Web 1.0, Web 2.0, and mobile technologies help small businesses to serve as the growth engine of the U.S. economy. We propose and test a model to compare the influence of organizational, environmental, and technological factors on the decision of small businesses owners, CIOs, and CEOs to select one set of technologies over the other. Results from structural equations modeling indicate that organizational (top management support, IT staff size, and IT budget), environmental (external support for IT adoption), and technological (perceived benefits of IT and perceived barriers to adopting IT) factors significantly influence the adoption of Web 2.0 by small businesses. All of the above factors except IT budget and perceived benefits fuel the adoption of Web 1.0 technologies. Despite perceiving barriers to adopting IT, small businesses owners, CIOs, and CEOs invest in mobile technologies. Top–management support and external support for IT adoption are the only factors positively affecting the adoption of all of the three sets of technologies considered in this study. We offer strategic suggestions to small businesses for selecting Internet and mobile technology–based solutions and services.
Findings and discussion
Conclusion and strategic suggestions
Businesses employing fewer than 500 employees, also known as small businesses, are the growth engine of the U.S. economy. They (a) pay approximately 44 percent of total U.S. private payroll, (b) employ around half of all private sector employees including 43 percent of high–tech workers (scientists, engineers, computer programmers, and others), (c) produce nearly 13 times more patents per employee than large patenting firms; and, (d) create more than 50 percent of the non–farm private gross domestic products (U.S. Small Business Administration, 2014). Small businesses have generated 65 percent of net new jobs between 1993 and 2013.
The use of information technology (IT) innovations by small businesses is essential for their performance and eventually defining their success (Cosh, et al., 2012). For instance, Internet and mobile technologies play an important role in enabling small businesses to serve as the growth engine of the U.S. economy. Sample benefits of using Internet and mobile technologies are as follows. Small businesses mainly make use of the Internet as a sales channel (To and Ngai, 2006) for advertising information related to their products and services (Fisher, et al., 2007). Internet boosts global competitiveness (Hamill and Gregory, 1997) and achieves significant financial gains for business solutions (Johnston, et al., 2007). E–commerce reduces distribution costs and overall supply chain management expenditure (Santarelli and D’Altri, 2003), and Internet–enabled business models enhance market positions of businesses through improved relationships with customers (Lohrke, et al., 2006). Organizations increasingly make use of social media to address internal and external challenges (Milstein, et al., 2009). Blogging has emerged as a networking and information sharing tool for organizations. Corporations use blogging to increase a sense of group cohesiveness and shared understanding of different organizational roles (Baehr and Alex–Brown, 2010). Social media could also be instrumental in creating branding campaigns, customizing products and services, managing customer relations, and marketing efforts. In case of emergency, Twitter is used effectively by transportation organizations to broadcast valuable real–time information to their customers (Larsson and Ågerfalk, 2013). In recent years, mobile technologies and applications have also enabled businesses to offer location–independent, time–independent, and context–specific information and services to their employees, partners, and customers. Either mobile–centric business models are being invented or existing businesses are being transformed into mobile enterprises to take advantage of “mobility” (Unhelkar, 2009). Businesses, irrespective of their sizes, can take advantage of mobility that has inherent features such as ubiquity of information, flexibility of carrying transactions, and ease of accessing customized information and services.
Due to inherent differences in their attributes and, hence, capabilities, Internet and mobile technologies create different levels and types of benefits for different functional areas of small businesses (e.g., accounting, marketing, production, etc.). The same technology would not be equally useful for all functional areas. For instance, Twitter might be one of the best ways to reach out to customers with real–time updates, but it is certainly not the best way to manage accounting operations. In fact, investment in a technology that does not address specific challenges or build competitive advantage for a business could hurt the bottom line of the business. Considering a wide range of choices available for Internet and mobile technology solutions and services in the market, it becomes necessary to study the factors that shape the decision of small business owners, CEOs, and CIOs to adopt a specific set of innovative technologies.
This study compares the adoption of Web 1.0, Web 2.0, and mobile technologies by small businesses in the Southeastern United States. Our understanding of the terms Web 1.0 and Web 2.0 is based on the business–oriented definitions of the terms introduced by Tim O’Reilly in a brainstorming session between O’Reilly and MediaLive International at a conference in 2005. Since the late 80s, businesses have increasingly relied on Web 1.0 for providing virtual access to information so that customers do not need to travel physically to business locations for carrying out transactions (O’Reilly, 2005). The new electronic channel in the form of Web 1.0 allowed customers to download content and make online payments with the help of the Internet banking feature (Choudhury and Karahanna, 2008). Businesses used Web 1.0 mostly to display information online and allowed customers to access and communicate information to businesses (Cohen, 2008; Johnston, et al., 2007; Yeung, et al., 2003), whereas Web 2.0 is about networked communication among customers, businesses, and other business stakeholders creating rich user experience for all. Thus the Web evolved from a Web of content to a Web of content embedded with user interaction elements in it (Raman, 2009). With the emergence of social media like blogs and wikis in the early 2000s, businesses started harnessing the power of collective intelligence or the wisdom of crowds to benefit online customers and businesses alike (O’Reilly, 2005). Web 2.0 led to creating a number of new business models that reuse and combine different existing applications on the Web or combine data and information from different sources, including customers, to create value for all (Murugesan, 2007). For instance, Web 2.0 applications like Twitter, YouTube, and Facebook enable customers to communicate their preferences to businesses, share their complaints with businesses and other customers, and find solutions to their problems with the help of knowledge shared by other customers; in turn, businesses can derive benefits using trends and patterns detected after processing a large volume and variety of data shared by customers at high speed (Lau, et al., 2012). Mobile technologies advance the networked communication facilitated by Web 2.0 by allowing customers and businesses to communicate in real time irrespective of their location (Unhelkar, 2009). Businesses can design mobile versions of their Web sites to make the sites available on customers’ mobile devices or may design new mobile applications that can be downloaded by customers on their mobile devices to carry out transactions with businesses. Thus mobile technology users can carry out transactions using (a) Web 1.0 and Web 2.0 sites that can be accessed over mobile devices or (b) mobile applications designed exclusively for mobile devices. This characteristic feature of Internet–based mobile communication blurs the boundary between Web 1.0, Web 2.0 and mobile technologies for customers and businesses.
However, for the purpose of this study, based on the business–oriented understanding of Web 1.0, Web 2.0 and mobile technologies, we identified the scope for the three terms. For instance, we considered the following four types of Web sites as part of Web 1.0: sites that only display information, sites that allow users to download forms and instructions, sites that support financial transactions, and sites that support transactions with mobile technologies. Web 2.0 included a cluster of Web sites with user–generated content, sites with social networking features and social media applications, and sites with content management systems. Mobile phones, laptops, iPods, iPads, and PDAs represented the group of mobile technologies for the study. Owners, CEOs, and CIOs of small businesses, who participated in this study, were informed about our business–oriented understanding and interpretation of the three terms; thus study participants knew what we meant by Web 1.0, Web 2.0, and mobile technologies at the beginning of data collection process. When top authorities in any organization decide to adopt a specific mobile technology for business the usage typically involves accessing both Web sites and mobile applications (Barnes and Scornavacca, 2006). Hence, we did not ask the study participants if they meant mobile devices for accessing Web sites or mobile devices for accessing mobile applications when they selected “mobile technologies” as their choice during data collection process.
We begin with a review of the literature on organizational adoption of IT. The methodology is described, followed by a discussion of results. We conclude with strategic suggestions to small businesses for investing smartly in IT.
