (Moschis et al., 1978). The learner is motivated to adopt some behaviours or intention because of a reward or punishment offered by the socialization agent. The reinforcement can be delivered on social media via written communication. Peer pressure also motivates the consumer to purchase or lack of purchase could be perceived to result in some form of punishment.
Conceptual Model and Data Analysis
Cognitive, affective and behavioural attitudes are affected by consumer communication as predicted by the consumer socialization theory (Ward, 1974). Consumption related skills; attitudes in the marketplace and knowledge are learned by consumers through the socialization process. The socialization framework outlines the learning processes and the role of consumers in a society (Moschis et al.1978; Churchill et al., 1979; De Gregorio et al., 2010s). A cognitive development model and social learning theory (Moschis et al., 1978) offers two theoretical perspectives on understanding and predicting consumer-to-consumer information transmission. Cognitive and psychological aspects form the focus for the former while the latter highlights environmental learning sources or peers as ‘socialization agents’. Norms, motivations, attitudes and behaviour are transmitted by the socialization process (Moschis et al., 1978; Shim 1996; Kohler et al., 2011). Consumer socialization process among non-family members has been explained by the consumer socialization theory (Ahuja et al., 2003; De Gregorio et al., 2010; Taylor et al., 2011).
Social networking sites act as agents of consumer socialization and provide a virtual space for communication through the Internet (Lueg et al., 2006; Muratore et al., 2008; Zhang et al., 2009; Kohler et al., 2011). Consumer socialization among peers is enhanced by three conditions. First, instant messages through electronic communication help in sharing of knowledge and skill through interactions among members. Second, consumers use social media websites for consumption related decisions (Lueg et al., 2006). Third, they provide vast information and evaluation quickly (Gershoff et al., 2006; Lewin et al., 2011). Socialization factors show the influence of EWOM and convert them into shoppers. Based on the theory of socialization, the study proposed a model to explain impact of social media on purchase decision. The study also explains the interaction of these constructs and develop hypothesis based on the model.
The demographic profiles of 508 respondents are shown in Table Approximately 56.8% of participants were frequent users of the social networking site Facebook. 17.1% used Google Plus, 10.1% used LinkedIn, 7.1% of the participants used Twitter and 8.9% used other social networking sites. There were 369 (72.6%) men and 139 (27.4%) women. Most of the respondents, 305 (60%) were in the age group of 17-26 and 120 (31%) respondents had an annual of income of 2.4 lakhs. Among them, 335(65.9%) had a bachelor’s degree, 130 (25.6%) had a master’s degree, 10 (2%) completed high school and 33 (6.5%) belonged to other categories of education including diploma. 388 (76.4%) of respondents were employed, 120 (23.6%) of them were students. Regarding social networking site usage, approximately 15.6% of the participants spent more than 3hours per day on their chosen social networking site and 10.1% spent 2-3hours per day, 60.2% spent 1-2 hours per day, and 14.2% spent less than an hour every day. The demographic characteristic related to the total Chennai population represented a higher level of young and educational respondents.
Reliability
Reliability refers to consistency of results over time. The questionnaire under study is said to be reliable if the results can be reproduced with identical methodology. Validity and reliability are the two important aspects of any research. Reliability estimates can be performed using internal consistency. Reliability is estimated by combining questions of the questionnaire that measures an identical concept. Cronbach’s alpha (Cronbach, 1951) is the measure of internal consistency. It computes consistency by using correlation values of the questions of a questionnaire. Cronbach’s alpha was measured for the entire questionnaire. The results are shown below table 1.
| Table 1 Reliability of Facators | ||
| Factors | Cronbach’s Alpha | Number of Items |
| Tie Strength with Peers | 0.84 | 4 |
| Identification with Peer Group | 0.91 | 5 |
| Peer Communication | 0.87 | 5 |
| Product Attitude | 0.89 | 3 |
| Product Involvement | 0.8 | 5 |
| Purchase Intention | 0.9 | 3 |
Acceptable level of Chronbach’s alpha is 0.70 (Nunnly, 1978). This questionnaire can be considered as a research tool as the entire questionnaire’s Chronbach’s alpha and factors fell within the accepted limits.
Validity
Construct validity refers to a set of measured items or constructs that reflect the latent construct to measure (Hair et al 2010). It determines if the item measures chosen from a sample are indicative of the actual true scores of the population. It focuses on the degree of assessment of the targeted variable. It is assessed by discriminant and convergent validity see table 2.
| Table 2 Discriminant Validity | ||||||
| Factors | 1 | 2 | 3 | 4 | 5 | 6 |
| TS | 0.77 | |||||
| IPG | 0.4 | 0.81 | ||||
| PC | 0.35 | 0.36 | 0.75 | |||
| PRA | 0.17 | 0.1 | 0.31 | 0.81 | ||
| PRI | 0.23 | 0.17 | 0.38 | 0.54 | 0.69 | |
| PI | 0.22 | 0.18 | 0.25 | 0.43 | 0.37 | 0.8 |
This index indicates the extent to which a construct is different from other constructs. For discriminant validity to exist, the AVE must be higher than the squared correlation between the constructs. According to the values in Table, the above condition was met in all cases.
