Abstract

Survey data

A mail survey was conducted in 2013 to collect information on social media use among members of the US forest products industry. Information collected in the survey included industry characteristics (e.g., manufacturing locations, size, net sales revenue, product types), e-commerce activities (e.g., types of e-commerce tools used, importance of e-commerce, concerns about e-commerce use, Web site information), social media use, perceived benefits from the use of social media as a marketing tool, impediments and constraints in the use of social media as a marketing tool, reasons for not using social media, and perceived effectiveness of social media as a marketing tool.

A directory of forest products industry participants under the Standard Industrial Classification (SIC) codes that includes lumber (SIC 2421), furniture (SIC 2511), and other forest products (SIC 2611, SIC 2621, and SIC 2631) was obtained from Dun & Bradstreet, which is the world's leading source of commercial information, including databases for businesses. A random sample of 1,600 companies was included in the survey. Dillman's (2000) Tailored Design Method was used in developing and administering the mail survey. Three mailings were sent to potential respondents to ensure a high response rate, including the initial mailing, a follow-up mailing (2 weeks after the initial mailing), and a final mailing (4 weeks after the initial survey).

Analysis

Summary statistics were calculated for the variables collected from the survey. In addition, a model was developed to examine the factors affecting social media adoption among the US forest products industry.

Social media adoption can be viewed as an adoption of a “new technology.” Adoption of technology by individuals has been explained by several theoretical models like the TAM (Davis 1989), which assumes that usefulness and perceived ease of use determine an individual's intention to adopt a system; the UTAUT (Venkatesh et al. 2003), a variation of TAM, which assumes that performance expectancy, effort expectancy, social influence, and facilitating conditions (e.g., age) are key determinants of usage intention and behavior; and the Innovation Diffusion Theory (Rogers 1995), which states that “innovation is communicated through certain channels over time among members of the social system.” These models have also been used to explain adoption behavior of organizations. Kalish (1985) developed a framework based on the Innovation Diffusion Theory and assumes that the adoption of a new product is characterized by awareness and adoption, which is controlled by advertising and price. Verheyden and Goeman (2013) used these models as the basis for investigating why organizations adopt social media for business purposes by specifically looking at company size. More recently, Nah and Saxton (2013) proposed a theoretical framework that integrates four key factors that affect organizational-level adoption and use of social media. These factors include strategy, capacity, governance, and environment. This article builds upon the theoretical framework proposed by Nah and Saxton (2013) and also incorporates the TAM by Davis (1989) and Rogers (1995). Specifically, it is hypothesized that social media adoption in the US forest products industry is influenced by organizational capacity, strategy, and perception about the ease of use or complexity of social media as marketing tools. Organizational capacity determines the firm's ability to successfully adopt a new technology. Thus, organizations that have the existing resources and capacities to support technology use are more likely to adopt the technology (e.g., social media). Organizations also choose specific strategies to achieve their goals (Nah and Saxton 2013). If these strategies can support or can complement social media use, then they are more likely to adopt social media in their marketing efforts. Last, the organizations' perception about the ease or complexity of social media will influence their decision to adopt. Organizations that have a negative perception about the usefulness of social media will less likely adopt it for fear that it will not work, compared with those that think highly of social media.