Traditionally, research on organizational adoption of IT is conducted using one of the two theoretical approaches: goal–oriented rational behavior or external forces. Organizational and technological factors represent the goal–oriented rational behavior of organizations for adopting IT, whereas social conditions, financial constraints, political pressure, and similar other environmental factors indicate external forces that shape organizations’ adoption of IT.
To study the effects of organizational factors on IT adoption, researchers typically test the influence of the following variables: top management support (Cragg and King, 1993; Foong, 1999; Premkumar and Roberts, 1999), tangible economic gains including reductions in inventory and operating costs (Yau, 2002), age of organizations (Coleman, 2005), adopter’s characteristics (Avraam, et al., 2008), innovativeness and IS knowledge (Thong, 1999), size of organizations (Bridge and Peel, 1999; Premkumar and Roberts, 1999), and intensity of strategic planning (Bridge and Peel, 1999). Ease of using IT, perceived usefulness of IT (Iacovou, et al., 1995; Igbaria, et al., 1998; Igbaria, et al., 1997), and perceived barriers to using IT (Khalil and Mady, 2005; Moore and Benbasat, 1991; Yeung, et al., 2003) are the most frequently tested variables to study technological factors.
Separating technology from its context ignores its broader effects, non–technical influences, and the factors motivating people and organizations to adopt the technology (Williams, 1998); hence, environmental factors play a critical role in studying organizational adoption of IT. External pressure (Iacovou, et al., 1995; Premkumar and Roberts, 1999) and relative advantage (Cragg and King, 1993; Premkumar and Roberts, 1999; Thong, 1999) are the most tested variables to study the effects of environmental factors on organizational IT adoption.
Recently, a growing number of studies combine the two approaches — goal–oriented rational behavior and external forces — since they are not mutually exclusive (Cosh, et al., 2012). Khalifa and Davison (2006) argue that with the widespread adoption of specific IT, “the uncertainties associated with its adoption are reduced and its potential payoffs become better known.”  At the same time, due to the growing popularity of specific technology in society, customers, suppliers, and competitors increasingly pressurize businesses to adopt the technology. Thus external forces and rational choice factors could play out simultaneously for influencing the businesses to adopt new technology. Hence, there is a need to incorporate both external forces and goal–oriented rational behavior for better explaining and predicting the organizational adoption of IT.
We combine the two approaches by proposing and testing a theoretical model with six independent variables to study the adoption of the Internet and mobile technologies by small businesses (see Figure 1). Our proposed model consists of three organizational factors (e.g., top management support, IT staff size, and IT budget) and two technological factors (e.g., perceived benefits of IT and perceived barriers to adopting IT), which represent goal–oriented rational behavior approach and external support for IT adoption as an environmental factor indicating the external forces approach.
Figure 1: Proposed research model.
A. Organizational factors
Prior research notes the importance of top management support for the successful adoption of technological innovations. Almost all critical decisions regarding the adoption, appropriation, institutionalization, or discontinuation of IT are made by CEOs, CIOs, and other top managers in any organization (DeLone, 1988). Top management support and enthusiasm is a key motivator of IT adoption and use (Thong, 1999). Irrespective of IT, the commitment to financial and all other types of resources made by the decision–makers in organizations is essential for IT adoption by organizations (Teo, et al., 2008). Much of the prior research, however, has targeted larger organizations (Purvis, et al., 2001; Rai and Bajwa, 1997). We call for testing the extent to which top management support could play a critical role in the adoption of IT by small businesses. Small businesses are more likely to adopt IT when owners, CEOs, CIOs, and other senior executives are more innovative, have a positive attitude towards adoption of IT, and possess greater IT knowledge (Thong and Yap, 1995). Sometimes employees exert pressure on top managers for adopting new technologies like electronic data interchange (EDI), professional social networking sites like LinkedIn, and business mobile applications; however, ultimately it is up to the top managers or owners who steer the direction of IT adoption by small businesses (Khalifa and Davison, 2006). Therefore,
H1: Top management support is positively related to the degree of adoption of IT by small businesses.
Size of organizations in terms of their employees (especially the size of IT unit) and overall revenue influence their ability to adopt new technologies. Larger firms (IT workforce) tend to adopt technology innovations more rapidly than their smaller counterparts (Damanpour, 1996; Shin, et al., 2009). Typically, IT department size represents the technical resources an organization possesses to effectively assimilate an innovation (Damanpour, 1991). A number of staff members directly responsible for maintaining the technology systems equips organizations to experiment with technological innovations (Gallivan, et al., 2005). A study assessing the factors influencing the use of alternative digital publishing channels by Greek newspaper agencies finds that the agencies’ decision to use Internet (e.g., e–mail, Webcasting, RSS, and blogs) and mobile technologies (e.g., PDA and tablet PC) over print medium was influenced by the years of publication, circulation, and type of newspaper (morning, evening, weekly, and financial) (Avraam, et al., 2008). Similarly, Teo, et al. (2008) find that the number of IT staff is one of the key factors responsible for the adoption of e–procurement activities such as e–communication with vendors and suppliers, e–payment, e–tracking, and e–marketing by various functional areas of organizations. From these findings we propose:
H2: IT staff size is positively related to the degree of adoption of IT by small businesses.
Greater financial commitment for IT allows greater extent of e–business use (Iacovou, et al., 1995). The operating budget for information systems and technologies, as percent of total revenue, is significant in shaping the adoption of IT innovations by e–business organizations (Zhu and Kraemer, 2005). Saeed and Abdinnour–Helm (2008) also find that the ratio of IT budget to total budget influences the organizations’ ability to adopt new systems. To reserve sufficient funds for using B2B e–commerce, small manufacturing organizations in Hong Kong rely on various cost saving mechanisms like reducing production time, increasing marketability of products, and reducing administration costs (Yau, 2002). Thus,
H3: IT budget is positively related to the degree of adoption of IT by small businesses.
B. Technological factors
Perceived benefits of IT, a technological factor, influences the adoption of IT innovations by organizations (Shin, et al., 2009). Organizations do not easily invest in IT innovations unless they are fully convinced of a wide range of benefits that could be created by the innovations. It is one of the strongest predictors of using IT by small businesses with low fiscal budgets (Chwelos, et al., 2001; Sharma and Rai, 2003; Sia, et al., 2004; Teo, et al., 2003). Higher levels of perceived direct and indirect benefits of adopting EDI positively affect the likelihood of EDI adoption by small businesses (Chau and Hui, 2001). Research on small and medium–sized enterprises focuses on the following perceived benefits of IT for: creating operational efficiency (Hamill and Gregory, 1997), attracting new customers and expanding outreach (Santarelli and D’Altri, 2003), and quality of service (Saeed and Abdinnour–Helm, 2008). Our next hypothesis is as follows.
H4: Perceived benefits of adopting IT are positively related to their degrees of adoption by small businesses.
Perceived barriers to adopting IT hinder the process of IT procurement in organizations (Gallivan, et al., 2005). For instance, perceived cost of implementation is one of the most common barriers to adopting IT innovations by small businesses (Moore and Benbasat, 1991). Perceived barriers to using e–commerce can be categorized into four types: e–payments and data confidentiality; quality assurance and proprietary requirements; human and capital resources; and, lack of drivers and initiatives, in concert with reluctance to change (Yeung, et al., 2003). It is important to note that all of the barriers to IT adoption may not be necessarily perceived as “real” barriers by organizations (Khalil and Mady, 2005). In any event, perceived barriers may not allow businesses to invest in hardware, software, and human skills required to operate IT. We therefore propose the following hypothesis:
H5: Perceived barriers to adopting IT are negatively related to the degree of adoption of IT by small businesses.
