An innovation is a new or novel idea for a product, service, or process, or an enhancement to those offerings (Hivner, Hopkins, & Hopkins, 2003). Diffusion is the process by which an innovation is communicated through specific channels over time among members of a social system that are linked via networks (Rogers, 1995). Thus, innovation diffusion involves the capacity to spread the production and the use of an innovation in practice through the social network structure of a group of stakeholders (Muzzi & Kautz, 2004; Dosi, 1988; Enos, 1962). Innovation diffusion is a central issue in high technology sectors of the economy, such as information technology and telecommunications, which continue to experience rapid technological changes and continuous innovation.
With network innovations, institutional networks have to be established to ensure that innovations are diffused successfully in the community of the adopters. Successful diffusion may require specific institutional actors, such as opinion leaders and change agents, to initiate and carry out interdisciplinary undertakings involving different stakeholder communities.
Structural network theorists argue that there are two aspects that determine the behavior and the propensity of a stakeholder toward adopting technological innovations: network density and centrality (Rowley, 1997; Nambisan & Agarwal, 1998). Network density characterizes the network as a whole. It measures its interconnectedness in terms of “the relative number of ties in the network that link actors together” (Rowley, 1997). The rationale of technologies is to provide social benefits that can be derived from positive network externalities associated with mass adoption (Papazafeiropoulou, 2004; Markus, 1990; Markus, 1990). Such technologies constitute “network innovations” that diffuse through social networks linking individuals and organizations (King, et al., 1994). The diffusion of network innovations, at the environmental level, which includes institutional and regulatory entities, is highly complex and has been relatively neglected in the literature. Therefore, this paper aims, through a general overview of the literature on the subject, to understand how the spread of social networks influence the economy of enterprise. In other words, the research question, which, the paper tries to answer, is, Can firms’ use of social networks influence the purchasing behavior of consumers, and if so, how?
In the first section, we study the main factors, according to academic literature, that can influence the purchasing behavior of consumers. Then, we proceed to a general overview of how and with whom social networks have spread, trying to figure out if and how they can influence the management of firms and organizations. Next, we investigate demand output and, in particular, the purchasing behavior of the consumer, trying to study if and how the use of social networks can influence the purchasing decisions of consumers. The fourth section describes the methodology that is based on the literature review of the topics covered by this work. Finally, we present the discussions and conclusions of the paper.
Consumers’ buying behavior has always been a popular marketing topic, extensively studied and debated over the last decades, and no contemporary marketing textbook is complete without a chapter dedicated to this subject. The predominant approach describes the consumer buying process as learning, information-processing, and decision-making activities divided into four steps:
1. problem identification
2. information search
3. purchasing decision
4. post-purchase behavior
According to much of the academic literature, demographic, social, economic, cultural, psychological and other personal factors, largely beyond the control and influence of marketing, have a major impact on consumer behavior and purchasing decisions.
Therefore, purchasing decisions are influenced by a complex combination of internal and external influences. Among these, Kotler and Armstrong (2010) identify group membership and social networks.
In recent years, online social networking has emerged as a strong component of social interaction. Social networking includes sites like blogs, networking websites such as YouTube, and entire virtual worlds like Facebook. The new social networking technologies offer a genuine communication channel that is much more credible than any advertising company (Anya, 2006).
Furthermore, the use of social networks increases the word-of-mouth effect. For this reason, marketers often try to identify or even create their own opinion leaders for their products, who address their marketing activities. Companies like Sony, Microsoft, McDonald’s, and Procter & Gamble create their own leader of opinions to facilitate the interactions between consumers (Voight, 2007).
Pellinen, Torma, Uusitalo, & Raijas (2010) indicate that financial skills and competence are based on financial knowledge and understanding, and are influenced by personal attitudes in spending and saving. For example, some consumers are reluctant to make most of their purchases with credit cards because of the fear that they may not be able to make full payment when their credit bills are due (Chakravorti, 2003). Some researchers have posited that age, income level, occupation, and marital status influence credit card holders’ spending behavior (Erdem, 2008; Ming-Yen, Chong, & Mid Yong, 2013). A number of interesting findings have been documented concerning age of credit card holders. Devlin, Worthington, and Gerrard (2007) found that the older the respondent, the more likely they are to possess one or more credit card. However, college students and young credit card holders, albeit possessing fewer credit cards, have been increasingly identified as contributors to credit card debt, compared to more senior card holders.