The response variable used in the empirical model was whether or not a firm uses social media as a marketing tool. The decision to use social media as a marketing tool was hypothesized to be influenced by three groups of variables: organizational capacity, strategy, and perception about ease of use. Organizational capacity can be measured by looking at organizational characteristics. Organizational characteristics have been shown to be potential determinants of the adoption process (Ghobakhloo et al. 2012). The variables representing organizational characteristics include number of manufacturing locations, number of employees, years in business or company age, net revenues, product types, and Web site content. The size of an organization can influence its adoption of new technology or innovation (Shook et al. 2002, Hewitt et al. 2011, Verheyden and Goeman 2013). It is hypothesized that smaller organizations are less likely to adopt social media compared with larger organizations. Smaller organizations usually have financial constraints, they lack professional expertise to handle new innovations, and are more susceptible to external factors (Rahab and Hartono 2012). Smaller organizations are faced with substantially more barriers to adoption. Thus, adoption of new technology like social media will be more difficult for them compared with larger organizations because of these constraints or barriers. On the other hand, larger organizations usually have the personnel and resources needed to adopt new technology like social media. The number of manufacturing locations and number of employees were included in the model as measures for company size and organizational size. Firm age has been shown to significantly influence adoption of technology (Das and Das 2012, Alderete and Gutierrez 2014). Firm age plays an important role in the innovative performance of firms (Yildiz et al. 2013) such as choosing to adopt social media. Durkin et al. (2013) also suggested that firm age as an influence on the adoption and utilization of social media should be looked at. Hence, firm age was included as a variable in the empirical model. It is hypothesized that older organizations are more likely to adopt social media. The reason for this is because older organizations have had the time to build the capacity and resources needed to support adoption of new innovations like social media. A company's financial assets as measured by net sales revenue are important for adoption of any new technology, as implementation of these new technologies comes at a price (Ghobakhloo et al. 2012, Nah and Saxton 2013, Verheyden and Goeman 2013). In fact, lack of financial resources is one of the barriers for social media adoption (Verheyden and Goeman 2013) and actually the largest barrier to B2B digital marketing usage (Järvinen et al. 2012). Thus, companies with higher financial capabilities are more likely to adopt social media because they are able to afford the cost associated with implementation. Product type (i.e., industrial products, consumer products) is another organizational characteristic that affects technology adoption (Porter and Millar 1985, Salmeron and Bueno 2006, Ghobakhloo et al. 2012). Companies that make products that have high value-added components (e.g., consumer products) are more likely to take advantage of social media to reach a broader consumer group. Consumer products can benefit from product differentiation offered by social media marketing. Another variable that was used to represent capacity is Web site content. Web site content describes the number of types of information found on the company Web site. Preexisting Web site capabilities can be an important tool that can support adoption of other Web-based technologies (Hackler and Saxton 2007, Kropczynski and Nah 2011, Nah and Saxton 2013). Companies with more information on their Web sites are more likely to adopt social media because they have a more developed platform to support social media use. More comprehensive Web sites can be regarded as additional resources that companies can take advantage of to successfully implement new information technology like social media (Hackler and Saxton 2007, Kropczynski and Nah 2011).

The second group of variables included in the model is strategy as measured by e-commerce usage of companies. Organizational strategies have implications on the use and adoption of new media like social media (Hackler and Saxton 2007). Many US companies, ranging from electric utilities to credit card firms to computer equipment manufacturers and even the forest products companies, have adopted e-commerce to maintain a competitive advantage in their businesses (Georgiou and Stefaneas 2002, Vlosky and Smith 2003, Arano and Spong 2012). E-commerce usage is represented by two variables: the number of e-commerce tools used and perceived importance of e-commerce. Companies that rely heavily on e-commerce tools and those that think that e-commerce is important are more likely to adopt social media. This is because e-commerce tools can strengthen and complement the use of social media and vice versa. E-commerce is often used by organizations to gain competitive advantage (Georgiou and Stefaneas 2002), and social media can be an additional tool in promoting a company's business.

The last variable included in the model measures companies' perception about ease of use as suggested by Davis' (1989) TAM. Specifically, this variable indicates whether or not a firm has a concern about the different challenges associated with using social media (e.g., ROI). Companies who expressed concerns about social media use are less likely to adopt social media. This is because a negative perception about the usefulness of a new technology or innovation may discourage organizations to try to use it.

The empirical model is therefore expressed as the following:

where β_i are the model coefficients and ɛ is the error term. The dependent variable (MEDIA) has two responses, “1” if the firm has adopted any type of social media and “0” if not. Descriptions of independent variables are reported in Table 1.

Table 1 Description of variables used in the regression model that examine the factors affecting social media adoption among responding companies in the US forest products industry.

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Because of the binary nature of the dependent variable, a binary logistic regression model was used to model the factors affecting social media adoption. Logistic regression is based on the cumulative logistic probability function and estimates the probability of a certain action given a set of categorical characteristics (Pindyck and Rubinfeld 1981):

where P_i is the probability that a company adopts social media, β_i the model coefficients, and X_i the independent variables. LIMDEP (Version 8.0) software was used to estimate the model parameters.

Survey results

Of the 1,600 potential respondents, a total of 596 surveys were returned because of undeliverable addresses and respondents who did not qualify. The total number of usable responses was 166, which accounts for a 16.53 percent response rate. To address the possibility of a nonresponse bias, the distribution of early respondents was compared with the distribution of late respondents on the basis of three demographic variables, firm size, years in business, and net revenue, using the Kolmogorov-Smirnov test (K-S test). The basic assumption is that late respondents are a proxy for non-respondents (Lin and Schaeffer 1995). Results of the K-S tests indicate that the distribution of the early respondents with respect to firm size (K-S statistic = 0.16), years in business, (K-S statistic = 0.04), and net revenue (K-S statistic = 0.15) was not statistically different from that of the late respondents. Thus, the survey results are judged to be sufficiently representative of the study population.