C. Environmental factors
In the organizational context, a wide range of institutional, social, and political factors influence their intention to adopt IT. Past studies note the importance of studying external and internal pressures as environmental factors that influence the implementation of technology in organizations. In general, businesses face three types of pressures: coercive, mimetic, and normative. Coercive relations may present themselves in the form legal mandates, policy regulations, etc., whereas lack of funding, shortage of skilled labor, and fierce competition are typical normative pressures experienced by small businesses. Coercive and normative pressures operate through connectedness relations. In contrast, mimetic pressures act through structural equivalence (Kimberly and Evanisko, 1981; Kwon and Zmud, 1987). Under mimetic pressure organizations attempt to imitate the business process or model of a successful or already established organization. However, copying someone does not necessarily guarantee financial success.
Before the rise of social media, business partners’ influence was one of the most significant factors for the IT adoption by small businesses (Hart and Saunders, 1997). However with the widespread use of social media, organizations’ interactions with external actors like customers, suppliers, government regulators, competitors and media, and internal actors, mainly employees at varying levels of authorities, may equally influence IT adoption decisions by organizations.
Environmental factors may have positive or negative effects on the organizations’ ability to adopt IT. For instance, level of external support positively affects the likelihood of EDI adoption by small businesses (Gallivan, et al., 2005). In a similar case, due to limited support and staff knowledge, for a number of years businesses were not willing to adopt Linux, an open source software version of the Unix operating system. The uptake of Linux skyrocketed only after an increasing number of hardware and software vendors, distributors, and third parties started providing support to Linux users (Kshetri, 2005; Margulius, 2003).
Environmental factors, like customer–created negative messages on social media, may severely disfigure the image and eventual brand of businesses (Baehr and Alex–Brown, 2010). Monitoring customer sentiments on the Internet has become a new imperative for businesses. Companies seek external support in the form of commercial off–the–shelf (COTS) software like sentiment analysis tools or outsource the task to IT consultants who then identify and analyze customer sentiments for the companies. Organizations find external support useful to overcome the challenges caused by other environmental factors. We hypothesize that:
H6: External support for IT adoption is positively related to the degree of adoption of IT by small businesses.
It is important to note that all of the six independent variables except “perceived barriers to adopting IT” positively influence the degree of IT adoption by small businesses.
D. Rationale for not considering other factors
We had a choice of selecting and testing a wide range of organizational, technological and environmental factors to compare the adoption of Internet and mobile technologies by small businesses. However, we considered only three organizational, two technological, and an environmental factor for this study. We focused on the organizational factors that were most applicable to the context of this study. For instance, this study targets small businesses in the southeastern United States. Most of the small businesses in this part of the country are in rural areas (U.S. Small Business Administration, 2014). Small businesses in rural America have limited access to financial resources compared to their urban counterparts (Premkumar and Roberts, 1999) and they often struggle with issues like access to high–speed Internet (e.g., Internet with more than 3MB of speed) and lack of skilled IT staff to design and maintain Web sites, social media, and mobile applications for business purposes (Lohrke, et al., 2006; Small Business Notes, 2014). Hence, we selected “IT budget” and “IT staff size” as two organizational factors to compare the differences in adoption of the Internet and mobile technologies by small businesses. In addition, the existing literature on organizational adoption of IT often finds “top management support” as one of the most influential factors affecting the adoption of IT by large organizations (Gallivan, et al., 2005); hence we wished to test if the factor holds any effect on IT adoption by small businesses. Benefits (positive incentive) and barriers (negative incentive) are the most natural incentives for people to make decisions (Cragg and King, 1993; DeLone, 1988). Hence, we selected “perceived benefits of IT” and “perceived barriers to adopting IT” as two technological factors for this study. Small businesses have less revenue and hence less internal resources compared to large multinational corporations (Coleman, 2005). As a result, small businesses heavily rely on local small business development centers, not–for–profit business consulting organizations like SCORE, and public libraries to seek resources (e.g., different types of information) and services (e.g., counseling, IT consulting, etc.) for managing a wide range of business challenges (Hatten, 2011). Hence, we decided to verify the role of “external support” in helping small businesses adopt IT.
The research study was conducted in partnership with the Tennessee Small Business Development Center (TNSBDC) and Knoxville Chamber of Commerce. Reference Database, LexisNexis’ Corporate Affiliations Database, and personal contacts of researchers served as the sources of contact information for potential respondents. Survey questionnaires were distributed to 6,885 CIOs, CEOs and owners of small businesses via e–mail messages. Only one top authority in each small business was contacted to make sure that all small businesses have equal voice in this study. Reminders were also sent to the potential respondents. Respondents were informed that their responses will be kept anonymous. All the respondents filled in an informed consent form online. We conveyed our business–oriented understanding of the terms Web 1.0, Web 2.0 and mobile technologies to study participants by displaying the scope for the terms at the beginning of the online survey. Since top authorities do not adopt mobile technologies for accessing Web sites alone or mobile applications alone (Barnes and Scornavacca, 2006) we did not expect study participants to distinguish the adoption of mobile technologies for accessing Web 1.0 sites, Web 2.0 sites, or mobile applications. Data collection continued for a year, yielding 352 responses with the response rate of 5.11 percent. Structural equation modeling (SEM) was applied to examine the factors influencing the adoption of Web 1.0, Web 2.0, and mobile technologies by small businesses. All six factors yielded Cronbach’s alpha score of 0.8 and more.
Findings and discussion
A. Descriptive statistics
1. Industry type
Sample of respondents represented a wide range of industries. Services, manufacturing, and retailing emerged as the top three categories of industries (see Table 1). Small businesses in this study earn revenue based on eclectic technical and non–technical services, manufacturing and retailing in the Southeastern United States. Considering the heterogeneous industry types, as part of SEM analysis, we controlled the effect of industry type on the adoption of Web 1.0, Web 2.0, and mobile technologies by respondents.
Table 1: Profile of respondents by industry.
2. IT staff size
Although small businesses with less than 500 employees were contacted for this study, almost 62 percent of respondents represented businesses with less than 10 employees, and 21 percent of respondents had businesses with 11 to 40 employees. Since 83 percent of respondents represented small businesses with less than 40 employees, the findings of the study are more applicable to very small businesses in the southeastern US.
The study asked respondents to distinguish between part–time and full–time IT staff. Twenty–seven percent of businesses did not have any full–time IT staff, whereas 23 percent of businesses had one full–time IT staff. Thirty percent of businesses did not have any part–time IT staff and 39 percent of businesses had only one part–time IT staff. Around 38, 24, 12, 10, and 16 percent of businesses had one, two, three, four, and more than five IT employees respectively. The maximum number of IT employees working for a respondent was 11. The above numbers suggest that a majority of respondents either don’t need, cannot afford, or undermine the importance of recruiting full–time IT staff. Further research is required in this direction.