In the same way, several studies have looked at the impact of income level on credit card ownership and use. The findings are, however, not without varying conclusions. Devlin, Worthington, and Gerrard (2007) found that households with higher incomes tend to hold more credit cards. Nevertheless, due to their high income, they are more likely to pay off their credit card debts (Balasundram & Ronald, 2006). Slocum and Matthews (1970) argue that those from the lowest category of income always think wisely before making any kind of money-related decision.
Other studies also show that employment plays an important role in consumers’ purchasing decisions. In fact, Joo and Pauwels (2003) assert that occupation could influence a person’s consumption behavior. They found in their study that managers and those in the self-employed category are most likely to be heavy users of credit cards. On the other hand, students are often categorized as having an occupation, and it has been recognized that many students are living on the verge of financial crisis (Joo, Grable, & Bagwell, 2003; Manning, 2000). It is for this reason that usage of credit cards by college students has received increased visibility throughout the media.
Kinsey (1981) and Steidle (1994) also demonstrate that marital status and length of marriage affect spending behavior. Devlin et al. (2007) discovered that married respondents who participated in their research had more departmental store credit cards than those who are single, separated, or divorced. This is not difficult to understand, as married consumers are likely to have higher expenditures than nonmarried consumers.
Bank policies and attitude toward money also play a role in spending behavior. Many issuing banks and nonbanks offer incentives to entice consumers to apply for credit cards (Chakravorti, 2003). These incentives include no annual fees (which have been packaged as an annual fees waiver), cash rebates, point rewards, airline miles, installment payment plan, and discounts for identified purchases. Several researchers have argued that green consumer behavior is determined by a multitude of factors depending on type of behavior and involvement with the product and behavior. Stern (2000) presents four categories of determinants of green consumer behaviors:
· contextual forces
· attitudinal factors
· habits or routines
· personal capabilities
Contextual forces affect behavior indirectly through attitudinal factors. Consumption attitudes are context-specific dispositions that connect personal, stable values to actual consumption-level attitudes and behaviors (Cleveland, Kalamas, & Laroche, 2005; Pickett-Baker & Ozaki, 2008). Using this notion, the value-belief-norm theory has been developed and found valid in a wide variety of green consumer (curtailment) behavior contexts, such as household energy use, conservation behavior, and car use reduction (Stern, 2000; Poortinga, Steg, & Vlek, 2004; Kaiser, Hubner, & Bogner, 2005; Eriksson, Garvill, & Nordlund, 2006; Nordlund & Garvill, 2003).
VBN theory postulates that the factors that influence the relationship between values and actual behavior are personal moral norms that guide the actions of an individual. Personal norms, experienced as feelings of moral obligation to act, are postulated to create a willingness to act pro-environmentally. Personal norms are in this respect assumed to be formed by incorporating social norms into a consistent personal value system. The analysis of the literature has identified a number of factors that, in some way, affect the actions of consumers on the market.
Knowledge is one of the most decisive factors in achieving competitive advantages for supply chain partners. However, economic systems based on small and medium-sized enterprises (SMEs) are an important barrier for transitions from traditional economies to knowledge-based ones. Malhotra, Gosain, and El Sawy (2001) maintain that supply chain partners engage in interlinked processes that enable rich information sharing and building information technology infrastructures to process the information obtained from partners, a scenario that creates new knowledge. There are different ways of understanding and classifying knowledge, and most focus on knowledge types: tacit, explicit, individual, organizational, etc.