Factors affecting social media adoption

The majority of the companies surveyed (58%) have used some form of social media in their marketing efforts, with Facebook being the most common social media tool adopted. Although this adoption rate is relatively higher than the e-commerce adoption in the industry in general (e.g., Shook et al. 2002, Vlosky and Smith 2003, Arano and Spong 2012), there is still a tremendous opportunity in social media for these companies. This adoption rate lags behind compared with fast-growing companies (i.e., Inc. 500) in the United States, which had a reported rate of 83 percent in 2010 (Barnes and Jacobsen 2013) and large global organizations, which were reported to be at 99 percent (Sinclaire and Vogus 2011).

To understand better the social media use of forest products companies, it is important to examine the factors affecting adoption of this tool in their marketing strategies. The results of logistic regression analysis (Table 2) indicate that years in business (AGE), net sales revenue, product type, amount of available information on company Web sites, perceived importance of e-commerce, and perceived ease of use of social media were significant factors affecting social media use among the US forest products industry. Organizational strategies and internal resources and capacities can influence a company's decision to adopt new technology like social media (Corder 2001, Zorn et al. 2011, Nah and Saxton 2013). Thus, this study examined several industry characteristics as measures of organizational capacity affecting social media use. The results indicate that companies that have been established longer are more likely to adopt social media. For example, the odds of companies that have been in business longer adopting social media are 1.02 greater than those of relatively newer companies. This is because older organizations have had the time to build the capacity and resources to support adoption of new technologies. These older firms might have the technological experience necessary for adoption (Hollenstein 2004). However, theoretical arguments regarding the relationship between firm age and adoption are not conclusive (Hollenstein 2004). For example, although our results show a positive relationship between firm age and adoption, Das and Das (2012) found a negative relationship between firm age and information technology adoption.

Table 2 Results of the regression model that examines factors affecting social media adoption among responding companies in the US forest products industry.

^a See Table 1 for variable definitions.

^b * = significant at the 10 percent level; ** = significant at the 5 percent level; *** = significant at the 1 percent level.

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Likewise, a company's financial assets, as measured by net sales revenue, was determined to be significantly and positively related to social media adoption. The use of social media is not cost free; thus, to be successful, organizations must devote resources in terms of time and money (Nah and Saxton 2013). One of the most important elements preventing companies from adopting social media is a lack of resources (Verheyden and Goeman 2013), and this is particularly true for B2B companies. Thus, companies who have higher net sales revenues are better able to afford the investment in social media and are more likely to use social media in their marketing efforts. In the case of the US forest products companies, the odds of companies that generated net sales revenue of >$1 million adopting social media are 5.51 greater than those that generated <$1 million in net revenue.

The type of products that a company offers also influences its decision to use social media. In this study, companies that only offer industrial products (e.g., lumber, pallets) were less likely to use social media in their marketing strategies than those companies that offer both industrial and consumer products (e.g., furniture, flooring, cabinets). Companies with both sets of customers have a broader consumer group to reach; thus using social media can be more beneficial. Companies offering consumer products that usually have value-added components were more likely to take advantage of social media. This is consistent with Kozak's (2002) findings that industries selling products with higher-priced custom products were more likely to adopt information technology.

Companies that have comprehensive Web sites were also more likely to use social media than those without (odds = 2.09). This is because preexisting Web capabilities might constitute resources that companies can mobilize in pursuit of additional Web-based goals or might contribute to the ability of companies to successfully use information technology like social media (Hackler and Saxton 2007, Kropczynski and Nah 2011). These are companies that have more information available on their Web sites such as product pictures, language translation, customer testimonials, product design tools, software, product inventory, distribution lists, and customer service contacts.

Another factor that influences an organization's use and adoption of a new media (e.g., social media) is the strategy it uses in fulfilling its goals (Hackler and Saxton 2007). In this article, we looked at e-commerce as a strategy and therefore a factor that affects the decision to adopt social media. The results indicate that companies that regard e-commerce as important are more likely to adopt social media. Social media tools can complement and strengthen the use of e-commerce. E-commerce has played a significant role in promoting company businesses by making them more competitive (Georgiou and Stefaneas 2002). Social media can therefore be part of e-commerce strategies of companies.

Last, firms that do not have major concerns about the challenges (e.g., generating ROI to cover the costs) associated with the use of social media were more likely to adopt social media. Those that have concerns were less likely to adopt. This is consistent with Davis' (1989) TAM and Rogers' (1995) argument that that the more complex a technology is perceived to be, the slower will be its rate of adoption. Negative views about usefulness, staff unfamiliarity, and lack of training can slow down the adoption of any new technology like social media tools (Venkatesh and Davis 2000, Buehrer et al. 2005, Michaelidou et al. 2011).