3. IT budget
In fiscal year 2012, more than 68 percent of respondents had a budget of more than US$100,000. However, around 75 percent of respondents had invested less than US$10,000 in IT solutions and services in the last three consecutive fiscal years. Hardware, software, and IT consultancy account for the top three shares of IT budget in the same order. Respondents placed hardware–related dollars in one–time purchases and ongoing maintenance of phones, cables, DSL, computers, peripheral devices, mobile devices, satellite communication for Internet access, and networking the head office with branches. Connectivity seems to be the major goal of the investment made in hardware. Expenses incurred in the software category consisted of fees for licensing and adding new accounts, and ongoing costs for software applications, databases, special analysis software tools, security, and protection tools. In a national survey of 800 small businesses conducted in 2013, the cost of software upgrades, security issues, and the time it takes to fix problems emerged as the top IT challenges faced by small business respondents. IT consultants were hired to manage core business operations. They helped small businesses in the following functional areas listed in the descending order of investment in IT: accounting, customer relationship management (CRM), HR and payroll, production, marketing, and outreach.
4. Current state of IT
Respondents use the following IT channels for business communication: Internet which includes both Web 1.0 and Web 2.0 (36 percent), mobile technologies (34 percent), secondary storage devices such as CD, flash drive, and hard disk (19 percent), virtual private networks (VPN) (10 percent), and others (1 percent). When inquired about their experience for implementing cutting–edge IT solutions, 67, 46, 34, and 26 percent of respondents had never heard about software–oriented architecture (SOA), Software–as–a–Service (SaaS), open source software (OSS), and VPN respectively. However, around 61, 59, 32, 23, 17 percent of small businesses were implementing mobile applications, Web 2.0, VPN, OSS, and SaaS. Small businesses seem to make maximum investment in developing mobile applications and Web 2.0 to replace or add on to their existing IT solutions and services. The top five features most in need of improvement of current IT solutions and services were: Ease of use for staff, reporting and data analysis, cost effectiveness, accuracy of information communicated over IT, and tracking clients using different products and services. This fact suggests that small businesses need to include user friendliness of technology as one of the criteria for selecting IT in the future.
B. Analyzing adoption of Internet & mobile technologies using SEM results
The values for fit indices — CFI, NFI, RMSEA, and relative chi square — indicated that the proposed model for each cluster of technologies (i.e., Web 1.0, Web 2.0, and mobile technologies) was a good fit. The variance explained by the proposed model for IT adoption is the highest for mobile technologies (47 percent) when compared to Web 1.0 (27 percent) and Web 2.0 (35 percent) adoption, which suggests that the proposed model is most applicable for explaining the adoption of mobile technologies by small businesses.
1. Web 1.0 adoption
Ninety–five percent of businesses had at least one Web site that (a) displays information alone; (b) allows users to download forms and instructions; (c) carries out financial transactions; or, (d) facilitates communication with mobile technologies. This fact illustrates the widespread adoption of Web 1.0 by small businesses. A majority of CEOs, CIOs, and owners of small businesses reported Web 1.0 to be the most appropriate technology for: (a) increasing operational efficiency; (b) improving financial reporting and operational information sharing; and, (c) earning maximum return on investment (see Table 2).
Table 2: Most appropriate IT to ...
SEM results presented in Table 3 indicate that the Web 1.0 benefits perceived by CIOs, CEOs, and owners and IT budget do not influence the adoption of Web 1.0 by small businesses. These facts lead us to infer that the above mentioned benefits do not drive the adoption of Web 1.0 by small businesses. Instead, IT staff size (most significant factor), support from top management, and external support for IT positively influence the adoption of Web 1.0 by small businesses.
Table 3: SEM analysis of factors influencing the adoption of Web 1.0, Web 2.0, and mobile technologies by small businesses.
IT budget, an insignificant factor for Web 1.0 adoption, and IT staff size, the most significant factor for Web 1.0 adoption, suggest that there exists an abundant low–cost IT workforce skilled in designing and maintaining Web 1.0 sites. Moreover, Web 1.0 is also reported as the easiest to use technology by respondents (see Table 4). As a result, only 26 percent of respondents perceive barriers for adopting Web 1.0, which is the lowest number of small businesses reporting barriers for adopting IT. In support, perceived barriers to adopting IT emerged as the least significant factor affecting the adoption of Web 1.0 by small businesses. These findings suggest that Web 1.0 would be the first technology choice for low–budget small businesses struggling to stay in business. Small businesses use Web 1.0 mainly for communicating information related to supply chain, production, and human resources.
Table 4: Miscellaneous comparison.
2. Web 2.0 adoption
In case of Web 2.0 adoption, nearly 92 percent of small businesses had Web sites that accept user–generated content, provide social networking features and social media applications, or support content management systems. IT budget and barriers perceived by CIOs, CEOs, and owners for the adoption are the most significant factors influencing the adoption (see Table 3 above). There are many social networking sites, social media applications, and content management systems available for free in the market. Moreover, there exists abundant free and paid external support (e.g., online forums, community discussions/guides, IT consultants, etc.) for implementing Web 2.0, which is far more than the level of technical support available for Web 1.0 and mobile technologies (see Table 4 above). However, the technical support does not seem to be helpful enough for implementing Web 2.0 since small businesses face the highest number of barriers to designing, developing, and maintaining Web 2.0 sites.
Benefits perceived by CIOs, CEOs, and owners also play a key role in the adoption of Web 2.0 sites. A majority of respondents perceive Web 2.0 as the most appropriate technology for: (a) competing with other companies; (b) attracting new customers; (c) retaining existing customers; (d) expanding company’s geographical outreach; (e) improving company’s marketing of products and services; and, (f) most visible effects on business (see Table 2 above). Thus Web 2.0 is a popular choice for marketing and customer relationship management. In support, 59 percent of respondents reported an ongoing implementation for Web 2.0. marketing, CRM, and social information are the top three categories of information communicated over Web 2.0.
Other factors responsible for the adoption of Web 2.0 were top management support and IT staff size. The latter was found negatively associated with the degree of adoption, which suggests that small businesses do not hire new IT staff members exclusively for adopting Web 2.0. Small businesses may be adopting Web 2.0 either with the help of existing IT staff or by retaining only those IT staff members who are skilled in designing and developing Web 2.0 sites and downsizing the remaining IT staff. Despite facing technical barriers, 92 percent of small businesses had at least one Web 2.0 site and 59 percent of respondents reported ongoing implementation of Web 2.0 sites; these facts emphasize the utility (i.e., perceived and real benefits) of Web 2.0 for small businesses. Web 2.0 has an inherent strength of creating business value from user–generated content. Increasingly popular social media sites (e.g., Facebook, Twitter, Foursquare, etc.) available for free facilitate the deployment of Web 2.0 by small businesses. The growing utility of user–generated content for businesses imply the rising demand for (a) content management systems that are capable of managing big data contributed by customers and partners and (b) business intelligence solutions for processing the data to create value for small businesses. For instance, small businesses can create consumer awareness for brands and products and engage with their customers to improve their experience by leveraging a range of sentiment analysis tools like Sentiment140, SAS, and Lithium.
3. Adoption of mobile technologies
Mobile phones, smartphones, iPads, and PDAs were deployed in decreasing magnitude by small businesses. IT budget emerged as the most influential factor for the adoption of mobile technologies. The positive association between IT budget and adoption of mobile technologies implies that it must be challenging for small businesses with low IT budget to embrace mobile technologies. Seventy–four percent of small businesses could not afford to have any kind of technical support for implementing mobile technologies (see Table 4 above). But SEM results show that external technical support positively influences the adoption of mobile technologies. These facts suggest that small businesses which can afford to pay for expensive IT consultation benefit from the external support for deploying mobile technologies.