Nonetheless, there are many other factors to consider, among which the interdependence between knowledge and the organizational context stands out (Zheng, Yang, & McLean, 2010). The literature on innovation has been extremely broad incorporating perspectives as diverse as traditional structuralist approaches through to more process-oriented approaches. From the structuralist perspective, innovation is seen as a thing or entity with fixed parameters (e.g., a new technology or management practice), which is developed externally, packaged (“black boxed”) by suppliers, and then transferred to potential users where it can be seen to offer them competitive advantage (Wolfe, 1994). Structuralist perspectives have been criticized for underemphasizing the dependency of innovation on the social and organizational context (Scarbrough & Corbett, 1992). In contrast, process perspectives argue that innovation should be seen, not simply as a thing to be transferred from place to place, but as a complex, time-phased, politically-charged design and decision process often involving multiple social groups within organizations. According to this approach, innovation may be defined as the development and implementation of new ideas by people who over time engage in transactions with others in an institutional context (Van de Ven, 1986). Networking as a social communication process that encourages the sharing of knowledge among communities is center stage in process perspectives, which is reflected in this definition. Therefore, the need and the possibility for the management company to have new knowledge, creates the conditions for the creation of a lasting competitive advantage. The company management can effectively manage the resources at its disposal only if it has adequate information and if there is a regular flow of information between the different sectors.
One of the first things to be said about knowledge management (KM) and innovation is that definitions abound. A broad definition encompasses any processes and practices concerned with the creation, acquisition, capture, sharing and use of knowledge, skills, and expertise—whether or not these practices are explicitly labeled KM. There are also clearly organizational trends aligned to this focus on KM in innovation. In organizational terms, the new era is typified by flatter structures, debureaucratization, decentralization, and coordination through increasing use of information and communication technologies (ICT).
There have been several theoretical studies and research efforts to explain how societies can affect actors’ behaviors, decisions, and strategies. Granovetter’s (1985) impressive article claims that economic action is socially constructed and is determined by the ongoing relationships between economic actors. The social-embeddedness approach emerged as a critique to the “rational actor” assumption of classical and neoclassical economic models. According to many researchers, the social capital of individuals helps them find better jobs and affects occupational success. Organizations and individuals that have numerous network ties can use these connections to transfer knowledge, reach resources, and influence others in their environment (Gargiulo & Benassi, 2000).
The measurement of social capital in organizations and individuals is a central issue in social network research. The high frequency of interactions between two actors can create acquaintanceship, according to some authors. Tsai and Ghoshal (1998) state that the increasing interactions between actors in the course of time can lead to perceptions of mutual trust, and parties start identifying each other’s personal characteristics. Tymon and Stumpf (2003) similarly define social capital of actors as being developed by the transformation of arms-length ties into social relations in a period. Individuals who occupy central organizational positions usually have a high frequency of interactions, which may be sufficient to strengthen arms-length ties. Hence, the increasing number of reports woven into business practices enhances confidence of the different actors involved in the process of value creation. In this way, an engaging process guarantees the spread of awareness about new technologies and allows actors to create a climate of social cohesion and develop suitable processes of value creation for all stakeholders.
In fact, the leveraging of interfirm networks is increasingly considered a strategic resource that can be shaped by managerial action. Interfirm networks in this context are defined as consisting of the interactions and relationships organizations use to access knowledge. These may be in the form of alliances concerning formalized collaboration and joint ventures that allow access to the knowledge held by other actors as a means of facilitating innovation. Some studies introduce the concept of “network resources” to understand the advantages bestowed by such networks in allowing firms to leverage valuable information and resources possessed by their interfirm network partners. Gulati (2007) defines network resources as an umbrella concept to describe and understand the resources or capital generated by interfirm networks. The academic literature highlights the importance of the spread of social networks and how they can help improve relations within companies and organizations. On this track it becomes interesting to study whether and how the use of social networks can influence the purchasing behavior of consumers.
Social media has aroused a lot of interest among researchers and academics. As use of social media has increased at an amazing rate, companies have allocated an increasing budget to social media to communicate and reach customers. It is difficult to measure a real return on investment, though many studies have sought to quantify this sum.
There is a strong consensus among scholars and practitioners that developments in information technology (IT) affect several aspects of marketing in significant ways. In particular, the role of information technology in influencing buying behavior has been well recognized. A central concern in marketing, organizational buying behavior has been an important domain of scholarly investigation for a long time [78–82]. The use of new information and communications technology allows for a better flow of information and thus a greater connection between the different actors.