Factor Structure Analysis
Muthen et al. (1992) have suggested that if the variables have skewness and kurtosis from -1 to +1, then estimating parameters with maximum likelihood method is acceptable. As maximum likelihood method is the default method in Amos.21, the check for skewness and kurtosis is a prerequisite. The skewness and kurtosis of the various factors of the questionnaire are given in the Table 3.
| Table 3 Skewness and Kurtosis | ||
| Scale | Skewness | Kurtosis |
| Tie Strength with Peers | 0.15 | -0.7 |
| Identification with Peer Group | -0.11 | -0.55 |
| Peer Communication | -0.24 | -0.68 |
| Product Attitude | -0.38 | -0.81 |
| Product Involvement | -0.36 | -0.97 |
| Purchase Intention | -0.3 | -0.79 |
The values of skewness and kurtosis of the parts of the questionnaire were found to fall within acceptable range.
Examining the factor loadings results of every item of the questionnaire gives discriminant and convergent validity (McLure, Wasko & Faraj, 2005). Factor loading of an indicator should be higher than the construct of it than other factors (Chin, 1998; McLure Wasko & Faraj, 2005). The loading is presented in Table; the factor loading of all the indicators of value greater than 0.5 were taken for further analysis. Developing the measure extracted 53 items from the pool of 25 items see table 4
| Table 4 Rotated Component Matrix | |||||||||||
| Items | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
| Tie1 | 0.828 | ||||||||||
| Tie2 | 0.805 | ||||||||||
| Tie3 | 0.845 | ||||||||||
| Tie4 | 0.595 | ||||||||||
| Iden1 | 0.81 | ||||||||||
| Iden2 | 0.803 | ||||||||||
| Iden3 | 0.793 | ||||||||||
| Iden4 | 0.811 | ||||||||||
| Iden5 | 0.815 | ||||||||||
| Peer1 | 0.686 | ||||||||||
| Peer2 | 0.767 | ||||||||||
| Peer3 | 0.788 | ||||||||||
| Peer4 | 0.809 | ||||||||||
| Peer5 | 0.703 | ||||||||||
| Pro_A1 | 0.813 | ||||||||||
| Pro_A2 | 0.846 | ||||||||||
| Pro_A3 | 0.771 | ||||||||||
| Pro_I1 | 0.587 | ||||||||||
| Pro_I2 | 0.679 | ||||||||||
| Pro_I3 | 0.685 | ||||||||||
| Pro_I4 | 0.718 | ||||||||||
| Pro_I5 | 0.769 | ||||||||||
| Pur_Int1 | 0.808 | ||||||||||
| Pur_Int2 | 0.803 | ||||||||||
| Pur_Int3 | 0.828 | ||||||||||
The above table, shows the factor loadings of all constructs involved in the study. The variance explained by the eleven factors was 65.73. In the principal component analysis, some of the poorly loaded and cross loaded items were deleted. (Carmines & Zeller., 1979) recommend higher factor loadings, which means that the shared variance between the construct and its indicators is larger than the variance of the error. Table shows that the loadings for all items were above the minimum.
Peer Communication, Product Involvement and Product Attitude
To find the relationship between peer communication, product involvement and product attitude stepwise regression was carried out and the results are shown in Table 5 and Table 6.
| Table 5 Table (A) Regression: Peer Communication, and Product Involvement and Product Attitude– Model Summary | ||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
| 1 | 0.540 (a) | 0.292 | 0.29 | 1.422 |
| 2 | 0.552 (b) | 0.304 | 0.302 | 1.411 |
| a. Predictors: (Constant), Product Involvement | ||||
| b. Predictors: (Constant), Product Involvement, Peer Communication | ||||
| Table 6 Regression: Peer Communication, Product Involvement and Product Attitude- Coefficients | ||
| Factor | Beta | Sig. |
| Product Involvement Peer Communication | 0.494 | 0 |
| 0.121 | 0 | |
| a Dependent Variable: Product Attitude P < 0.001 | ||
R square value shows the efficiency with which much variance in the dependent variable (Product attitude) can be predicted by the predictor or independent variables (Product Involvement and Peer communication). For the model that takes the predictor of product involvement and peer communication, the R square value was 0.30. This shows that 30% of variance in the consumer product attitude was explained by the product involvement and peer communication at significant (p < 0.001). The beta values for the factors were 0.494 and 0.121 indicating strength in predicting product attitude. The beta values were positive. Hence, it can be concluded that product involvement and peer communication plays a moderate role in determining the product attitude of a consumer. Hence, as the level of product involvement and peer communication increases, product attitude of consumers is also likely to increase.
To test whether product involvement predicts purchase decision of a consumer, SEM was applied and the results are shown in table 7.
| Table 7 Table: SEM Fit Indices | |||||
| Model | TLI | CFI | GFI | RMSEA | Ȥ2 /df |
| Facture structure of purchase intention | 0.98 | 0.98 | 0.96 | 0.045 | 2.033 |
TLI, CFI, GFI are all greater than 0.9 and RMSEA of 0.45 indicates a good fit. All the three factors of SMPC predict purchase decision moderately. All the factors of SMPC viz. peer communication, product involvement and product attitude are significant in predicting purchase decision.
Intention can be defined as the degree of perception of a particular group buying behaviour and it applies the theory of reasoned action (TRA) (Fishbein & Ajzen., 1975). According to TRA, people’s purchase intention is influenced by product involvement and this is corroborated by the current research results.
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Received: 30-Jul-2023, Manuscript No. IJE-24-14152; Editor assigned: 03-Oct-2023, Pre QC No. IJE-24-14152(PQ); Reviewed: 18-Oct-2023, QC No. IJE-24-14152; Revised: 24-Oct-2023, Manuscript No. IJE-24-14152(R); Published: 31-Oct-2023