SEM results also show that barriers perceived by CEOs, CIOs, and owners do not keep small businesses from adopting mobile technologies. For instance, the study found that although 48 percent of respondents reported cost as a key barrier for using mobile technologies (see Table 4 above), 61 percent of respondents reported ongoing implementation of mobile technologies. Benefits for integrating mobile technologies into the existing business process outweigh financial barriers to implementation, which is also supported by the positive association between perceived benefits of mobile technologies and the degree of adopting mobile technologies. Increasing operational efficiency was the most desired benefit for using mobile technologies.
The negative association between IT staff size and mobile adoption suggests unaffordable costs associated with recruiting IT staff specialized in integrating mobile technologies with small businesses. The ongoing implementation of mobile technologies by a majority (61 percent) of small businesses that often face financial challenges suggests that the businesses rely on IT workforce recruited for using Web 1.0 and Web 2.0 to implement mobile technologies.
The support by top management emerged as another significant factor responsible for the adoption of mobile technologies. Usually, top management steers IT policies for businesses; in spite of the positive association between top management support and adoption of mobile technologies, only 13 percent of small businesses had clearly defined IT policies for using mobile technologies. IT policies are critical for making a variety of IT–related business decisions. In the absence of IT policies for mobile technologies, it would be challenging for stakeholders (e.g., top management and customers) to resolve issues arising from the communication of business information over mobile technologies.
C. External support for Internet and mobile technology adoption
External support for IT adoption emerged as one of the two factors positively affecting the adoption of all of the technologies, i.e., Web 1.0, Web 2.0 and mobile technologies. Typically, U.S. small businesses may receive external support in the form of federal funding, bank loans, tax break, COTS software, OSS, or IT consultants for adopting innovative technological solutions and services. Due to the scarcity of research on the role of IT consultants in shaping IT adoption by U.S. small businesses, this study explored the relationship between IT consultants and small businesses.
When inquired about prior experience with IT consultants, several positive and negative stories were shared by CIOs, CEOs and owners of small businesses. For instance, a survey respondent from a local newspaper established in 1902 reported: “We have an excellent consultant for software/hardware and education of newspaper production.” The respondent was interested in recruiting IT consultants. He added: “If you have someone who can assist with networking and archiving ... we would like to have IT consultants.” In contrast, the owner of a young law firm said: “The IT consultants we used were qualified, but not as responsive to our needs as we would have liked. They also did not communicate well with us or within their organization. Their billing practices needed a lot of work — not timely and not sufficiently descriptive. In short — new useful services need to adopt some of the old useful customer service practices.”
Irrespective of the prior experience with IT consultants 74 percent of CIOs, CEOs and owners expressed interest in recruiting external IT consultants in the future. An executive of a small business established in 2007, which offers environmental remediation, UXO clearance, and diving services, said: “We haven’t really pursued consultants except for designing our Web site and solving our laptop problems and settings.” Due to a low staff size of four with no IT staff onboard, the scope of IT consultation for the business was limited to Web 1.0 and related hardware. However, due to the increasing popularity of Web 2.0, the respondent was interested in expanding the scope of IT consultancy. In a similar instance, an executive from a construction business with an annual revenue of over a quarter million dollars plans to hire IT consultants to devise an efficient business process: “We use in–house staff to troubleshoot IT issues. We are a small office and haven’t had trouble we couldn’t solve ourselves. We would use an IT consultant to review our current system to find efficiencies.” The interest for hiring IT consultants was spread evenly among small businesses irrespective of their revenue. A respondent from a non–profit organization working in the education sector said: “We are a nonprofit charitable organization. We would accept your donated services in consultation with gratitude.” Some small businesses revealed their plans to seek IT consultation in the near future. For instance, an industrial chemical manufacturing unit owner responded: “Is there a cost for this analysis? Does the university expect to be involved with their students for the analysis and implementation of ideas? If so, we have a similar service available to us with our local universities and will put priority on working with them. We do have it in our plans to work with MSU’s graduate department for a very similar analysis.”
Cost associated with hiring IT consultants and financial affordability for relying on external IT advisors were two primary concerns expressed by CIOs, CEOs, and owners of small businesses, who expressed interest in seeking external support from IT consultants in the future. An executive of an insurance agency emphasized a key barrier for accessing external consultants: “Consultants are VERY expensive and hard for a small business to afford.” A retail wine and liquor store owner said: “Not at this time ... (we) could not afford it (IT consultancy) at all.” A strong denial to IT consultation was also recorded by a music sales and instruction executive: “HELL no! It is not affordable ... Never pursued the use of a third party IT consultant.”
Lack of need was the main reason given by small business executives who were not interested in accessing IT consultancy services. For instance, a CEO of a small business established in 2009 selling medical devices said: “We are a small business in the New Business Incubator on the Agriculture Campus. While our current needs are small, we expect to require new functionality related to manufacturing, logistics, distribution, etc. in 2012. But we don’t need IT consultants or services ... .” In a similar instance, a respondent said: “We are a small but very old technical sales agency dealing in energy control products. Our company is pretty small, so we don’t really have much in the way of IT issues.”
Due to the shortage of appropriate IT consultants in local communities few small businesses could not seek IT services. A respondent from an advertising and media agency complained: “IT consultants for Apple products and Mac servers are very hard in small communities.” Survey findings revealed that only 33 percent, 41 percent, and 26 percent of small businesses had access to technical support for deploying Web 1.0, Web 2.0, and mobile technologies respectively. The above findings suggest a host of consulting opportunities for IT professionals.
One of the key limitations of this study is that it collects different technologies under umbrella terms like Web 2.0 and mobile technologies. For instance, the term Web 2.0 represents a heterogeneous group of technologies with unique characteristic features. Social networking sites have distinct characteristics than recommendation or online filtering sites, and so on. More research is required to analyze the impact of perceived barriers on the adoption of distinct types of Web 2.0 sites. Another important limitation is that the online survey did not differentiate between (a) mobile devices to access Web 1.0 and Web 2.0 sites and (b) mobile devices to access mobile applications. It is impossible to know what respondents really meant when they selected mobile technologies as a choice. For instance, perceived benefits of adopting mobile technologies are positively related to their degrees of adoption by small businesses. It is not clear if owners, CIOs, and CEOs of small businesses meant perceived benefits of accessing Web sites over mobile devices or perceived benefits of accessing mobile applications that exclusively run on mobile devices.
Conclusion and strategic suggestions
This paper presents the first empirical study comparing the adoption of Web 1.0, Web 2.0, and mobile technologies mainly by small businesses with less than 40 employees in the Southeastern United States. This part of the country has one of the lowest survival rates of small businesses. For instance, around 80 percent of small businesses crash and burn within a year after their inception. Findings and strategic suggestions below can be useful in improving the survival rate of small businesses by investing in appropriate IT solutions and services.
Traditional viewpoint suggests that as the business size gets smaller the degree of adopting innovative technologies by small businesses decreases. In contrast, a large number of small businesses with less than 40 employees adopt Web 2.0 and mobile technologies in this study. Respondents cannot afford to recruit IT personnel exclusively for integrating mobile technologies with their current business process instead they rely on IT workforce recruited for implementing Web 1.0 and Web 2.0 technologies. Based on the variance explained by our model, we conclude that the organizational, environmental, and technological factors in our study are least and most effective in predicting the adoption of Web 1.0 and mobile technologies respectively.
This study contributes to the organizational adoption of Internet and mobile technology literature. One of the unique contributions made by this study is to propose IT consultation as an environmental factor and discover its influence on Web 1.0, Web 2.0, and mobile technology adoption by small businesses. Stories and experiences related to IT consulting shared by heads of small businesses suggest a host of consulting opportunities for IT professionals.