Social networking websites act as a platform for bringing together people with similar interests, beliefs, and ideas. Users of social networking websites connect to each other with the purpose of finding and exchanging content. Social networking can also be used are for self-disclosure and self-representation and thus create and manage a social or even a professional identity (Haythornthwaite & Wellman, 1998). Social media, especially social network sites, might be an important agent of consumer socialization because it provides a virtual space for people to communicate through the use of internet.
Social media provides three conditions that encourage consumer socialization among peers online. First, blogs and social networking sites all provide communication tools that make the socialization process easy and convenient (Muratore, 2008). For example, in virtual communities Ahuja and Galvin (2003) find that new members can be socialized easily into virtual groups and quickly learn task-related knowledge and skills through their interactions with other members. Second, increasing numbers of consumers visit social media websites to find information to help them make various buying decisions (Lueg & Finney, 2007). Third, social media provides vast product information and evaluations, acting as a socialization agent between friend and peer by facilitating education and information (Gershoff & Gita, 2006; Taylor, Lewin, & Strutton, 2011).
In line with this opinion Taylor, Lewin, and Strutton (2011) find that online consumers’ attitudes toward social network advertising depend on socialization factors (i.e., peers). According Wang, Yu and Wei (2012), online consumer socialization through peer communication also affects purchasing decisions in two way: directly (conformity with peers) and indirectly by reinforcing product involvement. Lueg and Finney (2007) further suggest retailers should encourage such communication by setting up tell-a-friend functions on websites because they find that peer communications online can influence consumers so strongly that they convert others into internet shoppers. The rapid growth of social media has revolutionized methods of communication and sharing information and interests, redefining the priorities of businesses and marketers and creating a new place of interaction and communication among people (Yogesh & Yesha, 2014).
A key business component of social media is that the tool allows consumers to evaluate products, make recommendations to contacts, and link current purchases to future purchases through status updates and Twitter feeds. In addition, the use of social media presents a valuable tool for firms in which a satisfied user of a product can recommend that product (good or service) to other potential users. Forbes and Vespoli (2013) investigate consumers who made a purchase of an item based on the recommendation of a peer or contact via social media. Their results indicate that consumers are basing their buying decisions on recommendations from people they would not consider “opinion influencers or leaders.” Sharma and Rehman (2012) find that positive or negative information about a product on social media has a significant overall influence on consumer purchase behavior. Thus, companies could influence opinions through the word-of-mouth effect among consumers by encouraging them to recommend their products through social. Online word-of-mouth communication allows consumers to share and obtain information from a variety of groups of people—not only from people they know—and it has a greater impact than traditional marketing tools marketing (Ratchford, Talukdar, & Lee, 2001; Lee, Cheung, Lim, & Sia, 2006; Katz & Lazarsfeld, 1955). In fact before making any purchasing decision, especially when buying something new, many consumers check other consumers’ recommendations (Kim & Srivastava, 2007).
Consumers researching on the online community had a sufficient amount of inquiries to make their decision. According to Li, Bernoff, Pflaum, & Glass (2007), 50 percent of adult users of online social networks recommend products that they like. One of the main advantages of online social networking is the ability to create and manage a diffuse network of weak ties. Information exchange on social networking websites happens between a larger and broader group of actors, compared to offline exchanges, and encourages the amassing of as many contacts as possible without deepening connections between the actors in order to gain business advantages. These benefits are transferred to consumer behavior.
In fact, the network effect is the extra utility that a consumer derives from the consumption of a good or the service when there is an increase in the network size of that good or service. The literature has identified two types of network effects (Katz & Shapiro, 1985). Growth in the size of the network increases the value of the network to all users. Facebook is a leading social network, and several authors have conducted studies on its use and how it can influence the purchasing behavior of consumers. Pietro and Pantano (2012) find that enjoyment is a key determinant of social networks usage as tool for supporting purchasing decisions. They also suggest a casual positive relationship between the attitude of customers toward social media and behavioral intention. Leerapong and Mardjo (2013) focus on the online purchase decision and through the study of Facebook, examine the factors that influence their decision. In this study, customers ranked in order of importance relative advantage, trust, perceived risk, and compatibility as the factors that encouraged or discouraged them from purchasing product through Facebook. The academic literature on the subject shows that the spread of social networks and their use may affect the behavior of social actors.