Social media effectiveness and challenges

The adoption of social media did not change the marketing budget for most (68%) of the responding companies. About 30 percent reported an increase in their marketing budget as a result of social media use. Even though not many companies have increased their marketing budget, social media is still considered an effective tool for marketing by most of the respondents. Although maintaining social media sites can incur significant costs, especially if companies hire dedicated social media personnel to maintain their sites, signing up for social media accounts are essentially free and easily accessible (Chikandiwa et al. 2013). This can lead companies to think they do not need to allocate more money initially toward this effort. In the case of the forest products industry, social media adoption is still in its infancy—most companies have only started using social media within the last 5 years. Therefore, companies may not have the necessary dollar metrics to justify investment toward social media. In fact, the results indicate that a great majority (94%) of the responding companies do not measure the ROI from social media use. One of the reasons for this is that using social media in marketing is relatively new and companies do not have a good understanding yet on how to capture ROI from social media use. The nature of social media as a marketing tool (i.e., mostly involving human interactions) makes calculation of ROI challenging (Blanchard 2011, Vaynerchuk 2011, Paniagua and Sapena 2014).

Instead of focusing on ROI to measure social media effectiveness, forest products companies mostly focus on other quantitative metrics (non-dollar) as well as on qualitative metrics. The top three quantitative metrics used for evaluating social media effectiveness were (1) number of site visits, (2) number of social network friends, and (3) number of comments and profile views (Fig. 1). In terms of qualitative metrics used by companies to measure social media effectiveness, the three most commonly used metrics were (1) growth of relationships with key audiences, (2) audience participation, and (3) moving from monologue to dialogue with consumers (Fig. 2). From a marketing standpoint, focusing on these metrics rather than just the ROI may be a good social media strategy. According to Hoffman and Fodor (2010), returns from social media investment should not always be measured in dollars but also in consumer behavior (i.e., consumer investment). Focusing on consumer investments (e.g., number of site visits, comments) is an effective social media marketing strategy because it puts the brand to work for the customers by satisfying their needs to create, consume, connect, and control in the social Web. Such strategy takes into account the objective of both the brand and the online customer (Hoffman and Fodor 2010).

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When asked about the effectiveness of social media as a marketing strategy, approximately 94 percent of the respondents thought that the use of social media was somewhat effective to very effective (Fig. 3). Facebook was considered to be the most effective social media forum reported by the US forest products industry. This is not surprising, as this is the largest and most widely used social network today. In the United States, 71 percent of Internet users are members of Facebook (Chase and Knebl 2011) and it is also the most common social media tool used among the fast-growing companies in the United States (Barnes and Jacobsen 2013) and in other industries (Chikandiwa et al. 2013, Sago 2013, Lagrosen and Grunden 2014). The top three company goals addressed by social media are (1) increased exposure, (2) branding, and (3) lead generation (Table 3). These are also the main reasons why large global organizations use social media and reflect a proactive approach to customer engagement that aims to build repeat customers (Sinclaire and Vogus 2011).

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Table 3 Company goals addressed by social media that were recognized by forest products industry survey respondents.

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Table 4 presents the level of concern of social media adopters and non-adopters regarding the different challenges that the company may have in using social media. Except for generating ROI to cover costs, both adopters and non-adopters have the same level of concern on these potential social media challenges. Non-adopters were more concerned about generating ROI from social media than adopters. In general, no major concerns were expressed regarding potential challenges associated with the adoption of social media except for ROI concerns and ability to generate member and customer activity (i.e., mean level of concern, >3.5). As mentioned earlier, lack of resources is one of the important factors preventing companies from adopting social media. Thus, companies tend to worry about whether the ROI from social media will be enough to cover the associated costs of adoption. This is even more pronounced among non-adopters. This concern about ROI can also be attributed to the fact that it is more difficult to establish ROI from social media use. It can be difficult to identify the exact benefits of a social media strategy that can be used in calculating ROI (Arno 2014) and there are often significant reservations among marketers about their ability to measure ROI from social media (Adobe Systems Incorporated 2012). According to the results of our study, the majority of the forest products industries that employ social media in their marketing do not measure ROI at all. With regard to the ability to generate member and customer activity, both adopters and non-adopters of social media indicate that this is a relatively major concern. This may be attributed to the fact that tracking customer activities (e.g., site visits) is one of the major measures respondents used in evaluating social media effectiveness.

Table 4 Concerns faced by social media adopters and non-adopters among the responding US forest products companies in using social media.

^a Mean of Likert scale: 1 = not a concern; 2 = somewhat a concern; 3 = neutral; 4 = a concern; 5 = a major concern. Means with different letters across a given row are significantly different at the 10 percent level.

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