Small businesses struggle to incorporate emerging technologies like social media and their interactions with stakeholders like suppliers, financers, customers, etc. (Larsson and Ågerfalk, 2013). One of the key reasons for their struggle or failure to successfully adopt IT is their inability to select the most appropriate set of IT meeting the demands of their businesses. This study shows that Web 1.0, Web 2.0, and mobile technologies have different abilities to help create distinct benefits for specific functional areas of small businesses. Based on our study findings, we provide the following strategic suggestions on how to smartly invest in IT to benefit from it.
1. Need for formulating IT policies: Policies, in general, manifest organizational strategies. IT policies reflect organizations’ intentions to use IT for achieving organizational goals. Although 92 percent of respondents had implemented Web 2.0, only 34 percent of them had well–defined IT policies for Web 2.0; also only 13 percent of respondents had policies for mobile technologies in place. Thus there is a need for businesses to craft policies for using and addressing issues related to Internet (e.g., social media policies) and mobile technologies. Businesses can either integrate mobile technologies into existing business processes for achieving defined goals or design business processes around newly introduced mobile technologies. In both cases, IT policies can be critical in motivating and guiding the implementation of mobile technologies for meeting business goals.
2. Explore emerging IT solutions & online resources: CEOs, CIOs, and owners should learn more about mobile technologies since there are hundreds of ready–to–use mobile applications available for their small businesses. They should take advantage of guidance offered by online resources (e.g., Docstoc, SBA.gov, etc.). They can explore the social Web for marketing and customer relationship management (CRM) (e.g., social CRM products). Small businesses sometimes are unaware of the technologies that could drastically reduce their operational cost and help them to scale rapidly. For instance, cloud computing allows small businesses to get off the ground with little upfront investment and very little maintenance cost while accessing IT used by large businesses. Hence, small businesses should consistently explore emerging technologies and innovative solutions.
3. Build on your existing strengths: Since 95 percent of respondents in the current study had adopted Web 1.0, it is evident that they have access to an IT workforce capable of designing, developing, and maintaining Web 1.0 applications. While developing Web 2.0 and mobile applications, businesses should tap into tangible and intangible knowledge of Web 1.0 workforce, which represents one of the strengths of the businesses. They should also offer incentives to the existing IT workforce for acquiring skills required to develop and manage Web 2.0 and mobile applications.
4. Reduce operational cost by renting IS/IT: For at least 25 percent of businesses, cost was a barrier for implementing Web 1.0, Web 2.0, or mobile technologies. Because of budget constraints and a variety of high operational cost, small businesses should consider investing in cloud computing, which supports Software–as–a–Service, Hardware–as–a–Service, Infrastructure–as–a–Service, Platform–as–a–Service, Communications–as–a–Service, Storage–as–a–Service, and Business Intelligence–as–a–Service to get off the ground with little upfront investment. It is important to note that large vendors like IBM, Oracle, Microsoft, Salesforce.com, and SAP bring huge credibility to XaaS as acceptable and safe.
5. Invest in analytics & cybersecurity: Considering the rising popularity of social media with inherent characteristics of producing user–generated content on the Internet, small businesses need to invest in technologies that are capable of analyzing user–generated content. For instance, business analytics applications can create value for businesses by processing large volumes of a variety of data (e.g., videos, text, images, etc.) contributed by existing and potential customers at a great pace. As a result, Web 2.0 can be used very effectively for addressing issues and can help achieve objectives related to customer relationship management, product development, marketing of products/services, and threat assessment.
Increasingly advanced wired and wireless communications make it challenging for organizations to share and manage data securely over the Internet. Small businesses tend not to invest enough in cyber security. As a result, more than 50 percent of them fall victim to cyber–attacks and lose thousands of dollars and considerable man hours to fix the problem (National Small Business Association, 2013). It is important for small businesses to subscribe to certified authorities like Verisign, GoDaddy, or similar others for seeking secured socket layer or transport layer security certification that assures secured financial transactions with customers online. It is the ethical responsibility of small businesses to protect consumer data from all possible data hazards. Investing in comprehensive security packages offered by Microsoft, Norton, Semantic, or similar others is necessary for the privacy and security of consumer data stored on computers or servers owned by small businesses.
6. Collaborate with academia and small business development centers (SBDC): Small businesses should develop partnerships with local institutes and universities for recruiting undergraduates and graduates specializing in management information systems and IT–related specializations. Students can be engaged through pro bono assignments, internships, coops, and similar win–win opportunities for both parties. A small business can collaborate with other small businesses to share IT staff/IT skills, thereby reducing costs incurred from connectivity and hardware. Small business development centers in every state frequently offer hands–on training workshops on topics such as business planning and development, social media for businesses, marketing strategies, etc. Small businesses should take advantage of such guidance offered by the regional SBDC.
About the authors
Devendra Potnis is an assistant professor in the School of Information Sciences at the University of Tennessee at Knoxville. His research interests include microfinance, IT adoption by small businesses, ICTs for development, and e–government. Potnis received a Ph.D. in information science from the College of Computing and Information, University at Albany, State University of New York. He has received the Bonnie Carroll and Roy Cooper Faculty Enrichment Award at the University of Tennessee at Knoxville. He is on the editorial board of the International Journal of Information and Communication Technologies and Human Development and the International Journal of Technology Diffusion. He has published his interdisciplinary research in top–tier journals and conferences including Communications of the AIS, Government Information Quarterly, IEEE Technology & Society, Electronic Journal of Information Systems in Developing Countries, and Journal of Asia Pacific Business. He is co–chairing a minitrack titled “ICTs for Financial Inclusion” at the 20th Americas Conference on Information Systems. He is a member of AIS, ALISE, ASIST, IFIP WG 9.4, and INFORMS.
E–mail: dpotnis [at] utk [dot] edu
Kanchan Deosthali is an assistant professor of management in the College of Business at the University of Mary Washington. Her research interests are IT adoption by small businesses, employee training and development activities, citizenship behaviors, self–development behaviors, and motivation and performance. She completed her Ph.D. in organizational studies from the School of Business, University at Albany, State University of New York. She has published her research in the Journal of Business and Psychology, International Conference on Electronic Governance, and Southern Management Association Conference. She is a member of Academy of Management. She has taught undergraduate, graduate, and professional studies courses in management, organizational behavior, human resources, and organizational development and change at the University at Albany, State University of New York, University of Tennessee at Knoxville, and University of Mary Washington. She is a member of AOM and SMA.
E–mail: kdeostha [at] umw [dot] edu
The research study was funded by the Dean of College of Communication and Information, University of Tennessee at Knoxville. We are grateful to Mr. Larry Rossini and his staff at Tennessee SBDC for facilitating the process of data collection. Special thanks to Michelle Schabowski for her editorial assistance.
1. Khalifa and Davison, 2006, p. 275.
E. Avraam, A. Pomportsis, and G. Tsourvakas, 2008. “Implementation of alternative publishing channels by Greek newspapers,” First Monday, volume 13, number 4, at http://firstmonday.org/article/view/2072/1954, accessed 22 August 2014.
C. Baehr and K. Alex–Brown, 2010. “Assessing the value of corporate blogs: A social capital perspective,” IEEE Transactions on Professional Communication, volume 53, number 4, pp. 358–369.
doi: http://dx.doi.org/10.1109/TPC.2010.2077491, accessed 22 August 2014.