This study presents the results of the review of 111 academic papers selected from a large pool. Direct network effects have been defined as those generated through a direct physical effect of the number of purchasers on the value of a product (e.g., fax machines). Indirect network effects are seen in the market for systems, where the consumer’s utility function does not directly depend on the adoption decision of other consumers.
Selected papers demonstrated a focus on studying the effects of controllable factors that influence consumer behavior. The papers selected for the review were published after 1955. Out of the 111 papers, 64 were published between the years 2000 and 2014 and 47 between 1955 and 1999. The majority of papers were drawn from the Journal of Electronic Commerce Research, the Journal of Consumer Marketing, the Journal of Information Management, and the Journal of Internet Research. The elements identified in the literature as influencing online buying behavior were grouped into three main categories and five subcategories, each one including several of these elements. The selection of papers and the review and allocation of the web experience elements to one of the above categories and subcategories was done by the author, in order to ensure the conformity of the selection criteria. A minimum of one literature reference was necessary for including a given component in the classification.
Analysis of the literature has shown that social networks can bring about a certain degree of influence on the choices of consumers changing their buying behavior. In fact, the use of new information and communications technology allows a better flow of information and thus a greater connection between the different actors.
The use of social networks is a valuable tool that helps businesses increase the chances of survival through a the word-of-mouth effect among members of the virtual community. That finding is confirmed by the arguments of many researchers, but needs a further study to examine the reasons that are the basis of this influence.
Ahuja, M. K., & John, E. G. (2003). Socialization in virtual groups. Journal of Management, 29, 161–185.
Alwitt, L. F., & Berger, I. E. (1993). Understanding the link between environmental attitudes and consumer product usage: Measuring the moderating role of attitude strength. Association for Consumer Research, 20, 189–94.
Anya, K. (2006). The network unbound. Fast Company. Retrieved from https://www.fastcompany.com/56984/network-unbound
Arthur, W. B. (1996). Increasing returns and the new world of business. Harvard Business Review, 74, 100–109.
Balasundram, M., & Ronald, E. (2006). Perspectives on credit card use and abuse. Journal of American Society of Business and Behavioral Sciences, 2, 12–29.
Black, J. S., Stern, P. C., & Elworth, J. T. (1985). Personal and contextual influences on household energy adaptations. Journal of Applied Psychology, 70, 3–21.
Chakravorti, S. (2003). Theory of credit card networks: A survey of the literature. Review of Network Economics, 2, 50–68.
Cleveland, M., Kalamas, M., & Laroche, M. (2005). Shades of green: Linking environmental locus of control and pro-environmental behaviors. Journal of Consumer Marketing, 22, 198–212.
Devlin, J. F., Worthington, S., & Gerrard, P. (2007). An analysis of main and subsidiary credit card holding and spending. International Journal of Bank Marketing, 25, 89–101.
Dosi, G. (1988). The nature of the innovative process. London: Pinter Publishers.
Enos, J. (1962). Petroleum progress and profits: A history of process innovation. Cambridge, MA: MIT Press.
Erdem, C. (2008). Factors affecting the probability of credit card default and the intention of card use in Turkey. International Research Journal of Finance and Economics, 18, 159–171.
Eriksson, L., Garvill, J., & Nordlund, A. M. (2006). Acceptability of travel demand management measures: The importance of problem awareness, personal norm, freedom, and fairness. Journal of Environmental Psychology, 26, 15–26.
Forbes, L. P., & Vespoli, E. (2013). Does social media influence consumer buying behavior? An investigation of recommendations and purchases. Journal of Business & Economics Research, 11, 107–113.
Gargiulo, M., & Benassi, M. (2000). Trapped in your own net? Network cohesion, structural holes, and the adaptation of social capital. Organization Science, 11, 183–196.
Gershoff, A. D., & Gita, V. J. (2006). Do you know me? Consumer calibration of friends’ knowledge. Journal of Consumer Research, 32, 496–503.
Gulati, R. (2007). Managing network resources: Alliances,affiliations and other relational assets. New York: Oxford University Press.
Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91(3), 481–510.
Haythornthwaite, C., & Wellman, B. (1998). Work, friendship, and media use for information exchange in a networked organization. Journal of the American Society for Information Science 49, 1101–1114.
Hivner, W., Hopkins, S. A., & Hopkins, W. E. (2003). Facilitating,accelerating and sustaining the innovation diffusion process: An epidemic modeling approach. European Journal of Innovation Management, 6, 80–89.
Joo, S. H., Grable, J. E., & Bagwell, D. C. (2003). Credit card attitudes and behaviors of college students. College Student Journal, 37, 405–419.
Joo, S. H., & Pauwels, V. W. (2002). Factors affecting workers retirement confidence: A gender perspective. Financial Counseling and Planning, 13, 1–10.
Kaiser, F. G., Hubner, G., & Bogner, F. X. (2005). Contrasting the theory of planned behavior with the value-belief-norm model in explaining conservation behavior. Journal of Applied Social Psychology, 35, 2150–2170.
Katz, E., & Lazarsfeld, P. F. (1955). Personal influence: The part played by people in the flow of mass communications. Piscataway, NJ: Transaction Publishers.
Katz, M. L., & Shapiro, C. (1985). Network externalities,competition, and compatibility. American Economic Review, 75, 424–440.
Katz, M. L., & Shapiro, C. (1986). Technology adoption in the presence of network externalities. Journal of Political Economy, 94, 822–841.
Kim, Y., & Srivastava, J. (2007). Impact of social influence in e-commerce decision making. In Proceedings of the Ninth International Conference on Electronic Commerce.
Kotler, P. & Armstrong, G. (2010). Principles of marketing. London: Pearson Education.
King, J. L., Gurbaxani, V., Kraemer, K. L., McFarlan, K. L., & Raman, K. S. (1994). Institutional factors in information technology innovation. Information Systems Research, 5, 139–169.
Kinsey, J. (1981). Determinants of credit card accounts: An application of Tobit analysis. Journal of Consumer Research, 8, 172–182.
Lee, M. K. O., Cheung, C. M. K., Lim, K. H., & Sia, C. L. (2006). Understanding customer knowledge sharing in web-based discussion boards: An exploratory study. Internet Research, 16, 289–303.
Leerapong, A., & Mardjo, A. (2013). Applying diffusion of innovation in online purchase intention through social network: A focus group study of Facebook in Thailand. Information Management and Business Review, 5, 144–154.
Li, C., Bernoff, J., Pflaum, C., & Glass, S. (2007). How consumers use social networks. Cambridge, MA: Forrester Technologies.
Lueg, J. E., & Finney, Z. (2007). Interpersonal communication in the consumer socialization process: Scale development and validation. Journal of Marketing Theory & Practice, 15, 25–39.
Malhotra, A., Gosain, S., & El Sawy, O. A. (2001). Absorptive capacity configurations in supply chains: gearing for partner-enabled market knowledge creation. MIS Quarterly, 29, 145–187.
Manning, R. D. (2000). Credit card nation: The consequences of America’s addiction to credit. New York: Perseus Books.
Markus, M. L. (1990). Toward a “critical mass” theory of interactive media: Universal access, interdependence and diffusion. Sage Journals.
Ming-Yen, T. W., Chong, S. C., & Mid Yong, S. (2013). Exploring the factors influencing credit card spending behavior among Malaysians. International Journal of Bank Marketing, 31, 481–500.
Muzzi, C., & Kautz, K. (2004). Information and communication technologies diffusion in industrial districts. Dordrecht: Kluwer Academic Publishers.
Nambisan, S., & Agarwal, R. (1998). The adoption and use of national information infrastructure: A social network and stakeholder perspective. AISElectronic Library.