S. Barnes and E. Scornavacca, 2006. Unwired business: Cases in mobile business. Hershey, Pa.: IRM Press.
J. Bridge and M. Peel, 1999. “Research note: A study of computer usage and strategic planning in the SME sector,” International Small Business Journal, volume 17, number 4, pp. 82–87.
doi: http://dx.doi.org/10.1177/0266242699174005, accessed 22 August 2014.
P. Chau and K. Hui, 2001. “Determinants of small business EDI adoption: An empirical investigation,” Journal of Organizational Computing and Electronic Commerce, volume 11, number 4, pp. 229–252.
doi: http://dx.doi.org/10.1207/S15327744JOCE1104_02, accessed 22 August 2014.
V. Choudhury and E. Karahanna, 2008. “The relative advantage of electronic channels: A multidimensional view,” MIS Quarterly, volume 32, number 1, pp. 179–200.
P. Chwelos, I. Benbasat, and A. Dexter, 2001. “Research report: Empirical test of an EDI adoption model,” Information Systems Research, volume 12, number 3, pp. 304–321, and at http://pubsonline.informs.org/doi/abs/10.1287/isre.12.3.304.9708, accessed 22 August 2014.
D. Cohen, 2008. “Creating scholarly tools and resources for the digital ecosystem: Building connections in the Zotero Project,” First Monday, volume 13, number 8, at http://firstmonday.org/article/viewArticle/2233/2017, accessed 22 August 2014.
S. Coleman, 2005. “Computer use in small U.S. firms: Is there a digital divide?” Journal of Small Business Strategy, volume 15, number 2, pp. 91–103.
A. Cosh, X. Fu, and A. Hughes, 2012. “Organisation structure and innovation performance in different environments,” Small Business Economics, volume 33, number 2, pp. 301–317.
doi: http://dx.doi.org/10.1007/s11187-010-9304-5, accessed 22 August 2014.
P. Cragg and M. King, 1993. “Small firm computing: Motivators and inhibitors,” MIS Quarterly, volume 17, number 1, pp. 47–60.
F. Damanpour, 1996. “Organizational complexity and innovation: Developing and testing multiple contingency models,” Management Science, volume 42, number 5, pp. 693–716.
doi: http://dx.doi.org/10.1287/mnsc.42.5.693, accessed 22 August 2014.
F. Damanpour, 1991. “Organizational innovation: A meta–analysis of effects of determinants and moderators,” Academy of Management Journal, volume 34, number 3, pp. 555–590.
doi: http://dx.doi.org/10.2307/256406, accessed 22 August 2014.
W. DeLone, 1988. “Determinants of success for usage in small firms,” MIS Quarterly, volume 12, number 1, pp. 51–61.
doi: http://dx.doi.org/10.2307/248803, accessed 22 August 2014.
J. Fisher, A Craig, and J. Bentley, 2007. “Moving from a Web presence to e–commerce: The importance of a business–Web strategy for small–business owners,” Electronic Markets, volume 17, number 4, pp. 253–262.
doi: http://dx.doi.org/10.1080/10196780701635864, accessed 22 August 2014.
S. Foong, 1999. “Effect of end–user personal and systems attributes on computer–based information system success in Malaysian SMEs,” Journal of Small Business Management, volume 37, number 3, pp. 81–87.
M. Gallivan, V. Spitler, and M. Koufaris, 2005. “Does information technology training really matter? A social information processing analysis of coworkers’ influence on IT usage in the workplace,” Journal of Management Information Systems, volume 22, number 1, pp. 153–192.
J. Hamill and K. Gregory, 1997. “Internet marketing in the internationalization of UK SMEs,” Journal of Marketing Management, volume 13, numbers 1–3, pp. 9–28.
doi: http://dx.doi.org/10.1080/0267257X.1997.9964456, accessed 22 August 2014.
P. Hart and C. Saunders, 1997. “Power and trust: Critical factors in the adoption and use of electronic data interchange,” Organization Science, volume 8, number 1, pp. 23–42.
doi: http://dx.doi.org/10.1287/orsc.8.1.23, accessed 22 August 2014.
T. Hatten, 2011. Small business management: Entrepreneurship and beyond. Fifth edition. Mason, Oh.: South–Western Cengage Learning.
C. Iacovou, I. Benbasat, and A. Dexter, 1995. “Electronic data interchange and small organizations: Adoption and impact of technology,” MIS Quarterly, volume 19, number 4, pp. 465–485.
doi: http://dx.doi.org/10.2307/249629, accessed 22 August 2014.
M. Igbaria, N. Zinatelli, and A. Cavaye, 1998. “Analysis of information technology success in small firms in New Zealand,” International Journal of Information Management, volume 18, number 2, pp. 103–119.
doi: http://dx.doi.org/10.1016/S0268-4012(97)00053-4, accessed 22 August 2014.
M. Igbaria, N. Zinatelli, P. Cragg, and A. Cavaye, 1997. “Personal computing acceptance factors in small firms: A structural equation model,” MIS Quarterly, volume 21, number 3, pp. 279–305.
doi: http://dx.doi.org/10.2307/249498, accessed 22 August 2014.
D. Johnston, M. Wade, and R. McClean, 2007. “Does e–business matter to SMEs? A comparison of the financial impacts of Internet business solutions on European and North American SMEs,” Journal of Small Business Management, volume 45, number 3, pp. 354–361.
doi: http://dx.doi.org/10.1111/j.1540-627X.2007.00217.x, accessed 22 August 2014.
J. Kimberly and M. Evanisko, 1981. “Organizational innovation: The influence of individual and contextual factors on hospital adoption of technological and administrative innovations,” Academy of Management Journal, volume 24, number 4, pp. 689–713.
doi: http://dx.doi.org/10.2307/256170, accessed 22 August 2014.
M. Khalifa and R. Davison, 2006. “SME adoption of IT: The case of electronic trading systems,” IEEE Transactions on Engineering Management, volume 53, number 2, pp. 275–284.
doi: http://dx.doi.org/10.1109/TEM.2006.872251, accessed 22 August 2014.
O. Khalil and T. Mady, 2005. “IT adoption and industry type: Some evidence from Kuwaiti manufacturing companies,” International Journal of Enterprise Information Systems, volume 1, number 4, pp. 39–55.
doi: http://dx.doi.org/10.4018/jeis.2005100103, accessed 22 August 2014.
N. Kshetri, 2005. “Diffusion pattern of Linux: An assessment on major technology dimensions,” First Monday, volume 10, number 8, at http://firstmonday.org/article/view/1263/1183, accessed 22 August 2014.
doi: http://dx.doi.org/10.5210/fm.v10i8.1263, accessed 22 August 2014.
T. Kwon and R. Zmud, 1987. “Unifying the fragmented models of information systems implementation,” In: R. Boland and R. Hirschheim (editors). Critical issues in information systems research. New York: Wiley, pp. 227–251.
A. Larsson and P. Ågerfalk, 2013. “Snowing, freezing ... tweeting? Organizational Twitter use during crisis,” First Monday, volume 18, number 6, at http://firstmonday.org/article/view/4294/3688, accessed 22 August 2014.
doi: http://dx.doi.org/10.5210/fm.v18i6.4294, accessed 22 August 2014.
R. Lau, S. Liao, K. Wong, and D. Chiu, 2012. “Web 2.0 environmental scanning and adaptive decision support for business mergers and acquisitions,” MIS Quarterly, volume 36, number 4, pp. 1,239–1,268.
F. Lohrke, G. Franklin, and C. Frownfelter–Lohrke, 2006. “The Internet as an information conduit: A transaction cost analysis model of U.S. SME Internet use,” International Small Business Journal, volume 24, number 2, pp. 159–178.
doi: http://dx.doi.org/10.1177/0266242606061838, accessed 22 August 2014.
D. Margulius, 2003. “The real cost of switching to Linux,” InfoWorld (29 August), at http://www.infoworld.com/t/platforms/real-cost-switching-linux-544, accessed 29 July 2005.
S. Milstein, B. Lorica, R. Magoulas, G. Hochmuth, A. Chowdhury, and T. O’Reilly, 2009. Twitter and the micro–messaging revolution: Communication, connections, and immediacy — 140 characters at a time. Sebastopol, Calif.: O’Reilly Media.
G. Moore and I. Benbasat, 1991. “Development of an instrument to measure the perceptions of adopting an information technology innovation,” Information Systems Research, volume 2, number 3, pp. 192–222.
doi: http://dx.doi.org/10.1287/isre.2.3.192, accessed 22 August 2014.
S. Murugesan, 2007. “Understanding Web 2.0,” IT Professional, volume 9, number 4, pp. 34–41.
doi: http://dx.doi.org/10.1109/MITP.2007.78, accessed 22 August 2014.
National Small Business Association (NSBA), 2013. “Small business technology survey,” at http://www.nsba.biz/wp-content/uploads/2013/09/Technology-Survey-2013.pdf, accessed 2 January 2014.
T. O’Reilly, 2005. “What is Web 2.0: Design patterns and business models for the next generation of software,” at http://oreilly.com/web2/archive/what-is-web-20.html, accessed 31 July 2014.
G. Premkumar and M. Roberts, 1999. “Adoption of new information technologies in rural small businesses,” Omega, volume 27, number 4, pp. 467–484.
doi: http://dx.doi.org/10.1016/S0305-0483(98)00071-1, accessed 22 August 2014.
R. Purvis, V. Sambamurthy, and R. Zmud, 2001. “The assimilation of knowledge platforms in organizations: An empirical investigation,” Organization Science, volume 12, number 2, pp. 117–135.
doi: http://dx.doi.org/10.1287/orsc.22.214.171.12415, accessed 22 August 2014.
A. Rai and D. Bajwa, 1997. “An empirical investigation into factors relating to the adoption of executive information systems: An analysis of EIS for collaboration and decision support,” Decision Sciences, volume 28, number 4, pp. 939–974.
doi: http://dx.doi.org/10.1111/j.1540-5915.1997.tb01337.x, accessed 22 August 2014.
T. Raman, 2009. “Toward 2W, beyond Web 2.0,” Communications of the ACM, volume 52, number 2, pp. 52–59.
doi: http://dx.doi.org/10.1145/1461928.1461945, accessed 22 August 2014.
K. Saeed and S. Abdinnour–Helm, 2008. “Examining the effects of information system characteristics and perceived usefulness on post adoption usage of information systems,” Information & Management, volume 45, number 6, pp. 376–386.
doi: http://dx.doi.org/10.1016/j.im.2008.06.002, accessed 22 August 2014.
E. Santarelli and S. D’Altri, 2003. “The diffusion of e–commerce among SMEs: Theoretical implications and empirical evidence,” Small Business Economics, volume 21, number 3, pp. 273–283.
doi: http://dx.doi.org/10.1023/A:1025757601345, accessed 22 August 2014.
Y. Shin, S. Lee, B. Shin, and H. Lee, 2009. “Examining influencing factors of post–adoption usage of mobile Internet: Focus on the user perception of supplier–side attributes,” Information Systems Frontiers, volume 12, number 5, pp. 595–606.
doi: http://dx.doi.org/10.1007/s10796-009-9184-x, accessed 22 August 2014.
C. Sia, H. Teo, B. Tan, and K. Wei, 2004. “Effects of environmental uncertainty on organizational intention to adopt distributed work arrangements,” IEEE Transactions on Engineering Management, volume 51, number 3, pp. 253–267.
doi: http://dx.doi.org/10.1109/TEM.2004.830859, accessed 22 August 2014.
S. Sharma and A. Rai, 2003. “An assessment of the relationship between ISD leadership characteristics and IS innovation adoption in organizations,” Information & Management, volume 40, number 5, pp. 391–401.
doi: http://dx.doi.org/10.1016/S0378-7206(02)00049-6, accessed 22 August 2014.
Small Business Notes, 2014. “Operating a rural small business,” at http://www.smallbusinessnotes.com/small-business-resources/operating-a-rural-small-business.html, accessed 22 August 2014.
H. Teo, K. Wei, and I. Benbasat, 2003. “Predicting intention to adopt interorganizational linkages: An institutional perspective,” MIS Quarterly, volume 27, number 1, pp. 19–49.
T. Teo, S. Lin, and K. Lai, 2008. “Adopters and non–adopters of e–procurement in Singapore: An empirical study,” Omega, volume 37, number 5, pp. 972–987.
doi: http://dx.doi.org/10.1016/j.omega.2008.11.001, accessed 22 August 2014.
J. Thong, 1999. “An integrated model of information systems adoption in small businesses,” Journal of Management Information Systems, volume 15, number 4, pp. 187–214.
J. Thong and C. Yap, 1995. “CEO characteristics, organizational characteristics and information technology adoption in small businesses,” Omega, volume 23, number 4, pp. 429–442.
doi: http://dx.doi.org/10.1016/0305-0483(95)00017-I, accessed 22 August 2014.
M. To and E. Ngai, 2006. “Predicting the organizational adoption of B2C e–commerce: An empirical study,” Industrial Management & Data Systems, volume 106, number 8, pp. 1,133–1,147.
B. Unhelkar, 2009. Mobile enterprise transition and management. Boca Raton, Fla.: CRC Press.
U.S. Small Business Administration, 2014. “Advocacy: the voice of small business in government,” Small Business Advocate, volume 33, number 3, pp. 3–6, at http://www.sba.gov/content/small-business-advocate-april-2014, accessed 22 August 2014.
E. Williams, 1998. “Predicting e–mail effects in organisations,” First Monday, volume 3, number 9, at http://firstmonday.org/article/view/617/538, accessed 22 August 2014.
O. Yau, 2002. “An empirical investigation of the impact of business–to–business electronic commerce adoption on the business operations of Hong Kong manufacturers,” First Monday, volume 7, number 9, at http://firstmonday.org/article/view/985/906, accessed 22 August 2014.
J. Yeung, J. Shim, and A. Lai, 2003. “Current progress of e–commerce adoption: Small and medium enterprises in Hong Kong,” Communications of the ACM, volume 46, number 9, pp. 226–232.
doi: http://dx.doi.org/10.1145/903893.903941, accessed 22 August 2014.
K. Zhu and K. Kraemer, 205. “Post–adoption variations in usage and value of e–business by organizations: Cross–country evidence from the retail industry,” Information Systems Research, volume 16, number 1, pp. 61–84.
Received 12 June 2014; revised 4 August 2014; accepted 12 August 2014.
“Factors influencing adoption of Web 1.0, Web 2.0, and mobile technologies by the growth engine of the U.S. economy” by Devendra Potnis and Kanchan Deosthali is licensed under a Creative Commons Attribution–NonCommercial 4.0 International License.
Factors influencing adoption of Web 1.0, Web 2.0, and mobile technologies by the growth engine of the U.S. economy
by Devendra Potnis and Kanchan Deosthali.
First Monday, Volume 19, Number 9 - 1 September 2014