Nilsson, A,, Grisot, M., & Mathiassen, L. (2004). Translations in network configurations: A case study of system implementation in a hospital. Dordrecht: Kluwer Academic Publishers.
Nordlund, A. M., & Garvill, J. (2003). Effects of values,problem awareness, and personal norm on willingness to reduce personal car use. Journal of Environmental Psychology, 23, 339–347.
Papazafeiropoulou, A. (2004). A framework for the investigation of the institutional layer of IT diffusion: Using stakeholder theory to analyze electronic commerce diffusion. Dordrecht: Kluwer Academic Publishers.
Pellinen, A., Torma, K., Uusitalo, O., & Raijas, A. (2010). Measuring the financial capability of investors: A case of the customers of mutual funds in Finland. International Journal of Bank Marketing, 29, 107–133.
Pickett-Baker, J. & Ozaki, R. (2008). Pro-environmental products: Marketing influence on consumer purchase decision. Journal of Consumer Marketing, 25, 281–293.
Pietro, L. D., & Pantano, E. (2012). An empirical investigation of social network influence on consumer purchasing decision: The case of Facebook. Journal of Direct Data and Digital Marketing Practice, 14, 18–29.
Poortinga, W., Steg, L., & Vlek, C. (2004). Values, environmental concern, and environmental behavior: A study into household energy use. Environment and Behavior, 36, 70–93.
Ratchford, B. T., Talukdar, D., & Lee, M. S. (2001). A model of consumer choice of the internet as an information source. International Journal of Electronic Commerce, 5, 7–22.
Rogers, E. M. (1995). Diffusion of innovations. New York: The FreePress.
Rowley, T. J. (1997). Moving beyond dyadic ties: A network theory of stakeholder influences. Academy of Management Review, 22, 887–910.
Scarbrough, H., & Corbett, J. M. (1992). Technology and organization: Power, meaning, and design. London: Routledge.
Sharma, S., & Asad, R. (2012). Assessing the impact of Web 2.0 on consumer purchase decisions: Indian perspective. International Journal of Marketing and Technology, 2, 125–139.
Slocum, J. W., & Matthews, H. L. (1970). Social class and income as indicators of consumer credit behavior. The Journal of Marketing, 34, 69–74.
Steidle, R. P. (1994). Determinants of bank and retail credit card resolvers: An application using the life-cycle income hypothesis. Consumer Interest Annual, 40, 170–177.
Stern, P. C. (2000). Toward a coherent theory of environmentally significant behavior. Journal of Social Issues, 56, 407–424.
Taylor, D. G., Lewin, J. E., & Strutton, D. S. (2011). Friends, fans, and followers: Do ads work on social networks? How gender and age shape receptivity. Journal of Advertising Research, 51, 258–276.
Tsai, W., & Ghoshal, S. (1998). Social capital and value creation: The role of intrafirm networks. Academy of Management Journal, 41, 464–476.
Tymon, W. G., & Stumpf, S. A. (2003). Social capital in the success of knowledge workers. Career Development International, 8, 12–20.
Van de Ven, A. H. (1986). Central problems in the management of innovation. Management Science, 32, 590–607.
Voight, J. (2007). The new brand ambassadors. Adweek. Retrieved from https://www.adweek.com/brand-marketing/new-brand-ambassadors-91501/
Wang, X., Yu, C., & Wei, Y. (2012). Social media peer communication and impacts on purchase intentions: A consumer socialization framework. Journal of Interactive Marketing, 26, 198–208.
Wolfe, R. A. (1994). Organizational innovation: Review, critique, and suggested research directions. Journal of Management Studies, 31, 405–431.
Zheng, W., Yang, B., & McLean, G. N. (2010). Linking organizational culture, structure, strategy, and organizational effectiveness: Mediating role of knowledge management. Journal of Business Research, 63, 763–777.
Social Networks and the Buying Behavior of the Consumer by Rassega et al. from Journal of Global Economics is available under a Creative Commons Attribution 4.0 International license. © 2015, Rassega V, et al. UMUC has modified this work and it is available under the original license.